Proceedings of the 28th International Symposium on Advancement of Construction Management and Real Estate

Musculoskeletal disorders are common diseases with high incidence which are great impact on health in construction. They are often caused by excessive twisting or heavy load in bad posture, so it is very important to detect and identify these postures efficiently. Researchers often use inertial measurement units (IMU) to record the acceleration, Euler angle and other features from the IMU placed at some nodes to collect information of postures. However, lots of nodes and feature dimensions make the data memory very large, which will lead to the decrease of computing speed and the increase of storage cost. This study proposes a feature processing method to simplify the amount of data based on IMU, and use long short term memory (LSTM) neural network for recognition verification. The method combines the quaternions of the plane normal vectors with the coordinates of an original node. Using this method, we have achieved recognition accuracy similar to the original data while reducing the number of features by nearly half. The research results are beneficial to optimize the automatic recognition process of construction posture, which have a wide application prospect.

Effective public participation mechanism is a crucial process that aims to maximize positive inputs and mitigate the potential negative impacts on local communities resulting from the development of megaprojects, particularly in the context of displacement and resettlement. However, the currently perceived shortfall in benefits from megaprojects, coupled with the lack of fulfilment of participation, has resulted in delays and cost overruns of the project and significant damage to the organization's reputation. Current public participation mechanisms are reactive rather than proactive, mainly offering an instrumental perspective to make the participants comply with project process needs. As such, it is necessary to adopt a more comprehensive approach to public participation mechanism, which includes the affected community's perception that could be harmed by the displacement and resettlement strategies associated with megaprojects. This broader inclusiveness is critical to improving the effectiveness of the public participation mechanism. This study explores affected community's participation in depth. A qualitative methodology was introduced to allow identification of participation determinants to avoid ambiguity and disparity in understanding the adoption progress and status of community participation. The research affirms that an exploratory case study on recent Australian megaprojects will provide enhancements to the final community participation framework.

As global warming intensifies, excessive energy consumption has emerged as a prominent global concern, particularly within the building sector. However, the existing policies, technologies, and strategies used to evaluate building energy systems have fallen short of achieving the desired energy conservation targets. This can be attributed to the complexity of the energy system, which encompasses various influential factors, particularly on a national scale. To address this gap, the objective of this study is to elucidate the intricate internal interactions and feedback mechanisms within a national-level energy system. With a focus on the building sector in China, an extended projected-level life cycle energy system was established based on the LEAP model. This system is integrated with four other macro systems pertaining to energy, population, economy, and environment, in accordance with the IPAT and KI theory, thus forming a comprehensive national-level energy system. System dynamics (SD) methodology is employed to unveil the detailed top-down relationships within this system. Results shows (1) strong interdependencies exist among the five coupled energy-related subsystems, namely population, economy, buildings, energy, and environment; (2) an integrated dynamic energy assessment (IDEA) model has been developed, which allows for the quantification of national-level energy consumption; (3) mobile energy types significantly influence the life cycle energy of buildings. While the proposed comprehensive model requires further validation through empirical studies, this research not only advances the theory and model development for national-level building energy assessment, but also provides valuable insights for the Chinese government in formulating effective energy conservation strategies and policies.

The economic and environmental performance of buildings largely depend on the design stage. Design choices with a unilateral focus on the reduction in environmental impacts for developing low-carbon and energy efficient building practice may increase the cost of the building, as there is a trade-off problem between economic and environmental performance of a building. To address this issue, this research proposed a framework for making appropriate design decisions by solving the trade-off problem between economic and environmental performance of buildings in their early design stage. This framework combined a BIM and LCA integration approach with a machine learning-based optimization method. The developed framework was validated by a mid-rise residential building in China, which showed the potential to simultaneously reduce the cost and carbon emissions of the building by identifying the optimal design solutions that balance the trade-offs between economic and environmental impacts. The results revealed that for a mid-rise residential building in hot summer and cold winter climate zone in China, small increases in economic cost (102.57 Yuan/m2) could result in a reduction in carbon emissions by 2387.02 kg CO2eq/m2 in early building design practice. The framework proposed in this research provides insights in the optimization of economic and environmental performance of a building.

Corporate misconduct in the construction industry may lead to severe economic loss and even fatal injuries to workers and residents. An effective way to detect corporate misconduct timely is needed. This paper provides a tool to predict corporate misconduct by analyzing corporate data from 119 listed construction companies in China. XGBoost is used to construct a prediction model for corporate misconduct, and a support vector machine model is used as a benchmark to evaluate the performance of the built XGBoost model. The accuracy of the built XGBoost model in predicting corporate misconduct is 80.38%, outperforming the support vector machine model. The results may facilitate companies’ stakeholders to predict and identify corporate misconduct timely and accurately, and thus corporate scandals may be nipped in the bud.

China's economy has transitioned from a stage of rapid expansion to a phase of high-quality development. After the Chengdu Chongqing Economic Circle was put forward, the coordinated development of the region has garnered significant attention. In order to enrich the theoretical research of coordinated development and offer practical guidance for achieving high-quality development in the west, we carried out this research. Based on panel data of 16 cities in the Chengdu-Chongqing Economic Circle (CCEC), this study employed Entropy Weight-TOPSIS model and Coupling Coordination Degree (CCD) model to quantify the coordinated development level of CCEC from four dimensions of environment, economy, energy and technology. In additon, Arcgis 10.8 was used to visualize the calculated data, we select the coupling coordination level calculated in 2012, 2015, 2018, 2020 to conduct a spatio-temporal analysis. The results indicated that the coupling coordination degree of CCEC has shown an overall upward trend from 2012 to 2020. Furthermore, we find that in 2018, a radiation situation based on the Chengdu-Chongqing Ring Expressway was formed, and then in 2020, Suining and Neijiang located on the central axis of Chengdu-Chongqing interaction, serving as connecting ports for Chengdu, Chongqing, and other cities, they took the lead in reaching a quality coupling coordination level. These facts indicate that in order to integrally improve and stabilize the CCD of Energy-Economy-Environment-Technology (EEET) tetra-element system in the CCEC, it is necessary to strengthen the interaction level of regional cities and formulate differentiated optimization strategies to make up for their own coordination weaknesses.

Mega-events are an opportunity for a country to demonstrate its economic and social strength and enhance its international status. The success of mega-events has a profound impact on the economic and social development of a country. Currently, mega-event projects face noticeable challenges in terms of cost overruns and schedule delays, which indicates that the project management of such mega-event remains under-researched. Existing studies reveal that the lack of a sufficient study of the drivers of mega-event projects has become one of the most critical factors hindering project success. To address the issue, this paper selected the six Olympic Games projects as the objects of the case study, adopting the grounded theory to develop a theoretical model of the driving factors of project success. The study obtained six crucial driving factors, i.e., ‘government support', ‘atmosphere relationship' and ‘consciousness culture' are the external driving factors of the Olympic Games project success, while ‘leadership', ‘resource allocation' and ‘organizational management' are the internal driving factors. The research findings can provide new insights and approaches to enhance the effectiveness of project management for such mega-events during the life cycle process.

The e-industrial park (EIP) has been designed and constructed to promote the economic developments, innovation and sustainability. Alibaba Xixi EIP is a typical newly built EIP with ecological engineered landscaping (EEL) in Hangzhou City. In this study, several space syntax models, i.e., angular segment analysis (ASA), visibility graph analysis (VGA) and agent-based analysis (ABA) models, are applied to make quantitative estimates of the efficiency of spatial design techniques in the spaces of the EIP. The ASA model is adopted to estimate and explain the ‘centrality of accessibility’ and ‘centrality of choice’ in the EIP. The high-level ‘intelligibility’ is found between the local and global spaces. Differences in spatial configuration between the eye level and the knee level are revealed by the VGA model for both stuffs and visitors of the EIP, while specific spatial characteristics of spaces are found to correspond to those of the ASA model. Furthermore, the ABA model is adapted to explain the dynamic impacts of entrances in different status on pedestrian’s circulation, especially for areas around the constructed wetlands. This research illustrates the relationship between local and global spaces of the EIP through quantitative estimation, which provides valuable guidance for the design of sustainable development of EIPs.

At present, climate change characterized by global warming has become a crisis and challenge faced by the international community. Based on the reflection on the traditional real estate development model, energy-saving, environmental protection and green low-carbon real estate has attracted increasing attention. Based on the DPSIR model framework, this study constructs the evaluation index system of low-carbon real estate development, and uses the entropy right method to quantitatively evaluate the development level of low-carbon real estate in Hangzhou. The results show that in the process of low-carbon real estate development level in Hangzhou, the pressure and status showed a steady increase, respectively from 0.0515 and 0.0216 in 2017 to 0.1 852 and 0.2158 in 2021. The two subsystems of driving force and response decreased slightly in individual years, showing an overall increasing trend. The influence subsystem declined for two consecutive years in 2018 and 2019, and the downward trend in 2020 was contained and began to grow. The system comprehensive evaluation index increased from 0.2568 in 2017 to 0.9455 in 2021. The research shows that the development level of low-carbon real estate in Hangzhou has been rising continuously in recent five years and the development level is excellent in 2020 and 2021. From the rising trend of the system evaluation index, the low-carbon real estate will continue to develop in Hangzhou in the future and has a huge space for growth.

This study takes the development stage of China's REITs market as the background, and makes a case study by setting 5 primary and 6 secondary underlying asset evaluation indicators. In this study, a multi-angle radar model is constructed to observe the concerns and considerations of original equity holders in the selection of the underlying assets of infrastructure REITs projects, and to analyze their issuance strategies. This article categorizes the 27 Chinese Real Estate Investment Trusts (REITs) issued as of May 1st into four distinct types: Missing angle type I, Missing angle type II, Similar type, and Other type. The asset selection analysis examines the five key factors of asset yield ability, asset risk level, asset size or scale, geographic location of assets, and the original equity holders’ purchase ratio. The article also analyzes the characteristics and issuance strategies of corresponding assets according to different industry categories. The set of asset evaluation system designed in this study can help investors and original equity holders have a more concrete assessment and understanding of asset quality. It facilitates their asset selection process and can also serve as a reference for existing asset models when considering their own investment portfolios.

Due to accelerated population aging, nursing homes have been greatly developed in China. However, most of the indoor spatial layout design of nursing homes are independent respectively and lack of connection, which can not well meet the daily life requirements of older adults. In order to ease the pressure of aging and improve the quality of life of the elderly in nursing homes, this paper aims to analyze the indoor spatial layout of nursing homes for further optimization. The methods of field observation and on-site interview were used to investigate the satisfaction of elderly people with the indoor spatial layout of nursing homes. Data analysis software Nvivo11 was adopted to qualitatively analyze the content of interviews. Older adults’ general movement patterns in the nursing home were obtained using RFID positioning technology. The results revealed that the indoor space function allocation of the nursing home was unreasonable and some space utilization rates were low. The bedroom was the place where older adults spend their most time in the nursing home. The location of elevators in nursing homes was important for spatial cohesion. The diversity of spatial functions, spatial size, and relative distance between spaces would affect older adults’ satisfaction with the spatial layout of nursing homes. This study is expected to provide nursing homes with suggestions for both space design and management service to enhance older adults’ quality of life.

In the past few years, there has been growing recognition and widespread attention towards the severity of global climate change and environmental issues. Low-carbon behavior has become one of the important ways to address these problems. This article utilizes a sample of 585 Chongqing residents collected via a questionnaire survey as the research subject, and employs a binary logistic regression model to analyze the factors that influence low-carbon behavior. Additionally, an interpretive structural modeling (ISM) is constructed to examine the logical relationship between the various factors influencing low-carbon behavior. The research results show that: (1) Based on the results of binary logistic regression, this study reveals that low-carbon efficacy, behavioral willingness, low-carbon cognition, sense of responsibility, social pressure, infrastructure, and policy are all significant factors that impact low-carbon behavior. Among them, low-carbon efficacy, behavioral willingness, low-carbon awareness, and sense of responsibility have a significant positive effect on low-carbon behavior, while social pressure and policy have a significant negative effect on low-carbon behavior. (2) The fundamental factors that influence low-carbon behavior, as per the ISM analysis, are policy and infrastructure. Intermediate factors that exert an impact on low-carbon behavior encompass low-carbon efficacy, sense of responsibility, social pressure, and low-carbon cognition. Finally, behavioral willingness functions as a direct determinant of low-carbon behavior.

The operation and maintenance (O&M) of indoor fire-fighting equipment is the key to ensure indoor fire safety. Among the O&M activities, fire-fighting equipment inspection and counting is a periodic task, and currently such task is usually performed manually, which is time-consuming and labor-intensive. In order to realize the intelligent and efficient management of indoor fire-fighting equipment, this study introduces an integrated detection and counting method of indoor fire-fighting equipment based on computer vision and deep learning, and realizes the statistics of indoor fire-fighting equipment combined with on-site video. The method combines multi-target tracking algorithm Deepsort and YOLOv5 target detector. Firstly, the data set of indoor fire-fighting equipment is established. Then, the YOLOv5 algorithm was used to extract the target boundary box and input it into the Deepsort framework. Kalman filter and Hungarian algorithm were used to predict and track the target trajectory. Finally, the tripwire counting method is used to realize the statistics of fire-fighting equipment. The actual test results show that the mean Average Precision (mAP) of the method is 92.4%, and the detection speed can reach 30 fps, which can basically realize the real-time detection of multi-class and multi-target indoor fire-fighting equipment, improving the performance of the O&M of indoor fire-fighting equipment.

The development of prefabricated buildings is the key to achieving the sustainable transformation of the construction industry. The production process of prefabricated components connecting the front-end design and back-end construction plays an important role in a prefabricated building projects (PBPs). Nowadays, PBPs always adopt the mixed-flow production framework, which indicates that various types of concrete components are produced simultaneously on one production line. However, the mixed-flow production process usually falls in trouble of time delays, labor wastes, and cost overrun, these can be caused by inventory accumulation, process blockage, machine idle, etc. These activities that do not add value are identified as “waste” in lean principles. This study aims to develop a system simulation model to eliminate waste in the mixed-flow production of prefabricated components and optimize the production process within the context of lean methodologies. Take the real prefabricated component factory as the research object, and use real data to conduct research. Firstly, the current production process of components is described from two perspectives of material flow and information flow by using the value stream mapping (VSM) method to identify waste. Then, by designing future flow charts and using lean tools such as total productive maintenance (TPM), system layout planning (SLP), and just-in-time (JIT) inventory management to eliminate or improve waste, and take corresponding measures to ensure the smooth implementation of lean optimization solutions. Finally, based on the Anylogic simulation platform, the conventional and the lean-optimized prefabricated component production process are simulated, and the visualization of the production system is realized. By comparing the two systems, this research found that the lean optimization system proposed in this paper improves the productivity of precast production line. The research output contributes to the lean philosophy exploration in construction and advanced practice in prefabrication projects.

Information technologies such as electronic bidding are constantly trying to strengthen competition in the construction industry. As a result, the competitive climate for contractors is more uncertain and dynamic. The efficacy of market competition policies, however, is likely to differ from expectations because the perceived key points and action paths of contractors on the competitive environment are still unclear. This paper proposes an E-ISCP analysis model based on the classical SCP paradigm, with the goal of analyzing the key environmental perception factors and their role in bidding decisions. After designing a questionnaire and collecting 158 valid responses, we tested each path hypothesis in the E-ISCP model and conducted a difference analysis based on the sample's background characteristics. According to research, the contractor's attention to rivals—which is correlated with the number of competitors involved, the clarity of the competitive situation, and risk appetite—is the key to perception of the competitive environment. Additionally, bid/ no-bid decision are influenced by historical data and bidding requirements through competitive strength, but bidding behavior is directly hampered by contractors’ risk appetite.

Online review platforms are now widely used and have a significant impact on consumer behavior. They enable catering suppliers to reach customers beyond geographical boundaries by providing electronic word-of-mouth (eWOM) recommendations. This study aims to compare the effects of road centrality and eWOM on the popularity of different types of restaurants in Tianjin, China. Data was collected from dianping.com across six districts of the city. The popularity of restaurants was measured by the number of total reviews, reviews with pictures, and good and bad reviews received. The index of eWOM was established using the star rating, per capita consumption, food rating, environment rating, and service rating. Urban network analysis tools were used to calculate road centrality, and multiple linear regression, geographic detectors, and multi-scale geographic weighted regression techniques were employed to explore the factors that influence restaurant network reputation. The study highlights that eWOM has a more significant impact on restaurant popularity on a large scale, leisure food providers are most affected by eWOM, and the popularity of Chinese restaurants is more dependent on road centrality than Western restaurants, especially on a small scale. However, foreign cuisines are more dependent on large-scale road centrality. These findings provide valuable insights and recommendations for restaurant operators and urban planners to leverage road centrality and Internet word-of-mouth to increase the popularity of different types of restaurant networks.

Cognitive processes are the basis for safe or unsafe behaviors of construction workers, and cognitive failure is a significant contributing factor to unsafe behaviors in this field. Exposure to the hypoxic plateau environment may impair the cognitive abilities of construction workers and increase the probability of their cognitive failure, thereby posing safety risks. However, it remains unclear how the hypoxic plateau environment affects the cognitive abilities of construction workers. To address this gap in knowledge, 5 practitioners involved in high-altitude construction were recruited and administered a cognitive ability test battery that assessed four cognitive abilities: sustained attention, selective attention, working memory, and executive ability. Participants completed a low-altitude baseline test and a plateau follow-up test. Our results indicate that altitude exposure has significant and widespread negative effects on all four cognitive abilities. While sustained attention, selective attention, and executive ability recovered rapidly with adaptation to the hypoxic environment, working memory ability remained impaired. Based on these findings, this study recommends ongoing cognitive workload monitoring for lowland construction practitioners entering the plateau for the first time to ensure their occupational safety and health. This study provides a basis and rationale for protecting and improving plateau construction practitioners’ occupational safety and health.

With urban renewal work in full swing, promoting the use of the engineering contracting (EPC) method in projects with more complex renewal content is an important initiative to promote high-quality urban renewal. The differences in natural, economic, political, social and human environments that urban renewal EPC projects face lead to various risks in the process, and scientific assessment of urban renewal EPC project risks is essential. Based on the theory of urban symbiotic system and organic urban renewal, this paper extends the single static risk assessment to construct a two-pronged risk assessment index system and assessment model of “internal vulnerability-external threat” from an ecological perspective. Through Chongqing Yuzhong as the case area, the entropy-weighted fuzzy integrated hierarchical evaluation method is used to analyze the risk factors affecting urban renewal EPC projects. The results show that from the overall risk assessment the overall risk level of urban renewal EPC projects in Chongqing is low, external policy risk and public sentiment risk are the most important risk factors in the process of urban renewal EPC projects, and internal contract risk is also a key concern for urban renewal projects. Finally, we propose corresponding preventive measures and contingency plans for such risks to provide theoretical guidance for urban renewal work.

The layout of temporary facilities on the construction site is an important content of construction organization design and construction site management. Since the 1990s, it has been continuously concerned by many scholars. This article systematically reviews and analyzes relevant literatures from the dimensions of problem classification, assumptions, objective functions, constraints and optimization algorithms of mathematical models for facility layout problem, exploring the current situation, future trends, and research directions in the field of optimizing the layout of temporary facilities on construction sites. Results show that how to make the mathematical model be more consistent with the engineering practice and explore the optimization algorithm with higher accuracy and efficiency are the key points in this field. The future research direction from the aspects of the assumption that the conditions should be more realistic, the objective function tends to be diversified, the improvement of optimization algorithm and paying more attention to prefabricated buildings.

Particulate matter (PM) pollution from the construction industry is a serious problem that threatens the environment and people working in the industry. These PMs are mainly emitted from various construction activities and materials. Although several construction controls have been introduced in different standards, these controls still cannot be targeted at specific sources of PM because there is no comprehensive understanding of the emissions from performing different activities with different materials. This study identified 8 studies that focused on the emission of PM from construction activities or materials, extracted the concentration of PM and compared it with the World Health Organization (WHO) air quality standard. The results showed that cutting, mixing and drilling were the three most studied activities and the different types of concrete were the most studied materials. All reported PM10 and PM2.5 concentrations exceeded the WHO standard, with the maximum concentration being more than 900 times the standard. Even if workers were only allowed to cut, mix or drill for one hour a day, and assumed to be in air completely free of PM10 and PM2.5 for the rest of the day, they would still be exposed to air that did not meet the WHO 24-h standard. In addition, the emission ranking of construction activities could be different for different types of PM. This study will help industry and academia to better understand the different risks of typical construction activities and materials, so that refined controls for PM pollution in different scenarios in the construction industry can be promoted.

Nowadays truss structures are commonly utilized in large-span public buildings. In order to ensure the safety of truss structures, it is necessary to regularly check the geometric quality of the structure. However, traditional truss geometric quality inspection still relies on manual work, which is inefficient and costly. Light detection and ranging (LiDAR), considering its efficiency and reliability, is now widely used for geometric quality inspection of structures. This paper proposes an automated intelligent algorithm for truss geometric quality detection. Firstly, the truss structure is separated from the background in the original point cloud through Building Information Model (BIM). Then, the geometric information of the truss is automatically calculated based on key point detection. Finally, the inspection results are obtained by comparing the calculation results with the design information from the BIM. A deformed truss BIM was converted to point clouds to verify the above method in this paper. The experiment results show that the proposed algorithm is effective in automatically processing truss point clouds for truss geometric quality inspection, which can accurately and quickly identify the locations of anomalies in the truss, improving the performance of truss geometric quality inspection.

Green building is an organic combination of sustainable concepts and the construction industry, and is a leading trend in the development of the construction industry. However, the current delivery method of green building cannot effectively meet the requirements of its highly integrated systems, the use and recycling of non-traditional materials, etc., which reduces the efficiency of the process of green building delivery. Lean construction, as a method to reduce waste and improve productivity, provides a new idea for delivering green buildings, and its theoretical system and its content play a certain contribution in the process of delivering green buildings. Combined with the characteristics of green buildings, the idea of lean construction can be applied to green building delivery to improve the efficiency of green building delivery process. This paper firstly reviews the relevant studies on lean construction and green building through literature, based on which the synergy between lean construction and various stages of green construction is analyzed using the relationship matrix diagram, and further elaborates how lean construction can be implemented in green construction to improve the efficiency of green building delivery process, which provides a reference method for future green building delivery.

Large language models, such as Chat Generative Pre-training Transformer (ChatGPT), have shown great potential to revolutionize the construction industry by automating repetitive and time-consuming tasks. This study presents a framework to explore the feasibility of ChatGPT to automatically analyze causal factors using narrative accident reports in construction. Firstly, the ChatGPT was used to extract primary causal factors from accident reports from the perspectives of human-related, crane-related, environment-related, and management-related. Then, outputs from ChatGPT were evaluated by a group of participants in terms of clarity, specificity, reliability, and inspiration. Finally, the outputs from ChatGPT were further corrected by the same participants through careful addition, deletion, or re-judgment of the factors. The findings of this study include: (1) ChatGPT was generally considered a satisfying assisting tool for causal factor extraction and analysis; (2) the results also implied that this technology still needs further development before it can be widely applied in professional areas, such as construction crane safety. Overall, this study highlights the potential and the required further efforts of applying large language models in research.

With the increasing reliance on digital technologies in the architectural, engineering, construction, and operation (AECO) industries around the world, Building Information Modelling (BIM) is considered to be the information backbone and integration platform for the industry’s digital transformation process. For individuals, projects, and organizations to adopt and utilize BIM in the industries, it is important to identify necessary BIM capabilities that need to be developed, applied, and measured. Currently, an increasing number of studies have focused on BIM capabilities across various individual, project, organizational, and national levels and among different project stakeholders. However, a knowledge gap exists in understanding the state-of-the-art in this field. The main purpose of this paper is to examine research scope, patterns, and development trends of BIM capabilities research and achieve a better understanding of the cutting-edge developments in this area. To do so, a bibliometric analysis and review was conducted to identify influential authors, journals, and keywords in the research area of BIM capabilities. Using the Web of Science database, a total number of 638 published research articles were obtained and selected for bibliometric analysis. The VOSviewer software is used to help map out the keyword co-occurrence network, co-authorship network, etc. New fields in BIM capabilities research are highlighted, such as the increasing use of integrated digital tools, sustainability, performance and risk management. These development trends propel the utilization of BIM in the AECO industries to new heights to achieve more efficient, sustainable, and intelligent construction project management. This study contributes to the existing body of knowledge on BIM capabilities, offering insights into promising directions for future research advancements in this field.

Citizens’ sense of gain in smart cities (CSGSC) has become a vital standard in evaluating the sustainable development of smart cities, which encompasses both the subjective spiritual and objective material gains experienced by citizens. However, existing CSGSC evaluation studies primarily focus on measuring citizens’ subjective feelings using scales, neglecting the potential impact of emotional contagion among individuals. This study addresses this study gap by establishing an agent-based simulation model of CSGSC, incorporating expectation confirmation theory and emotional contagion theory to analyze the effect of emotional contagion on CSGSC. The simulation results demonstrate that emotional contagion diminishes the overall CSGSC of the population, possibly due to the heightened susceptibility of individuals to low-level CSGSC. Furthermore, emotional contagion contributes to the polarization of the population. This study identifies the effect of relationship network density and individual characteristics on the effects of emotional contagion by multi-scenarios simulations. Additionally, the implications of these findings for government and enterprises engaged in smart city construction are discussed. This study offers a novel perspective for CSGSC research and carries significant implications for the evaluation of CSGSC. From the perspective of emotional contagion, this study provides valuable guidance for policymakers and technology research enterprises involved in smart city development.

The use of floor scaffolding is widespread, but it is also prone to accidents. To ensure safe scaffolding construction, there is a need for reasonable construction standards and simulation exercises. With the development of technologies such as digital twins and metaverse, immersive interactive experiences have made it possible to conduct scaffolding construction safety training. This article summarizes common scaffolding safety accidents and designs a game-based interactive virtual platform. The platform uses the Unity3D engine to construct virtual scaffolding materials and visualizes the scaffolding erection, featuring game-like interactive operations where construction personnel complete virtual scaffolding construction in the form of game levels. After applying the platform to actual construction sites, construction personnel have become more familiar with the entire scaffolding construction process through game-based training, which has effectively implemented construction standards and strengthened safety awareness. This method addresses the lack of construction simulation in scaffolding construction and promotes the improvement of safety awareness among construction personnel.

Carbon emissions from the construction phase are highly concentrated and annual emissions are high, but the construction site environment is complex and difficult to calculate accurately. Besides, the accurate calculation of carbon emissions are the data base for exploring how to design and develop construction plans to control carbon emissions. In view of this, aiming at the building construction stage, this paper defined the spatial scope of carbon emissions calculation, took machinery and equipment as the calculation object, reorganized the calculation model from a spatial and temporal perspective, and proposed a method suitable for field measurement. The results of the study showed that the energy consumption data of the tower crane based on the quota data and rated power estimation were 126.64% and 160.54% higher than that measured by electricity meters respectively. The method of measurement using electricity meters is highly accurate and reflects the energy consumption on site. Therefore, when calculating carbon emissions from the construction site, it is necessary to adopt a more targeted calculation method based on construction machinery and equipment. What’s more, the research results provide some reference for the accurate acquisition and update of carbon emissions activity data from construction sites.

In recent years, China has continuously increased investment in the medical care field. The quality of medical care has a crucial effect on the physical health and people’s well-being and the environment of medical buildings is an important factor affecting the level of medical quality. Most of research on improving medical care quality has focused on medical staff’s personal service and hospital management while neglecting the impact of medical building environment on medical quality. There are some research on one-dimension of medical building environment, however systematical research is still fragmented. This study aims to develop a comprehensive measurement scale to evaluate the quality of medical building environment and test the impact of quality of medical building on the level of patients’ satisfaction. Based on literature review, six factors under building environment are identified: spatial perception, soundscape, temperature, humidity, ventilation, and lighting. Chongqing university hospital is selected as a case study. Questionnaire survey is used to collect data on patients’ perception on building environment of university hospital and satisfaction level. 122 valid questionnaires are collected. Based on regress analysis, it is found that patients' satisfaction with ventilation has the greatest impact on overall hospital satisfaction, followed by spatial perception, lighting and soundscape. An impressive finding is that temperature and humidity have an insignificant impact on overall hospital satisfaction. It can be inferred that there are two possible reasons. For one thing, the temperature and humidity range of the hospital stipulated by the national standard is roughly consistent with the comfortable range of people. For another, people in Chongqing area are insensitive to humidity and temperature. Therefore, it is suggested that the construction of the hospital should consider the local climate and the needs of the main population.

The construction industry is a typical labor-intensive sector with a high incidence of production accidents and safety risks. Many studies have found that consistently intense workloads could cause workers to lose sufficient vigilance and attention, reduce their risk detection and perception, and lead to the wrong estimation of potential risks and unsafe behaviors. Therefore, monitoring and evaluating the workload of construction personnel will be conducive to assigning, adjusting and controlling the construction process more effectively and safely. Recently, research in many fields has proposed that the EEG signal of employees at work can be obtained through wearable devices in real-time, enabling the quantitative measurement of their workload level. However, the effectiveness of this signal processing framework is often severely affected by EEG “artifacts”. Moreover, the complex construction tasks pose various requirements on the postures and movements of construction workers, making the traditional workload detection method based on static deployment EEG schemes impossible to implement in construction. To address this challenge, this study proposes a new type of hybrid kinematic-EEG signal and corresponding workload indices. This signal preserves the “artifact” triggered by muscle movement in the original signal as a new data feature, creating a more feasible workload monitoring and evaluation scheme for the construction industry. Previous research on occupational accidents in construction has revealed that a significant number of fatalities and injuries are caused by slips, trips, and falls during the mobilization of materials. Therefore, the validation experiments focus on a specific process of material mobilization in construction, aiming to demonstrate the reliability and effectiveness of this approach, which enables a significant advancement in understanding the workload of the construction workers and could have implications for improving overall safety and productivity in the field.

As an important part of the construction industry, bridge construction consumes a large amount of energy and materials. This study proposes a bridge structure optimization framework using a two-stage genetic algorithm (GA) in order to minimize the steel and concrete of prefabricated reinforcement concrete T-beams (RC T-beams). Under the constraints of corresponding design specifications, the first stage of the proposed GA is utilized to optimize the section size and area of the vertically-pulled steel of T-beam, with material cost as the optimization objective. Then the optimal combination of rebar diameters can be obtained by the second stage of the proposed GA. In the end, an illustrative example is provided to demonstrate the performance of the proposed framework and compares the proposed framework with conventional approaches.

With the rapid development of information technology in China’s construction industry, intelligent construction sites emerge. The State has issued a series of policies emphasizing on vigorously promoting research and development, manufacturing and application of advanced manufacturing equipment, intelligent equipment and related equipment on smart sites. On the basis of smart site, this paper takes influencing factors of project cost control as research object, carries out a lot of literature reading and field investigation, sorts out 20 representative influencing factors, invites experts to score each factor by Delphi method, and calculates weights of influencing factors by ISM-AHP method. The analysis shows that project management experience and system, the proportion of training fee for smart site personnel to total cost are the deepest factors affecting the cost control of smart site. Material utilization ratio, real-name passage fee to total cost ratio and enterprise management fee to total cost ratio are the most direct and most important factors affecting the cost control of smart site. An example is given to verify the feasibility and reliability of the model. Finally, based on the results of the study, reasonable and effective cost control measures and suggestions are put forward to provide reference for the development of smart sites, so as to improve the progress and quality of the project.

Local government bonds in China have undergone various pilot reforms, providing a useful research perspective for analysing local government debt issues. We use the pilot reforms in 2011 and 2014 as two quasi-natural experiments that adjusted the regime of local government bonds. We examine the impact of local government regime adjustments on regional environmental pollution using the difference-in-differences method. The empirical results suggest that regime adjustments have exerted a substantial influence on government expenditure compositions, thereby influencing the degree of regional environmental pollution. Our paper extends the study of local government bonds and sheds light on their social impact.

Scientific decision-making of design plans is the key factor in ensuring the sustainable construction and operation of prefabricated buildings. The complexity and uncertainty are major challenges. Therefore, a novel design decision-making indicator system and of supply chain scheme of prefabricated buildings were proposed. Firstly, an indicator system was constructed from the resistance and the resilience to solve the complexity of decision-making of supply chain scheme of prefabricated buildings. Secondly, a decision-making model based on the structure entropy weight method and the grey target theory was developed. Finally, a prefabricated building in Nanchang, China was selected for an empirical research. The results showed that resistance was the most important factor affecting the decision-making of supply chain scheme. The scheme ranking calculated in this paper was consistent with the actual situation, demonstrating the scientific and effective nature of this research. Through comparative studies with other decision-making methods, the novelty was emphasized. The research findings are of great practical significance for improving the sustainability of green building projects.

Since China has introduced its “Dual carbon” goal, reducing carbon emissions to reach carbon neutrality is a current research focus. This work proposes a structural equation model based on the theory of planned behavior to more effectively direct college students to practice low-carbon behavior (LCB) and meet China’s “carbon neutrality” target. In order to gather the fundamental information for this study, a total of 377 authentic questionnaires from college students in Chongqing were obtained. The structural equation model was drawn using Amos. 26. The study’s findings demonstrated that (1) college students’ willingness to engage in low-carbon behavior and low-carbon values can have a direct impact on low-carbon behavior; (2) that subjective norms, perceptual behavioral control, and the environment all have an impact on willingness to engage in low-carbon behavior; (3) that the environment facilitates both college students’ willingness to engage in low-carbon behavior and low-carbon values. Based on the findings, this study recommends that schools strengthen practical education relating to being close to nature to improve students’ perceptions of the environment. This would indirectly encourage college students to engage in low-carbon behavior by increasing their intentions to engage in low-carbon behavior and their values.

The emergence of Corona Virus Disease 2019 (COVID-19) pandemic has conclusively impacted global economies. This study conducted a systematic literature review to assess the impact of COVID-19 pandemic on construction costs in Australia. Upon the review of multiple sources, it was conclusively found that construction costs had blown out of proportion and many Australian projects were struggling. From the findings, many Australian construction businesses to be operating at a loss, major companies falling to the rising costs with many more liquidations to occur in the coming months, and the shortage of skilled construction professionals anticipated to be felt throughout till 2025. Despite these findings, future research can also be conducted into two areas to foresee if the costs that continue to rise are purely due to the COVID-19 pandemic or not. This can be done by assessing if the Ukraine and Russian war post pandemic has played a role in these costs and how the Australian weather through the last 12–24 months has impacted the progress on construction job sites.

With the rapid development of aviation industry, the relationship between airport and city is very contradictory. In the process of airport site selection, the relationship between airport and city based on air traffic analysis is very few, and the relevant research literature is also relatively poor. However, such analysis can provide sustainable and efficient guarantee for airport engineering planning and operation. From the perspective of air traffic, this paper analyzes various factors that affect the relationship between airport location and city, including noise, economy and air traffic connectivity (i.e., ground traffic). Based on ArcGIS software, this paper analyzes and evaluates them quantitatively and graphically. Finally, these indicators are weighted according to the analytic hierarchy process to obtain the final evaluation score. And to graphically draw, for the suitability of visual expression. The evaluation and analysis of the relationship between airport site selection and city can provide a strong theoretical basis for airport site selection decision, and also provide reliable preliminary planning opinions for the planning and development of city and airport in the future.

With the emphasis on green growth and sustainable development, modular construction (MC) has been increasingly adopted in both developed and developing countries. MC is considered a safe and efficient construction method since it shifts many aspects of on-site activities to off-site factories. However, the unique feature of MC can bring a new set of safety risks either in the manufacturing or construction stages. Existing studies investigate the safety accidents in MC through simple statistical analysis or expert interviews, but they fail to reveal the accident chain at a detailed activity level. This study aims to identify the critical causes of safety accidents in MC by using texting mining and network analysis approaches. First, 138 accident reports were collected from the Fatality and Catastrophe Investigation Summaries of the U.S. Occupational Safety and Health Administration and the corresponding government websites of China. Then, the network model of MC accident causes is established, and the indicators of the network model are calculated in order to highlight the critical causes. The findings of this study lay the foundation for the development of safety management systems for MC.

Based on the theory of full life cycle assessment (LCA) and the concept of “from cradle to cradle”, this study divides the whole life cycle of green building into five stages: early stage, materialization stage, operation stage, demolition and scrap and material recycling, constructs the carbon footprint calculation model of green building in each stage, collects and calculates the carbon footprint factors, and forms a carbon footprint database. In addition, the consumption calculation method based on BIM is combined with computer technology to build a calculation platform, and then the carbon emission calculation of a three-star green building in Jiangsu at all stages of the life cycle is studied. The research results show that the operation stage and materialization stage are the highest carbon emissions in the full life cycle of green buildings. The quantitative calculation of the full life cycle carbon footprint of green buildings based on BIM Technology is conducive to promoting social sustainable development and helping to achieve carbon peaking and carbon neutrality goals.

Buildings are widely known as the most crucial element of urban landscapes, and extracting building elevations is a cornerstone of many modeling procedures. Although laser scanning can accurately obtain building façade information, its use is too expensive. This paper introduces a novel approach to automatically generate sketches of building facades using computer vision technology when building facade images are captured. We employed computer vision algorithms with CAD secondary development technology to extract the facade and windows from a single building image and convert them into a simplified CAD style. Initially, the proposed method conducts semantic segmentation to distinguish various regions of the building. Next, the 2D coordinates of the identified objects are automatically extracted, and the extracted 2D point set coordinates are fitted and inputted into the CAD system for automatic drawing. The generated sketch effectively captures the building’s facade information while also accurately representing the dimensions of the windows and facade. Our proposed method has undergone evaluation using real-world building image data. The experimental results demonstrate that the proposed approach can generate precise facade sketches and attain 85% recognition accuracy for windows. The entire process, starting from semantic segmentation to mapping CAD drawings, can be completed in 2–2.5 s. This research has potential applications in applying computer vision techniques in modeling in architecture, urban planning, and computer graphics.

Forest recreation is now a popular mode for promoting physical and mental health and enhancing well-being. However, as the number of tourists increases, its environmental impact gradually becomes apparent. Quantifying carbon emissions of forest health base is considered to be the first issue that needs to be solved towards green and low carbon tourism. Therefore, this research aims to develop a method for calculating carbon emissions of forest health bases. Firstly, based on the existing studies, the calculation boundary and calculation framework of carbon emissions of forest health base were determined. Secondly, based on the whole life cycle perspective, specific formulas for calculating carbon emissions of forest health bases were constructed from construction, service, and management and maintenance respectively. Then, the established calculation method was validated by case study. Finally, combined the carbon emissions calculation results of the case base, key nodes were dissected and corresponding carbon reduction strategies were proposed from the government, tourism enterprises and tourists respectively, including: (1) strengthen carbon source analysis and develop targeted countermeasures; (2) increase publicity efforts to cultivate visitors’ low-carbon awareness; and (3) strengthening the protection and construction of base carbon sinks. Findings of this research provide a means to quantify carbon emissions from forest health bases and help promote the green, low-carbon and sustainable development of forest health bases.

In the event of emergencies, inadequate emergency management in large-scale building construction projects can result in significant losses and adverse social impacts. Currently, there are still numerous problems in emergency management for these projects, and related research is insufficient. Therefore, this study aims to clarify the current state and improvement demands of emergency organization and resource management for large-scale building construction projects during the construction phase when handling unexpected emergencies. To achieve this aim, this study first identified potential problems and improvement demands in emergency organization and resource management for large-scale building construction projects through theoretical analysis and expert interviews. Then, this study developed a survey questionnaire and collected responses from 30 construction professionals in China who have participated in large-scale building construction projects. The analysis of the research results finalized the current state and notable improvement demands of emergency organization and resource management for large-scale building construction projects. The results first indicated that: (1) in terms of emergency organization management, work coordination among relevant parties and the quality of information communication have significant improvement demand; (2) in terms of emergency resource management, forecasting of emergency resource demand, acquisition channels, and reserve mechanisms show improvement demand. To address these demands, emergency organization management requires optimization from the perspectives of internal and external coordination mechanisms and information communication mechanisms; Emergency resource management requires improvement in both forecasting methods and reserve models. The results of this study first provide a foundation and direction for future research on emergency organization and resource management in large-scale building construction projects. Moreover, stakeholders in these projects can refer to the results of this study to identify their weaknesses in emergency organization and resource management and propose optimization strategies in a targeted manner.

Global climate change is today’s biggest threat, encompassing rising extreme temperature events. The increase in the thermal mass of the building envelope by adding phase change material (PCM) is one of the promising measures to improve the thermal performance of buildings. In this study, a calcium carbonate (CaCO3) coated PCM-impregnated recycled concrete aggregate (PCM-RCA) is developed by immersion and injecting carbon dioxide (CO2) techniques. Two concrete samples using RCA and PCM-RCA aggregate are prepared and compared their thermal performance in building envelopes through EnergyPlus simulation for Sydney, Australia. The results showed that using PCM-RCA concrete in exterior walls could reduce the peak indoor air temperature by 1 °C and have an energy savings of 14.2% compared to the reference house.

The simulation of operational carbon is the focus of the building design phase because it accounts for a significant part of building life cycle carbon emissions. However, operational carbon simulation based on industry foundation classes (IFC) is still challenging in the early design stage without building equipment information. Therefore, this paper takes IfcSpace as the calculation and storage unit for operational carbon. Furthermore, a building is used as a case study to verify this approach. The case results show that this building generates 90,818.7 tCO2eq carbon emissions over its 50 year operational period. The office room type contributes the most significant carbon emissions (41.8%), and the computing center has the highest carbon emission density at 443.28 kgCO2eq/(year m2). This study extends the property set of IfcSpace for operational carbon simulation without building service models, which helps optimize operational carbon at an early design stage.

China’s 14th Five Year Plan proposes to promote urbanization construction with county towns as an important carrier, ushering in unprecedented opportunities for county town urbanization development. This article analyzes the macro environment faced by promoting urbanization with county towns as an important carrier from the perspectives of national development strategy, industry policy development, local government status, and enterprise development demands. By analyzing the current development status and difficulties of the old urban area, expanded areas, and surrounding towns in the county town, a concept of spatial integration of county town urbanization based on “three-level gradient integration” is proposed. Based on the theory of urban governance and the characteristics of county town urbanization, the mechanism of multi-agent linkage to promote county town urbanization was explored and sorted out. Subsequently, based on policy analysis such as land acquisition and integrated land improvement, and based on models such as “Investor + EPC + O” and ROT, a path for promoting county town urbanization through the linkage of government, enterprises, and residents is proposed, providing a reference for promoting urbanization with county town as an important carrier.

Despite that there is an increasing trend of BIM adoption in the construction industry globally, the perceived BIM benefits among construction professionals are not fully explored. In addition, majority of extant studies have been conducted in a single region. A cross-regional comparative study between China and the UK was hence conducted to investigate the differences and similarities of perceived BIM implementation benefits in the two countries. A total of nine critical BIM implementation benefits were first identified via an extensive literature review and a pilot study. Then, an online questionnaire survey was administered among the construction professionals in China and the UK. A total of 170 valid responses were collected, with 67 from China and 103 from the UK. Both Mann–Whitney U test and quartile analysis were employed to analyse data. The results show that improved visual management, improved information management, and enhanced planning and scheduling were perceived most significantly differently in the two countries, whereas design optimization was the common critical BIM implementation benefit in both countries. Further analysis reveals that the different level of BIM expertise, the different perceptions on BIM definition, the different degree of governmental and organizational support, and the different construction practices in the two countries were the major factors contributing to such differences. A set of recommended strategies were also provided for achieving successful BIM adoption and implementation in the two construction industries.

Compared with general civil engineering, the horizontal and vertical construction spaces of super high-rise projects intersect more closely within the same time window, which makes the interlocking of resources more likely to cause scheduling confusion. Accordingly, the paper introduces the Multimode Resource Constrained Project Schedule Problem (MRCPSP) model to solve the optimal construction scheduling scheme with the objective of duration-cost-resource balance. In terms of model optimisation, considering the ambiguity of the traditional MRCPSP in defining multi-mode combinations, a definition based on the upper and lower bounds of duration as well as a P-MRCPSP (with Pre-emptive execution) model based on mode optionality for specific processes are proposed. In terms of goal setting, this paper innovatively introduces the resource tension index and realises the resource levelling calculation based on the risk importance of resources. In terms of algorithm optimisation, the NSGA-II algorithm was considered to solve a multi-mode multi-objective model in which the task chain table coding sequence could not satisfy the process logic; the serial scheduling import process was also inefficient, and the elitism strategy high-density solution was easily rejected. The results of the algorithm showed that the minimum duration and cost scheduling solutions were superior to the original solutions, and new patterns were identified for the relationships among the three objective values and for resource scheduling.

Transportation system resilience, as a core element of transportation safety research, has been receiving extensive attention. In recent years, complex network theory has provided new perspectives and directions for the study of transportation system resilience, but few scholars have systematically sorted out the existing research on complex network theory in transportation system resilience. Therefore, this paper firstly analyzes the development trend, publication distribution and hot keywords of transportation system resilience by using bibliometric analysis and summarizes the research hotspots in this field. Secondly, from the definition of transportation system resilience, the existing research is sorted out from three aspects: evaluation indexes and evaluation methods of transportation system resilience, simulation modeling of disruption scenarios, and optimization strategies of transportation system resilience. Finally, the main problems and challenges faced by the existing research are summarized systematically, and the future development direction of transportation system resilience is proposed.

Current construction safety training evaluations are in the form of textual readings and lectures, failing to replicate the retention of safety knowledge in practice. This paper aims to propose a VR-based evaluation method for construction safety training and examine the effectiveness of the VR-based evaluation method compared with the traditional methods. An experiment was conducted with a 2 × 3 experimental design based on the cognitive psychology. Participants were divided into a traditional evaluation group as the control group and a VR-based evaluation group as the study group. Results indicated that in the short term, VR-based evaluation performed better than traditional evaluation in distinguishing whether inaccurate hazard attention or handling caused participants’ unsafe behavior. In the long term, VR scenes might consolidate safety knowledge and enhance memory retention. The findings of this study highlight the advantages of VR-based evaluation in construction safety training, which could be useful for practitioners to improve construction safety.

Based on the prerequisites of regional development in Hongshan District, the complex network theory was applied to evaluate the development functions. The evaluation results show that the above-ground space in Hongshan District needs to meet the high demand development of residential space on the one hand and focus on improving the functions of urban green land and municipal public service land on the other. Based on determining the development direction of the above-ground space, the site selection strategy and development direction of the underground space is proposed based on the suitable parcel D1 and parcel D2 respectively. The underground space of parcel D1 is suitable for the development of a municipal public facilities system, and the underground space of parcel D2 is suitable for the development of a transportation system and underground commercial complex.

In order to satisfy the development needs of cities and conform to development trends, urban planners typically take into account factors such as urban function type and functional density. The purpose of this study is to construct a complex network model of urban functions and to determine the future direction of urban development. Before constructing the model, the researchers used tools such as crawlers to collect POI data within the city and classify them to determine the categories of city functions and the associated quantities. Next, urban function data were analysed and calculated to ascertain the dominant functions of each administrative district after the epidemic. In addition, the conditional probability formula was utilised to examine the interdependence between the city's functions. Finally, an urban function network model was created to investigate Wuhan, Hubei Province, as a case. The findings revealed that the current urban function layout of Wuhan city is dominated by living services, with transportation facility services, catering, and retail services as auxiliary functions, and that these functions are interconnected. The study contributed to the harmonious and coordinated development of Wuhan by providing a theoretical foundation for its development direction.

Project scheduling to maximize net present value (NPV) poses a significant challenge due to the inherent complexities associated with large-scale projects comprising multiple work packages and uncertain task durations. Existing scheduling methods encounter difficulties in effectively maximizing NPV when confronted with multi-work package projects characterized by stochastic task duration distributions. In light of this problem, this paper proposes a three-level reinforcement learning (TRL) framework aimed at addressing these challenges. To determine resource allocation for each work package within the project, the TRL framework leverages human empirical decision-making at the resource assignment level. At the work package level, a Priority Experience Replay Dueling Double Deep Q-Network (PER-DDDQN) is trained. This PER-DDDQN incorporates a graph embedding method, enabling it to maximize the expected NPV for each work package. The graph embedding method facilitates the determination of the work package's scheduling state, while the PER-DDDQN governs the scheduling of task start times within the work package. Furthermore, at the project level, work packages are scheduled using the same principles employed at the work package level to maximize the expected NPV of the entire project. Numerical experiments conducted on adapted case projects provide evidence that the TRL framework surpasses existing heuristics in achieving higher NPV for most work packages. Moreover, the TRL framework yields a minimum improvement of 26.68% in the maximum expected NPV of the entire project compared to the heuristic method employed in this study. This research contributes significantly to the enhancement of cash flow management in large-scale projects characterized by multiple work packages. Additionally, it opens up possibilities for the integration of reinforcement learning technology within the field of construction project management.

Faced with ever-increasing demands and complex social challenges, research on corporate social responsibility (CSR) in the domain of international construction is becoming increasingly rich and sophisticated. Nevertheless, a systematic review on CSR of international contractors in this field is yet to appear. Combining Citespace bibliometric software and traditional literature review methods, this study provides an overview on CSR of international contractors from three aspects: research on the influence factors of CSR fulfillment; research on the relationship between CSR fulfillment and corporate performance; and research on the effect of CSR fulfillment. It is considered that the external factors affecting CSR fulfillment mainly focus on the national macro environment, while research on internal factors focus more on contractors’ CSR awareness; research related to CSR fulfillment and corporate performance have experienced a process from static to dynamic and from single to comprehensive; scholars generally agree that the improvement of CSR investment will bring long-term economic benefits to enterprises when studying the effect of CSR fulfillment; the topics of CSR research under stakeholder theory are highlighted in the end. Based on these, this study emphasizes the possibility of combining future research issues related to CSR in the international construction field with ESG philosophy. This study presents a comprehensive overview of the current status of research on CSR, providing practitioners and researchers with an in-depth understanding of CSR research in the domain of international construction.

In order to track the research trend and hotspots sustainable construction of transportation infrastructure, this research conducted quantitative analysis and visualization analysis of relevant literature in Web of Science from 2015 to 2023 by using the softwares of Vosviewer and Citespace, explored the development of this field from the perspectives of keyword distribution. In addition, knowledge maps such as key co-occurrence label view and keyword timeline graph are drawn, so as to introduce the research hotspot and development trend in this field. It is found that the research hotspots in this field are mainly concentrated in three aspects: (1) the relationship between transportation infrastructure and ecological environment; (2) the economic effects of transportation infrastructure; (3) the social problems of transportation infrastructure. Some new hot research directions have emerged in recent years, such as application of new energy and new technology in transportation infrastructure, construction of green transportation infrastructure and ecological corridor, the relationship between economic agglomeration and transportation infrastructure, the measurement and evaluation of economic spillover effect and network effect of transportation infrastructure, sustainable urban transport policy research and urban transport and social behavior research, it will provide some reference value for the subsequent research of scholars in this field.

With the continuous expansion of infrastructure coverage, road foundation projects are gradually moving to remote mountainous areas, and the number of high side slope projects is gradually increasing, which will inevitably damage the geological structure during construction and may cause safety problems such as landslides and slope instability. The traditional methods of high slope deformation risk assessment are to assess the high slope deformation risk by comparing the difference between the accumulated deformation value and the industry standard, but such methods are difficult to accurately identify the location and changes of deformation risk and are not accurate enough. This research proposes a complex network method based on time series to analyze and assess the risk of high slope deformation from a systematic perspective based on deformation data, and addresses the limitations of traditional methods. The raw time series of slope surface deformation instrumentation data during construction were measured into a similarity matrix with correlation coefficients. A threshold was then determined and binarized into adjacent matrix to identify the optimal topology and structure of the complex network. Finally, the statistical features of complex networks are applied to describe the deformation risk distribution of high side slopes. A high slope project case of China's Menghua coal transportation railroad was applied to verify the effectiveness and feasibility of the proposed approach. Results demonstrated that the proposed approach confirms the small-world and scale-free characteristics of high slope deformation processes and evaluates the significance among high slope deformation time series. This approach can be a tool for extracting macroscopic and microscopic decision information from complex systems perspective for high slope multi-deformation time series. It not only offers a systematic way to interpret deformation risk of slope environment in high slope construction but also has the potential to improve safety performance in construction sites.

With the accelerated urbanization process in China, the massive gathering of the population to cities, and the improvement of urban construction levels and functions, the safety and importance of cities cannot be ignored. Today’s cities face many risk factors and frequent natural disasters. A scientific resilience evaluation system can objectively evaluate the level of resilience construction of cities and effectively guide cities for targeted enhancement. This paper uses the super-efficient Slack Based Measure(SBM) model to build an evaluation system that divides 27 tertiary indicators by secondary nature from the three dimensions of resilience, recovery, and adaptation. It selects 29 major large and medium-sized cities nationwide to measure their resilience levels from 2018 to 2020. The research results show that the development of resilience in China’s cities is on the rise. However, the average level is not high, and the polarization is evident, with an “olive-shaped” structure. In general, Quanzhou, Yinchuan, Jiaxing and Xining are at a high level of resilience, with little change from year to year; Jinan, Kunming and Wenzhou are less resilient and temporarily short of development; Chongqing, Harbin and other cities have significant differences in resilience between the three dimensions of resistance, recovery and adaptation, and have uncoordinated development problems. In the subsequent urban development, resources can be appropriately tilted to supplement the short board in time, and low-resilience cities learn experience from high-resilience cities.

With the acceleration of global urbanization, urban agglomerations have become cities’ most common spatial organization. The resilience of urban agglomerations is of great significance to the high-quality integrated development of urban agglomerations. How explore the resilience of urban agglomerations has become one of the essential issues in promoting the construction of high-quality urban agglomerations in China. This paper uses the three-dimensional spatial structure model and the barrel theory to establish the urban resilience level assessment model based on the Slack Based Measure model of non-expected output super-efficiency. It also constructs a three-dimensional evaluation index system of urban resilience with three primary and 24 secondary indicators based on the three characteristics of a resilience system: resistance, recovery and adaptation. Taking 16 prefecture-level cities in the Chengdu-Chongqing urban agglomeration and nine coastal prefecture-level cities in the Pearl River Delta urban agglomeration in China as research objects, the resilience degree of the two urban agglomerations are as follows: a whole and the cities within them are measured from 2017 to 2022. In the latest year, 2021, both cities lack resistance resilience. The overall resilience of both urban agglomerations is at a high resilience level. However, the resilience of the Chengdu-Chongqing urban agglomeration is higher than that of the Pearl River Delta urban agglomeration. The resilience value of the central cities of the urban agglomeration is not necessarily higher than that of the smaller cities within the urban agglomeration. The level of city resilience is not only related to their internal conditions but also to the surrounding cities. In the past five years, the overall resilience of both urban agglomerations has been increasing. However, there is a trend of two levels of differentiation, which should be noted when building cities.

The application of information technology in the field of construction engineering and management has led to the construction industry undergoing a revolutionary and transformative shift, characterized by the emergence of numerous and diverse technologies that have invigorated traditional production models and processes, resulting in notable enhancements in production levels and product development. Despite the immense potential of intelligent construction and the significant improvements it could bring, this innovative approach has not yet been widely recognized and adopted globally. In fact, the utilization of new technologies in this domain remains disappointingly low, given their potential impact. To better understand and address this issue, this article utilized an ISM-based approach to fully analyze the influencing factors of smart construction adoption, in a bid to identify and examine the 16 most significant obstacles to the adoption of intelligent construction. To determine the causal relationship and impact intensity between factors, the DEMATEL model is applied, and the resultant causal relationship between factors is visualized to evaluate the presence and strength of the key factors. To establish a multi-level model of the identified obstacles, an interpretive structural model is used, with the result being a comprehensive strategic framework that addresses the 16 major obstacles. The key factors that affect the development and popularization of intelligent construction are then sorted out, their importance and interaction relationships analyzed, and the differences in the action paths of different key factors are discussed. Against the backdrop of informatization and intelligence becoming the development trend of the times, this article provides countermeasures and suggestions for the future development and popularization of intelligent construction, with the ultimate goal of accelerating the transformation of the construction industry from traditional construction methods to intelligent construction.

There are many drawbacks in traditional housing data governance, but the rise and application of blockchain technology provides a new idea for housing data governance. Based on the analysis of the characteristics of housing data and the shortcomings of traditional housing data governance as well as the possibility of enabling housing data governance by various functional technologies of blockchain and its mechanism of action, this paper combines the governance objectives, governance objects, participating subjects, governance processes and governance means of blockchain technology to enable housing data governance to construct the governance realization path model of the data layer enabling housing data collection and storage, the network layer enabling housing data query and transmission, the consensus layer enabling housing data consistency verification, and the contract layer enabling housing data use and transaction, so as to achieve the goals of authenticity, privacy, sharing and consistency, and low the intermediary fee of housing data governance.

Stabilizing atmospheric greenhouse gases and maintaining economic growth are two objectives of the urbanization process. Reconciling them means “carbon productivity”. Despite the burgeoning body of knowledge on measuring total factor carbon productivity (TFCP) at the construction industry level, TFCP of construction projects had never been made explicit systemically. The contradiction between the relative concept of TFCP and the uniqueness of construction projects caused the “comparing apples to pears” syndrome in unifying the conceptualization and identifying the parameters to analyze and improve TFCP of construction projects. All these suggest a significant knowledge gap in effectively addressing the complex socio-technical features of construction projects’ TFCP. Thus, this study aims to fill the gap by formulating system boundaries of TFCP for construction projects. The model elaborates the boundaries of construction projects’ TFCP in four dimensions with 11 system boundaries: policy timeframe, project timeframe, climatic, urbanization, carbon emission, energy supply, stakeholder, parameter, project typology, density, and business sector. The boundaries are validated through multiple real-life cases in Chengdu, China. This paper concluded that although the boundaries are described separately, they are dynamic and interactive. The findings also indicate that the TFCP of construction projects should be regarded as a complex socio-technical system, and the proposed boundaries should facilitate the paradigmatic improvements in the knowledge of construction projects’ TFCP.

Based on the complex network theory, the urban rail transit networks of ten Chinese cities with relatively developed rail transit systems were selected as research objects, and the topological model of the rail transit networks of the case cities was constructed using the Space L method, with complexity, connectivity, and extensibility as macroscopic feature metrics, and node average, tightness centrality, mesoscopic centrality, average path length, and average clustering coefficient as microscopic feature metrics, combining macro and micro characteristic indicators, the entropy weight method is used to determine the weight of each indicator, and the TOPSIS method calculates the comprehensive score index and the maturity analysis of typical urban rail transit network in China is conducted based on the calculation results. The results indicate that: Most of China's urban rail networks are still in the early stage of development, and the line network structure is still immature, but the primary network architecture has been built. Among them, Shanghai's rail line network is at the fourth stage of maturity, with a high degree of line development and a dense network. Beijing, Tianjin and Chengdu are in the third stage. Their development degree is low compared with Shanghai, which has formed a relatively complete rail network system. Wuhan, Zhengzhou, Guangzhou and Chongqing are in the second stage, and the basic structure of their rail network has been built, but the network tightness is not yet high. Xi'an and Nanjing are in the first stage of maturity. The development of their rail transit systems is still in the early stage, with a few complete and formed rail transit lines and closed lines.

China rarely implements on-site sorting regarding construction waste disposal, and on-site sorting of construction waste is conducive to improving the utilization rate of construction waste resource utilization. The implementation of construction waste classification and sorting, i.e., the construction waste on the construction site is cleaned and placed separately, is an effective way to improve the recycling rate of construction waste. The paper analyzes the factors affecting on-site sorting of construction waste through literature research and proposes corresponding policy recommendations for these factors, with a view to providing references for promoting on-site sorting of construction waste.

In order to investigate the mechanism of illegitimate tasks on construction workers’ unsafe behavior and to reduce the occurrence of safety accidents in the construction industry, this study develops a theoretical model based on Cognitive-Affective Personality System theory with emotional exhaustion and group identification as mediating variables. 213 frontline construction workers were selected as respondents to conduct this study on safety management practices. The research findings reveal that illegitimate tasks have a beneficial influence on construction workers’ unsafe behaviors, and that emotional exhaustion and group identification mediate the link between illegitimate tasks and construction workers’ unsafe behaviors. The research findings shed light on the mediation mechanism between illegitimate tasks and unsafe behaviors, further researching into the underlying causes of construction workers’ unsafe behavior, and provide references for project managers to prevent and control the unsafe behaviors of construction workers successfully.

The rural tourism destination residents are the core participants in organizing rural tourism activities. Promoting environmental responsibility behaviors among them is of great significance for achieving the rural revitalization strategy and promoting the sustainable development of rural tourism. Based on the Theory of planned behavior (TPB), this study constructs a framework of the influencing mechanisms of rural tourism residents’ willingness to engage in environmentally responsible behavior and environmentally responsible behavior based on 247 rural tourism site residents in Xingwen County, Sichuan Province, explores the moderating effects of environmental policy factors in each dimension on the willingness-behavior relationship, and empirically analyzes the theoretical framework using structural equation modelling (SEM). The findings show that subjective norms (SN), perceptual behavior control (PBC) and environmental attitudes (EA) have positive driving effects on environmental behavior intention (EBI), respectively; each dimension of environmental policy has different intervention effects on environmental behavior intention (EBI) affecting environmental responsible behaviour (ERB); and public participation-based policies (PPP) do not have positive moderating effects on the conversion of environmental behavior intention (EBI) into environmental responsible behaviour (ERB). Finally, the corresponding theoretical basis and management suggestions are provided to improve the environmentally responsible behaviour (ERB) of residents in rural tourism places.

The problem of construction and demolition waste (C&D waste) is becoming more and more prominent, which has attracted the attention of various countries and cities. Due to the different development speeds of various cities, there are also differences in the generation and disposal of C&D waste, which to some extent leads to uneven management levels of C&D waste in different cities. At present, there is still a lack of a framework to evaluate the effectiveness of C&D waste management at the city level. To make up for this gap, the study, through desktop surveys and brainstorming methods, based on three dimensions of C&D waste whole-process management, security system construction, stakeholder participation and sense of gain, a set of C&D waste management efficiency evaluation indicators consisting of 9 first-level indicators and 31 s-level indicators was established. The first-level indicators include source control of C&D waste, transportation management, resource utilization, final disposal, system construction, market system construction, technical system construction, regulatory system construction, stakeholder participation and sense of gain. This evaluation system will help cities develop their strengths and avoid weaknesses in C&D waste management, improve the level of waste management, and further promote the construction of “zero waste cities”.

Smart construction, driven by the integration of new generation information technology, has transformed the construction industry and mitigated traditional safety risks. However, the application of intelligent mechanical equipment, such as construction robots, introduces new safety risks. Existing studies have predominantly focused on safety issues in traditional construction sites, overlooking the emerging safety risks associated with new construction technologies in smart construction, which is not conducive to the safety control of smart construction. This study employs grounded theory and coding methods to identify safety risks and scenarios specific to robot work in smart construction, and analyzes the key risk factors extracted through expert scoring. The results highlight significant emerging risks that demand attention in the smart construction mode. This study helps to enhance safety awareness among operators and enable construction managers to identify key safety hazards, ultimately reducing accidents and improving safety performance of smart construction.

The effective and timely management of component information is essential for the smooth running of construction projects. Especially at the phase of development of construction industrialization, the project form of organization, planning, supervision, collaboration, and communication should be conducive to the construction project. Timely and effective management of component information is a fundamental requirement for the development of prefabricated buildings. Tracking accurately and location of components precisely are good for managing the entire process of prefabricated buildings. Component tracking and positioning is a dynamic process that is closely related to the content of each phase of work. Therefore, based on the in-depth understanding of the construction process of prefabricated buildings, the paper analyzes and summarizes the spatial information of components needed for each stage of work, combining BIM technology and RFID technology, using digital measurement technology and positioning identification technology to establish an IoT-based inspection system for the installation and positioning of assembly building components to meet the requirements of whole life cycle component tracking and positioning.

Improving the total factor productivity (TFP) is critical to the high-quality development of the real estate industry. To apply the TFP to explore the development path and regional differences of China’s real estate industry, this research develops Global Malmquist-Luenberger index model (GML) to assess the regional differences of TFP in the urban real estate industry. Dagum gini coefficient and kernel density estimation are used to analyze the spatio-temporal characteristics and dynamic evolution in China’s 35 cities covering the period of 2011–2020. The results show that the TFP in China’s real estate industry has presented cyclical growth, whereas the regional difference of the TFP has wavily declined. Besides, there is an obvious spatial imbalance in China’s real estate industry. The polarization exists between the eastern and central regions, and the spatial difference has a tendency to narrow in the western region. Based on the findings, this research proposes several policy implications for policymakers regulating the real estate industry. This research measures the TFP of China’s real estate industry in different cities and periods and investigates the resources of regional disparities. The findings of the research can provide important insights on the promotion of the development of China’s real estate industry, thereby enhancing regional economies.

In recent years, safety accidents during subway construction have been occurred frequently in China, resulting in significant casualties and property losses. To minimize the losses caused by such accidents effectively and identify key factors influencing subway construction safety, this study employs the Human Factors Analysis and Classification model (HFACS). Using 107 typical subway construction safety accident investigation reports as data samples, the study constructs an HFACS model that is applicable to subway construction safety accidents, and screens out 21 accident factors. Additionally, a prediction model for subway construction safety accident levels is developed using the Random Forest method to rank the accident factors in terms of importance. The results show that the accuracy of the prediction model using Random Forest reaches 0.88, and five key factors that have a greater impact on accident severity are identified. Based on these findings, the study puts forward targeted pre-control measures and recommendations to reduce the probability of subway construction accidents effectively.

Floods are a natural disaster that frequently occurs worldwide and has a high destructive power, with complex influencing factors. Establishing flood models for monitoring, simulation, estimation and prediction is crucial to reducing disaster risk and minimizing human and property losses. With the development of computer computing power and deep learning's powerful classification and prediction capabilities, the application of deep learning in flood research is increasingly attracting attention, leading to a significant increase in data-driven flood research using deep learning. This article uses a mixed method combining bibliometric analysis and qualitative analysis to analyze the characteristics and development trends of deep learning-based flood research. Using a bibliometric analysis of 425 papers in the Web of Science core database, scientific knowledge graph analysis was conducted, indicating that the field is entering a period of rapid development with a promising future. Based on the latest literature and combined with the bibliometric results,23 selected papers were qualitatively and systematically analyzed to accurately evaluate the current situation and future directions of deep learning in flood research. Three main research scenarios emerged from the qualitative results: flood sensitivity, flood inundation, and precipitation runoff. Combining recent research trends, the data sources, method choices and strategies for different research scenarios were summarized, and suggestions for future research development and innovation were proposed from three levels of data, algorithms, and research contexts to promote the further development and application of deep learning in flood research.

In the context of urbanization, mental health disorders are more and more becoming a presence that cannot be ignored. In fact, countries and organizations around the world have been trying to find ways to enhance public mental resilience. It has been discovered that built environment can generate mental recovery benefits, has now become a frontier subject in ameliorating the current state of mental health. To explore the relationship between built environment and mental health, this review first visualized the research hotspots and research focus, and then combined the keywords clustering results with qualitative analysis in three aspects: definition of the spatial scale of built environment, environmental elements affecting mental health and data acquisition methods, and analysis methods, and ultimately determined the existence of built environment have influence on mental health. In summary, first, the public attaches great importance to mental health, especially the younger depression and psychological dilemmas of the elderly. Second, the built environment evaluation index system influencing mental health has become relatively comprehensive, and the research hotspots have gradually shifted from macro-scale to meso-micro-scale. Third, the application trend of modern science and technology has gradually emerged, enhancing the accuracy of data. Fourth, built environment and mental health have been proven to be non-linearly correlated, providing a new perspective for research. The literature review indicates that the current research progress provides a reference and basis for urban planning and recommendation to improve public mental health, which is of great significance to the construction of healthy cities and communities.

In recent years, modular integrated construction have gained more and more attention for cleaner and more efficient construction methods. But there are also many challenges in the development of modular integrated construction. Most of the current production operation planning in precast concrete components (PCC) plants is based on experience, and the management model is rough, with problems such as waste of resources and high costs. In order to improve the production efficiency of PCC in the manufacturing process, it is necessary to obtain accurate information on the quantity, type and delivery time of PCC at the time of production, and then make initial optimization of the production strategy. Then, based on the data returned in real time from the passive radio frequency identification (RFID) components pre-embedded in the production process, the deviation process is corrected and rescheduling is performed to dynamically ensure that the production strategy remains optimal. In this paper, based on the graph constrained crating algorithm, a production strategy planning model for PCC of modular integrated construction is established. Using breadth-first search (BFS) technology, sorting algorithm with variable weights and RFID technology, it can realize dynamic scheduling optimization of production strategy for prefabricated orders with many types and large quantities, and achieve high efficiency and sustainability of prefabricated production.

Communication has been considered a crucial factor in successful project management. This study examines the associations among different types of network centrality of project participants, expertise coordination, administrative coordination, and communication efficiency in construction projects. Based on the data of 12 companies in China, this study empirically tests the hypothesis. We conduct social network analysis to understand the relationships between project participants of different organizations and the characteristics of communication networks in construction projects. Results of regression analysis connote that the degree centrality of actors, expertise coordination, and administrative coordination can positively impact communication efficiency. An in-depth study of different network roles and structures can provide a meaningful reference for improving the effectiveness and efficiency of organizational communication in construction projects.

Metro safety accidents have caused a large number of casualties, property damage and traffic congestion. In order to reduce the occurrence of metro safety accidents and to improve the level of metro safety management, it is of great practical significance to analyze the knowledge structure and hot research in the field of metro construction and operation safety. This study takes 179 articles related to metro safety research included in the core database of Web of Science from 2012–2022 as the research object. Based on the theory of knowledge mapping and CiteSpace software, the analysis about metro safety research was carried out by visualizing the development trend, cooperation network, keyword co-occurrence and research hotspots. The results show that the number of articles published on metro safety research has increased rapidly since 2017; the core group of authors and relevant research institutions are relatively fragmented; the research hotspots are focused on “subway station network”, “safety monitoring”, “deep foundation pit”, “subway fire”, “flood risk”, etc. In the future, it is worthwhile to deeply explore in the research field on data and intelligence of metro safety studies.

In order to accurately grasp the key factors affecting the resilience of the construction supply chain under the background of the VUCA era, 26 factors affecting supply chain resilience across six dimensions are identified from a holistic perspective of a supply chain in construction through questionnaire surveys and expert interviews. A multi-level structured model is established based on the interpretative structural modeling (ISM) method. The fuzzy comprehensive evaluation method is used to screen factors having significant VUCA characteristics in each dimension. Ultimately, a set of key factors that affect the construction supply chain resilience and have clear relationships and significant characteristics are obtained. The result of this study provides a more targeted theoretical basis for enhancing the construction supply chain resilience in the VUCA era.

International contractors are frequently confronted with complex operating environments, particularly during a period of turbulence and transformation. The institutional environment of the host country is one crucial factor that can significantly affect international contractors to operate efficiently. The complexity of social systems contributes to the uncertainty surrounding the institutional environment in which they operate. Thus, this research selects literature from Web of Science database for nearly 15 years, and analyzes all literature with the help of document visualization software VOSviewer aiming to examine the impacts of host country institutional environment on international contractors by way of a thorough, systematic literature review. The results show that the institutional environment of the host country exerts a profound influence on international contractors, and the impacts of the institutional environments in international construction exist in a multi-faceted context. Against this backdrop, this study aims to assess the effects of host country institutional environments on international contractors in these three areas, within the context of complex and turbulent environments. The analysis provides valuable insights into the challenges and opportunities facing international contractors seeking to navigate and succeed in uncertain institutional environments.

By collecting the research data from seven provinces in East China from 2003 to 2019, a spatial Durbin model (SDM) is established to study the spatial effects of carbon emission intensity in East China. Then the analysis of transport carbon intensity, grey correlation degree and spatial autocorrelation test are further examined. The results show that the transportation carbon emission intensity has a significant spatial effect among provinces in East China. The proportion of tertiary industry and population density has a negative spatial direct effect on transportation carbon emission intensity. Passenger turnover, urban public vehicles and highway mileage have a positive spatial direct effect on transportation carbon emission intensity. The urbanization rate has an insignificant spatial direct effect on transportation carbon emission intensity. Passenger turnover and highway mileage have a positive spatial spillover effect on carbon emission intensity. In contrast, the proportion of the tertiary industry, population density, urbanization rate and urban public vehicles have no significant spatial spillover effect on carbon emission intensity.

In recent years, greenhouse gas emissions have become a hot topic of international concern. Construction projects will result in significant greenhouse gas emissions. Accurate calculation of carbon emissions is essential for taking appropriate measures to reduce carbon emissions during construction. Several studies in the construction sector have been performed to measure and reduce greenhouse gas (GHG) emissions. However, extensive and in-depth research on this topic is lacking. This article describes its scope for calculating carbon emissions and analyzing prior research in the construction industry. Using the site as the spatial boundary, we used the carbon emission factor method (CEFM) to calculate energy and carbon emissions in the construction phase. Based on the selected carbon emission factor method of calculation, different sources of activity data of each carbon source and emission factor data in the construction phase are summarized in terms of activity data acquisition and carbon emission factor selection, and comparative analysis of data accuracy and difficulty of acquisition is carried out. Excellent practices are proposed according to different construction practical situations. This study's results will help better guide carbon emission calculations throughout the construction phase and give a more solid basis for data selection for carbon emission calculations across different scenarios.

As an important player in the future building sector, the study of carbon emissions (CE) during the materialisation stage of modular integrated building (MiC) is of particular importance. This paper divides the materialisation stage of MiC into five stages (building material production, transport, module integration production, transport and assembly stage), defines the boundary of the materialisation stage system in terms of time, space and objects, then the components of the CE measurement model of the materialisation stage and the influencing factors of each component are analyzed, and the model is constructed by using the CE factor method, and finally proposes emission reduction measures for each small stage based on the analysis results. The objective of this paper is to analyse and measure the CE during the MiC materialisation stage, and propose corresponding emission reduction measures to help the building sector achieve sustainable development.

The new Western Land and Sea Corridor is located in the western hinterland of China, connected to the Silk Road Economic Belt in the north, the 21st Century Maritime Silk Road in the south, and the Yangtze River Economic Belt in coordination, which is a critical development axis in China's regional development pattern. The high level of the high-speed railroad (HSR) network will further help the coordinated development of China's western region. This paper takes 73 prefecture-level cities in western China as research objects and builds an urban high-speed railroad operation network with cities as nodes and high-speed railroad operation lines as sides. Analyzes the topology of the network based on complex network theory analysis method and combines the k-core algorithm to stratify the network, adopts the idea of first dividing and then combining, divides the whole network into three layers. The results show that: high-speed railroad operation in western China network has a small world effect, and the network is dense its average path is small; the node cities show obvious hierarchical and regional characteristics, showing that the lower level of development of the high-speed railroad network, the more peripheral.

In order to improve the operational management of urban railway systems during rainstorms and waterlogging events, this study employed the PSR model to establish a framework to analyze the vulnerability factors and to uncover the vulnerability formation mechanisms in the operational aspect of the urban rail transit system. The ISM-MICMAC model was subsequently used to analyze the hierarchy of influence among vulnerability factors and influence paths. The study classified the vulnerability of urban rail transit systems into five levels. It was found that the degree of greening around the station and the regional economic level are significant contributing factors to vulnerability, and two dominant influence paths have been discovered. Therefore, improving the regional economic level can enhance the transit system's resilience and increase the overall disaster prevention and relief capabilities in the face of heavy rainfall and waterlogging events.

The construction industry is one of the significant contributors to global greenhouse gas emissions, generating about one-third of the world's carbon emission (CE) each year and having an enormous impact on environmental issues. Modular integrated construction (MiC) is the future trend of industrialised buildings, and it is crucial to study the carbon emission of MiC systematically. However, previous studies by scholars have mainly focused on traditional or industrialised buildings. Most research topics are estimated before building completion or accounting after building completion. There need to be more real-time carbon emission monitoring studies. This study proposes a carbon emission monitoring study based on CPS technology for MiC, defining the system boundary of the materialisation phase of MiC, identifying the carbon sources in each phase, constructing a measurement model, and building a real-time monitoring framework to obtain real-time carbon emission data of building activities and providing a basis for managers to manage the carbon emission process dynamically. This study provides a systematic knowledge framework to fill the gap in the real-time carbon emission monitoring of MiC. It provides a reference for future scholars' research on carbon emission monitoring of MiC.

Due to the challenges associated with obtaining specific building-level non-geometry information, the building archetype method has been proposed as a means of modeling building energy use on an urban scale. This method involves the classification of buildings and the characterization of archetypes. Traditional classification methods select certain building features that are known to have a significant impact on energy usage (such as building age and use type) and classify similar buildings into one category based on these features. However, these methods are subject to researcher bias. Recent progress in machine learning techniques offers new insights into this field. This paper proposes a method for developing building archetypes based on machine learning, and presents a case study of New York City. Building features are extracted from open-source datasets available on official websites. Well-known machine learning algorithms are employed to classify buildings based on these features. Each building category is assigned a template based on related ASHRAE standards to characterize the non-geometric parameters required for energy modeling input. The validity of this approach will be tested by simulating the total energy use of buildings using the proposed archetypes via the EnergyPlus engine and OpenStudio.

Construction inspection is critical to ensuring the quality and long-term performance of highway infrastructure. In the current practice the inspectors need to manually gather and interpret the lengthy quality standards for inspecting and evaluating the highway construction work, which is subjective, error-prone, and time-consuming. Many transportation agencies have developed inspection forms to organize construction requirements that reside in textual documents (e.g., standard specifications, construction inspection handbooks, and quality standards) in the format of checklists in order to reduce the workload for inspectors and enhance productivity. However, due to the missing link between the inspection forms/checklists and the construction work under inspection, the inspectors might need extra work to find the applicable forms/checklists for inspection. This paper thus proposes an image-based approach to establish the missing link. First, use computer vision algorithm to understand the construction scene in the image and deduce the construction activity through the elements in the scene. Second, the semantic structure underlying the quality standards for highway construction (a Chinese quality standard for highway engineering is selected as the case) is analyzed, followed by the re-structuring of the quality requirements as inspection forms. Then the inspection forms are associated with their relevant construction activities. Last, an image-enabled prototype is presented to illustrate the generation of customized inspection forms for a given construction site image. With this newly developed tool, field inspectors can get rid of the overwhelming texts in the quality standards and can be equipped with the necessary knowledge regarding what and how to inspect.

Due to China's fast urbanization, the expansion of urban construction land has recently received significant academic attention, and the assessment of the effect of land finance on urban spatial development has been expanded. Even though the significance of land issues is well acknowledged, there hasn’t been much research done to analyze how land finance affects the expansion of urban construction land. Few studies have been completed at the macro level or on the variations in impacts among cities in different areas, and the research that has been done tends to be case studies from some typical cities. Using panel data from 29 provincial administrative units in China from 2008 to 2020, this paper introduces the two-way fixed-effects model to further explore the relationship between land finance and the expansion of urban construction land in China at the national and regional levels. According to the study's findings, local governments' incentive to grant extra-budgetary revenue makes them attempt to increase the scale of land finance through outward sprawl. This encourages the expansion of urban construction land. Further research reveals that there are large regional disparities in the impact of land finance on land expansion for urban development. Based on regional economic conditions, land finance has a significant positive impact on the expansion of urban construction land in the middle and western areas. Finally, appropriate policy implications are suggested based on the findings of the empirical analysis, including improving the land property rights system properly, enhancing the regulatory framework, and maximizing the potential of stock land, which have consequences for directing the orderly growth of cities and attaining sustainable urban development.

Ethical issues are prevalent in the engineering practice of China’s construction industry. Engineering managers are expected to serve as the core ethical agents and assume primary ethical responsibilities, but they often lack the necessary ethical sensitivity and relevant experience to identify and address ethical problems effectively. Based on relevant theories, this paper proposes a five-dimensional method for identifying ethical problems in engineering management and constructs an analytic framework for the corresponding managerial measures, which are applied to a case study. Based on the Daxing International Airport Terminal Core Area Project, the elements adjusted of effective managerial measures in the case project are analyzed and seven principles of managerial mechanism design for dealing with ethical issues are proposed. The paper provides theoretical understanding and practical suggestions for engineering managers in the construction industry in response to similar ethical issues through managerial mechanism design, including improving managers’ ethical awareness and competences, adjusting responsibility-power-interest relationships reasonably and fairly, setting appropriate incentives, optimizing management processes and regulations, aligning with macro values, investing in engineering innovation while avoiding consequential risks, and introducing influential subjects as media.

Smart city requires the comprehensive utilization of heterogeneous data sources, and the integration of Building Information Modeling (BIM) and Geographic Information System (GIS) can provide the primary data for buildings and cities. Industry Foundation Classes (IFC) and City Geography Markup Language (CityGML) respectively serve as the standards of BIM and GIS, and play an important role in the data integration of BIM and GIS. Due to incompatible data formats and semantic mismatches, BIM/GIS integration faces unsloved challenges. A bibliometric analysis was performed on Web of Science using “IFC” and “CityGML” as keywords, covering the time period from 2013 to 2023, to identify the development trends of IFC-to-CityGML conversion and the authors’ academic contributions. After excluding irrelevant papers and removing the time limit, 35 papers were eventually reviewed. A systematic review was conducted on geometric conversion and semantic mapping in IFC-to-CityGML conversion. Currently, complete IFC-to-CityGML conversion cannot be achieved, and the algorithms related to geometric conversion and semantic mapping still need to be improved.

In the era of globalization, major urban events have emerged as a critical tool for sustainable urban development due to their potential to contribute towards the spatial restructuring and economic and social progress of a city. The 19th Asian Games, which Hangzhou has been selected to host, is one such event that has garnered significant attention in recent years. To better understand the impact of this event on Hangzhou's residential property prices, this paper employs a Difference-in-Differences (DID) model, utilizing data from 2013 to 2022. The results of the study reveal that the location of the Asian Games Village has a significant effect on the surrounding residential property prices. Specifically, the prices of properties within a 2 km radius of the Asian Games Village experienced a significant increase after the site was identified in 2018. However, the identification of Hangzhou as the host city in 2015 did not have a substantial impact on the residential prices around the Asian Games Village. Further analysis indicates that the Asian Games may influence housing prices in Hangzhou through various mechanisms, including infrastructure development, creating housing demand, and boosting housing price expectations. As the Asian Games draws closer, there is likely to be an increasing demand for housing in Hangzhou, which could drive up prices even further. Overall, the findings of this study suggest that major urban events like the Asian Games can have a significant impact on a city's residential property market. Therefore, policymakers and urban planners must carefully consider the potential economic and social implications of hosting such events to ensure that they contribute positively to sustainable urban development.

Construction and demolition activities generate a significant amount of construction waste, which has a negative impact on the natural environment and human health. Both academia and industry are exploring how to use information technology for efficient management. Blockchain technology, as a disruptive information technology, is increasingly valued for its unique technical features in addressing management problems. In order to gain a deeper understanding of how blockchain technology addresses the issue of construction waste management, as well as to further clarify the research hotspots and trends in this direction, this paper conducted a detailed analysis of relevant publications from 2019 to 2023, investigating the latest research in blockchain technology for managing construction waste. This was achieved through analyzing aspects such as the countries where the author was located, keyword collaboration networks, national collaboration networks, and research topics. The results of the paper indicate that blockchain technology-based management is still in its early theoretical stage and lacks practical application in specific sectors. It is worth noting that blockchain technology has real potential to improve management performance and regulatory models in multiple sectors and can provide solutions to many challenges. Therefore, based on the evaluation, we expound on the opportunities and possibilities for using blockchain technology in construction waste management.

The analysis of China's housing system leads to the conclusion that there are three significant issues with affordable rental housing. First, it is important to improve access standards and rent pricing, followed by the lack of demand-side subsidies, the imperfection of the legal and regulatory framework governing housing security, and the absence of specialized management organizations. This paper makes recommendations for the future development of affordable rental housing in China from the perspectives of rent subsidies, specialized institutions, and the legal supervision system by summarizing the experience of affordable housing policies and systems in Germany, the United Kingdom, the United States, and other developed countries.

With the slowdown of new urban development, urban renewal has entered a stage of sustainable development. The renewal of old communities in urban renewal has attracted increasing attention from scholars due to its large size and the numerous residents involved. As the main participants in old community renewal, residents gradually have a greater say in old community renewal. Resident participation can be defined as the process in which community residents participate in decision-making, management, and supervision of community affairs through certain channels and forms, participate in expressing opinions, and exercise their rights. However, currently, scholars lack a clear and systematic understanding of resident participation in old community renewal. With the large-scale renewal of the old community being carried out, promoting understanding of this field is becoming increasingly important, and the focus of this study is to fill this gap. Based on a review of 70 journal papers related to resident participation in old community renewal, this paper explains the connotation of resident participation and proposes to summarize factors influencing resident participation into four parts: participants' factors, participation prospects, participation process management, and support from relevant departments. This article studies the role of resident participation, incorporating it into the government needs, the resident’s needs, and the connection between the government and residents. Although optimizing resident participation is difficult, to better serve old community renewal, this study proposes four suggestions, including enhancing residents' sense of community identity, training residents' ability to participate, building a communication platform, and confirming resident participation in planning. This review article can serve as an important reference for researchers interested in the theme of resident participation in old community renewal.

Most current research on healthcare facilities carbon emissions focuses on the dynamic data generated by the building entity and systems without considering human activities and built environment as an integrated system. This paper presents a model-driven approach for carbon emission assessment in healthcare facilities that can: (i) support convergence of carbon emissions impact factors analysis from human activities in building systems, (ii) integrate spatial computing and Internet of Things technologies for real-time performance assessment of carbon emissions in healthcare facilities, (iii) enable continuous optimization of carbon emission performance. Recognizing the gap, this paper presents the model-driven framework for energy efficiency optimization and prediction in hospital buildings including three layers: schematical model, data collection and assessment model, adaptive and predictive model. An illustrative case is presented to state how the model-driven paradigm applied in a real-world hospital energy management platform.

The use of robotics and automation in construction has become increasingly popular in recent years, but negative attitudes toward working alongside these technologies persist. This is a significant issue, particularly for construction students who are future construction professionals. Understanding the safety challenges and countermeasures associated with working with robots on-site is essential for improving attitudes toward them. However, logistical and financial constraints present challenges in incorporating an effective robot-related learning tool or alternative into construction education. To address this issue, this study developed a virtual site visit, which integrates Virtual Reality (VR) technology and immersive storytelling to expose students to a robot-dominant construction site. The virtual site visit allows users to navigate a robot-dominant construction site and learn about four-legged robots, their applications, safety challenges, and countermeasures for working safely with them. This study aims to explore the potential relationship between students’ backgrounds, specifically their construction experience and familiarity with construction robots, and the impact of virtual site visits on improving attitudes toward construction robots. The findings indicate that following the virtual site visit, there was a significant decrease in negative attitudes among students with limited construction experience and limited knowledge about robots.

With the implementation of construction informatization, the positive effect of BIM technology on project cost control is recognized. Still, the application of BIM technology in cost management needs to be more popular. This paper mainly focuses on the application of BIM technology in the whole process of engineering cost management and uses the literature review method to study the factors affecting its promotion and application in four aspects, such as policy, economy, society and technology, and makes suggestions on this basis to provide a reference for the subsequent related research.

Under design-build mode, the “Employers Requirements” has become an important basis for the employer to exercise contract control. In the fields of building and municipal infrastructure of China, employers often formulate “Employer's Requirements” based on relevant policy documents but lack analysis of the logical components and planning process involved. This leads to increased risks in contract control for the employer. This study defines the connotation of “Employer's Requirements” by examining relevant policy documents and contract samples. It clarifies the system requirements for employers in formulating “Employer's Requirements” and designs a planning process for them. Furthermore, we construct a framework system for “Employer's Requirements” based on functional and quality standards, as well as contract measurement and pricing rules. This study proposes that the formulation of “Employer's Requirements” should be based on a reconstructed project breakdown structure, incorporating quality control and cost considerations throughout the construction process. The framework system is constructed from five aspects, which are project decomposition structure, functional and quality standards, construction requirements, operation and maintenance requirements, and measurement and pricing rules. The aim is to provide references for employers in formulating “Employer’s Requirements”.

The construction industry has played a significant role in socio-economic progress while it has brought a large amount of energy consumption during its rapid development. During the whole life cycle of buildings, the energy consumption in operational phase accounts for over 80%, where the electricity consumption of Heating Ventilation and Air Conditioning (HVAC) system takes a large part. Research has been widely carried out on the energy conservation of air conditioner from the perspective of technological innovation and occupant behavior, however, few research focused on such a question during the use of air conditioner in winter: should the air conditioner be turned off from an energy-saving perspective when leaving room for a short time? In order to explore an energy-saving usage mode of air conditioner, this research selected an office as the experimental site and simulated the scenarios of turning off the air conditioner or not for a short leaving. Through five types of experiments and statistical analysis such as Shapiro–Wilk and Mann–Whitney U test, the “balance point” of energy consumption was found. Further, the future intelligent management of energy consumption of HVAC system based on the concept of Agent-based modeling and Digital twin were prospected. The results refuted the popular saying on the Internet and provided guidance for public to objectively understand this issue. A novel concept that combines physical model and data-driven simulation provides a possible solution to improve energy efficiency in buildings.

Urban flood has emerged as a global problem due to rapid urbanization and climate change, resulting in significant economic losses. To address this issue, urban flood resilience (UFR) has been introduced as a concept to guide flood management strategies and reduce flood risks. However, most research in this field focuses on individual aspects of social, ecological, or technological components, overlooking the interrelationships among these components. This paper explores whether social-ecological-technological systematic (SETS) perspective can enhance UFR. Firstly, a bibliometric analysis was conducted to highlight the importance of linking social, ecological, and technological components in UFR research. The analysis revealed that the SETS perspective is closely connected to key terms such as governance, ecosystem, and green infrastructure, underscoring its relevance in UFR studies. Secondly, an analysis of the social, ecological, and technological components in urban flood resilience assessment was performed. The study found that these components are directly or indirectly associated with UFR indicators, emphasizing the need for a comprehensive SETS approach to evaluate and enhance flood resilience. Lastly, three case studies on flood risk management were examined to provide evidence of the effectiveness of SETS in enhancing UFR. These case studies demonstrated how the SETS perspective can contribute to conceptual and theoretical innovation in the field of urban flood resilience by capturing the evolving nature of SETS elements, assessing flood vulnerability, and integrating SETS with smart city plans. Overall, this paper highlights the importance of adopting a social-ecological-technological systematic perspective to enhance urban flood resilience. The findings suggest that considering cities and urban regions as dynamic SETS, and understanding the interactions among social, ecological, and technological components, can lead to more effective flood management strategies and ultimately reduce the damage caused by urban flooding.

Project portfolios (PPs) have various risks in the implementation process. Project portfolio risks (PPRs) management is of great importance in achieving project portfolio objectives. In this study, hyperparameter optimization of GA-BPNN model is carried out, and the optimized GA-BPNN model is used to assess the primary risks and secondary risks of PPs. The test results show that the optimized model had higher test accuracy, and the critical primary risks and critical secondary risks are identified through sensitivity analysis, so as to determine the primary risk response action and secondary risk response action. Effective PPRs assessment and response can help managers better deal with risks, and thus promote the smooth implementation of PPs and improve the efficiency of projects execution.

With the accelerated process of urbanization, most villages have been demolished and rebuilt, resulting in a large number of resettled communities. As a unique product of China's passive urbanization process, resettled communities are an important manifestation of urban spatial reconstruction. The spatial characteristics of these communities have undergone dramatic changes from rural areas to resettled communities, yet existing studies have not provided rigorous and structured identification of these factors. Therefore, in order to provide theoretical guidance for the spatial reconstruction of such communities, this paper proposes an analysis framework for the influencing factors of spatial reconstruction. Firstly, based on Lefebvre's trialectical space, a three-dimensional theoretical framework on the influencing factors of the spatial reconstruction of resettled communities is established. Secondly, based on this framework, 19 influencing factors of spatial reconstruction are extracted through literature review and content analysis. Finally, a governance analysis framework for the influencing factors of spatial reconstruction is established, and suggestions are made for the integration of landless peasants into the spaces of the resettled community. This paper provides insights for the research on the governance of resettled communities and the spatial reconstruction.

With the adjustment of the Chinese real estate industry, traditional real estate development enterprises are facing tremendous development pressure, which prompting them to engage in asset-light business. To effectively respond to changes in the external environment and maintain a competitive advantage in the face of adversity, enhancing organizational resilience has become a critical strategy for asset-light real estate development and management companies (AREDME). The purpose of this study is to explore the influencing factors of AREDME’s organizational resilience and analyze the mechanism of action among the factors. This study uses a literature review and expert interviews to identify 11 factors influencing organizational resilience in AREDME from three dimensions: environment, organization and individual. A hierarchical structure model of factors influencing organizational resilience in AREDME was constructed using the Interpretive Structural Modeling (ISM) method. The research results show that organizational resources, threat perception and organizational change belong to the direct factors; while organizational learning, organizational culture, organizational strategy, organizational commitment, communication mechanism, and external environment are indirect factors; and leadership and organizational structure are the fundamental factors. In addition, according to the results of Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC), the influencing factors are classified into three categories independent, autonomous and dependent factors based on their dependence and driving force. The research results contribute to the theoretical development of organizational resilience and have important reference value to improve the organizational resilience of AREDME.

As environmental consciousness and resource conservation become increasingly significant, the concept of sustainable development has taken center stage, leading to the rise of “zero waste cities.”. A prime example of these sustainable initiatives is the recycling of Construction and Demolition Waste (CDW) into environmentally friendly materials like recycled concrete and bricks. Despite the environmental benefits of CDW recycling materials being widely acknowledged in China, the overall recycling rate remains below 10%. A potential explanation for this discrepancy may lie in the implicit negative attitudes towards these materials. While CDW recycling materials are lauded for their eco-friendliness, such recognition has not translated into broad acceptance or use, indicating a possible underlying bias against these recycling materials. This study investigates implicit disgust attitudes towards CDW recycling materials, using college students as a proxy for the general public. Utilizing a Single Attribute Implicit Association Test (SA-IAT) and P200 component analysis in EEG experiment, implicit attitudes towards CDW recycling materials of three participants were captured. A subsequent questionnaire survey assessed explicit attitudes. The results indicate a significant implicit disgust towards CDW recycling materials, as evidenced by both a positive D-value in the SA-IAT and a larger P200 amplitude in response to CDW recycling materials words. This suggests a potential automatic attentional bias and negative emotional response triggered by these words, which may be part of a complex process leading to the observed implicit disgust attitudes towards CDW recycling materials. In contrast, the explicit questionnaire responses revealed less pronounced disgust, indicating a discrepancy between implicit and explicit attitudes. This research highlights the complexity of public attitudes towards recycled materials and underscores the value of multi-method approaches in capturing these attitudes. Furthermore, it suggests the need for strategies to counteract the implicit bias and enhance public acceptance of CDW recycling materials. The findings have implications for the promotion and sustainability of the CDW recycling industry.

Modular construction methods have demonstrated notable improvements in productivity and quality control compared with conventional methods. To further increase efficiency and flexibility in the construction industry, the concept of mass customization through a configurator has been adopted from the manufacturing industry. Previous efforts in configurators rely heavily on direct client involvement for configuration. However, clients’ inherent semantic gap and knowledge lacuna form a natural barrier to promoting configurator efficiency. Additionally, data deficiency and system maintenance hardship hinder the creation of a robust configurator. To ameliorate these gaps, this work proposes a conceptual paradigm of a natural language-based configurator with the help of ChatGPT, a state-of-the-art generative model. The configurator's primary strengths lie in its simplicity and generalizability, as it makes decisions based solely on natural language expressions provided by clients rather than on pre-defined options and components. To obtain an adequate amount of data for supervised learning, ChatGPT is utilized to generate vivid user requirements. Deep learning methods are then applied to characterize the relationship between user requirements and existing variants. As a practical implementation, a configurator for selecting suitable modular houses is developed. This research contributes to the field by offering a novel conceptual model design, realistic data collection, and model construction. The proposed paradigm illustrates its superiority and potential to facilitate decision-making while effectively fulfilling client needs, as demonstrated through a concrete experiment and visualization.

In China, the prefabricated building (PB) supply chain has developed rapidly and made outstanding contributions to expanding the market scale. Meanwhile, there are many obstacles to supply chain development due to a series of complex factors, which bring about high transaction costs (TCs). However, few scholars have focused on governance behaviors that have an essential impact on TCs. This study explores the critical governance behaviors that influence TCs in the PB supply chain. A theoretical framework of TCs has been developed based on an extensive literature study about supply chain obstacles. Then, according to the transaction cost theory, a governance framework that affects TCs has been created. Taking Anhui province as a case, this study conducted BP neural network analysis based on data from the questionnaire survey to identify the most significant governance behaviors. The main findings highlighted that the government and enterprises’ governance is equally important. Governance behaviors mainly affect TCs through the transaction dimension, among which the business years of enterprises to carry out prefabricated projects is the most critical. Another important finding is that the top three governance behaviors of the government that affect TCs all belong to the transaction environment. This study provides a comprehensive understanding of the governance behaviors affecting TCs in the supply chain, extending the knowledge of influencing factors at the top-level design level. It offers scientific and reliable recommendations for the government to improve efficiency and the enterprise in reducing TCs.

With the continuous advancement of urbanization process, the number of large-scale commercial complexes and other buildings continues to increase. As an important part of construction, the excavation of foundation pits also has increasingly high requirements for earthwork measurement and progress management. Currently, manual measurement is mainly used for earthwork excavation, utilizing total stations and Global Navigation Satellite System (GNSS), but this method has problems such as low accuracy, low work efficiency, and high costs. In this study, a scheme for earthwork calculation based on drone oblique photography and real-time management of earthwork excavation progress was proposed, which can improve the efficiency of terrain surveying and the accuracy of earthwork calculation, while enabling real-time monitoring without affecting construction. The experimental results demonstrate that the proposed method has good applicability for earthwork excavation calculation, and further research will be conducted in conjunction with other technologies to continue to improve calculation efficiency and accuracy.

Under the background of ecological civilization construction, China's river system governance has reached a new level. In recent years, Guangdong Province has combined past experiences in water management and proposed the construction of the Ecological Way. This paper introduces the development of the Ecological Way, sponge city, and greenway, and analyzes their similarities and differences from a theoretical perspective in terms of concepts and construction objectives. The functions of the Ecological Way were analyzed, and the planning and design of the Ecological Way were discussed. A discussion was conducted, and several suggestions were derived. The research findings indicate that the Ecological Way and sponge city share common construction objectives in terms of water resources, water security, and water environment. The Ecological Way and greenway also share similar functions in terms of ecology, economy, and culture. However, the concept of the Ecological Way is completely different from that of sponge city, as the Ecological Way also undertakes the construction objectives of water ecology, water economy, and water culture. While the Ecological Way and greenway have similar functions, the Ecological Way bears greater ecological responsibilities, and their respective responsibilities and authorities differ.

With the frequent occurrence of complexity challenges in implementing project-based activities and processes, more and more studies have been devoted to these issues by adopting a perspective of complex systems theory, as evidenced by the growth of related papers published in the past three decades. Complex construction systems (CCS) management has gradually become an emerging topic of concern in the field of construction project management. This study aims to investigate the current quo and trends of CCS research through a structured literature review. Twenty-four relevant articles published from 1990 to 2022 in three journals, the Project Management Journal, International Journal of Project Management, and IEEE Transactions on Engineering Management, were identified and analyzed in terms of the number of articles published annually, country/regional contributions, and research institutions. The analysis results indicate that CCS in the sector of infrastructure and construction has become an important case for research on complex project systems. In terms of country/region contribution, the United States, the United Kingdom, Australia, and China have enjoyed relatively more advantages in CCS research due to involving more publications. Four subtopics are then identified: system analysis and decision-making, system organization and planning, system development and design, and system information management. Regarding research design, CCS research favored the qualitative research design and used real large complex projects as a research base. Finally, conclusions and suggestions for CCS development in the future are provided.

The study presents an integrated framework for managing the carbon footprint of prefabricated buildings throughout their life cycle. The framework integrates ontology modeling, knowledge graph storage, and life cycle assessment (LCA) to analyze carbon footprint data. Sensor technology and natural language processing techniques are used for data collection and processing to build a knowledge graph for managing the carbon footprint of prefabricated buildings throughout their life cycle. The framework enables carbon footprint measurement, carbon source tracking, monitoring and feedback, and provides the basis for carbon reduction decisions. The study outlines the challenges in carbon footprint knowledge data management, including inconsistent definitions and methods, limited knowledge availability, and the complexity of carbon footprint data collection and processing. It also outlines the process of building carbon footprint ontology models, data processing, knowledge graph construction and storage, and carbon footprint measurement and carbon source tracking. The goal of the framework is to promote sustainable building practices and facilitate informed decision-making processes.

The development and application of BIM technology based on intelligent construction bring opportunities and challenges to the cultivation of discipline talents in architectural and civil engineering. The cultivation of BIM talents mainly depends on colleges and universities, so it is particularly important to carry out BIM teaching in colleges and universities. Architectural and civil engineering students are the main reserve force to promote and apply BIM in the future construction industry, so it will be of great practical significance to provide them with systematic BIM teaching and practice. Therefore, the purpose of this paper is to innovate the construction mode of virtual simulation training course for the whole process management of engineering project construction based on BIM with the integration of science and education, so as to improve students’ system application ability; BIM-based school-enterprise cooperation, BIM practice base construction; Cooperate with enterprises to carry out collaborative education and deepen the training mechanism of BIM applied talents; Promote learning by competition and teaching by competition, and improve the quality of BIM applied talents training. The integration of documentary evidence can improve students’ BIM application ability, constantly improve the talent training system and professional courses in colleges and universities in the practice of BIM technology, enhance college students’ innovative practice ability, and cultivate advanced compound and applied talents with solid modern advanced construction technology and engineering application ability in the future.

Retrofitting older equipment with the catalyzed diesel particle filter (CDPF) or Diesel Oxidation Catalyst (DOC) can reduce PM emissions from older equipment. Few studies have compared different retrofit technologies for cost-effectiveness. According to the database of the Hong Kong Environmental Protection Department (EPD), this study analyzes the cost-effectiveness of CDPF and DOC technologies in promoting PM emission reduction from construction equipment. The results show that the annual total PM emissions from the six types of construction equipment are 167,783.07 kg. CDPF technology is lower than DOC technology in terms of emission reduction and the unit cost of emission reduction, but the cost is also higher. It is suggested that the government should promote different retrofitting technologies to reduce PM emissions from older construction equipment in Hong Kong according to its own emission reduction targets and financial budgets. governments with different fiscal budgets can use these results as a reference.

Under the circumstance of the rapid deterioration of global climate, it is an important measure to promote the efficient transformation of contractors' green knowledge and pro-environment behavior to solve the problems of high energy consumption and carbon emission in the construction industry. Most of the existing studies have been conducted from the one-way perspective of the influence of green knowledge on pro-environment behavior, and it has been proved that the two have a strong correlation, but there is a gap in the exploration of their interactive relationship. In view of the spiraling characteristics of knowledge and behavior, this paper innovatively introduces the double helix of DNA to describe the relationship. Based on the relevant literature in recent 10 years, the elements of green knowledge chain and pro-environment behavior chain are visually identified and analyzed. This paper identifies five elements of the green knowledge chain including sustainable concept, knowledge management, knowledge diffusion, knowledge innovation and knowledge barriers, four elements of the pro-environment behavior chain including pro-environment attitude, influence factors, behavior types and environmental performance. The relationship between the elements on the double chain is further expounded to determine the analysis index for the double-chain coupling relationship measurement in further study.

Supplying citizen-centric smart city services (SCS) is a common strategy for countries to enhance urban sustainability in the volatile, uncertain, complex, ambiguous (VUCA) era. Citizen’s sense of gain in the Chinese context could perfectly interpret the connotation of citizen-centric. In order to formulate enhancement strategies for citizens’ sense of gain on smart city services (CSGSCS) in the VUCA era, this research set simulation hypotheses based on ECM, established the agent-based simulation model by Anylogic software, and validated the model by code check and agent state check. Nanjing city was taken as the case to carry out the scenario simulation to enhance CSGSCS. The results showed that the polarization would occur with the enhancement of CSGSCS, expectation confirmation was the most effective influencing factor to enhance CSGSCS, and high information spreading speed would reduce the total CSGSCS and exacerbate the polarization of CSGSCS. Finally, three implications were proposed, including caring for the vulnerable groups to bridge the digital divide, surveying citizens’ demands to guide SCS supply, and strengthening public opinion supervision to avoid the spread of negative information. This study not only improved the academic framework in the field of CSGSCS, but also provided practical guidance for the government to formulate the sustainable supply strategies of SCS.

Against the background of accelerated advancement of urbanization, the market scale of China's construction industry has been expanding. However, there are still problems such as labor force shortage, low production efficiency and frequent safety accidents, which hinder the development of the construction industry. The construction industry, as one of the important industries of national economy, has also started digital and intelligent technology innovation. Therefore, it is important to reduce the reliance on manual labor, reduce the occurrence of safety accidents and further improve productivity. In this study, EEG data were collected through motor imagery experiments. Based on CNN algorithm to extract, identify and classify EEG signals, the user's operational intention is transformed into a digital signal that can be recognized by the computer. The virtual experiment platform is built based on Unity, and the computer signals that can express the user's intention are transmitted to the virtual experiment platform by means of network communication, and then the movement of the manipulator is controlled to complete the scaffolding dismantling task under the virtual scene in a human–robot collaborative way. The method proposed in this paper can be applied in other scenarios in the engineering field as well, with a certain degree of scalability. In addition, the platform can also be used as a training system and practice platform. Workers can immerse themselves in virtual operation scenarios in order to more skillfully use the human–robot interaction system to complete construction tasks and achieve the purpose of technical handouts.

In response to increasing demands for precision and efficiency in the construction industry, intelligent compliance checking has emerged as a promising avenue for innovation. This review explores the evolution, application, and potential of intelligent compliance checking within the context of Building Information Modeling (BIM). This study commences by outlining the use of traditional compliance checking methods in the construction industry, emphasizing their limitations in handling the complexities of regulatory requirements and their inefficiency in large-scale applications. This sets the stage for the introduction of intelligent compliance checking as a more sophisticated, efficient, and accurate solution. The current state of BIM-based compliance checking research is delved into in this review, tracing the evolution from automated compliance checking to intelligent compliance checking. This exploration illustrates the advantages and transformative potential of intelligent compliance checking. Despite its promise, challenges that hinder its widespread adoption, such as data incompleteness, issues of interpretability, and the need for consistent reliability, are acknowledged. An in-depth analysis of these bottlenecks is presented, and future directions are proposed to stimulate further development and refinement of intelligent compliance checking technology. In conclusion, it is argued that while intelligent compliance checking presents an array of benefits such as improved efficiency, reduced error rates, and enhanced work quality, its full potential can only be realized by addressing the aforementioned challenges. This paper offers a comprehensive review for researchers and practitioners, aiming to contribute to the discourse on BIM-based intelligent compliance checking and its role in shaping the future of the construction industry.

In engineering education, traditional cognitive internships pose safety risks and limitations in terms of time, location, high costs, and resource requirements, which can affect students' learning experience and practical application abilities. Therefore, there is a need to explore innovative teaching methods to overcome these limitations. In light of the emerging technology of virtual reality (VR), this study proposes a VR-based cognitive internship teaching method aimed at reducing the dangers associated with field internships and saving teaching resources and costs. The method involves scanning and generating digital models using panoramic cameras, importing these models into a game engine for further development through script writing. Additionally, the Bloom's taxonomy is proposed as an evaluation method to assess students' cognitive development. An experiment was conducted involving 40 students who were surveyed through questionnaires. The results indicate that students have a positive attitude towards incorporating VR technology into cognitive internships for learning purposes. This demonstrates that integrating virtual reality technology into engineering education is a highly promising approach.

A BIM project often encounters many major and minor BIM versions throughout the project’s lifecycle. The main reason is expansive and inevitable changes in the conduction of lengthy, complex, and uncertainty-rich construction project. Recently, a semantic differential transaction (SDT) approach was proposed to minimize the data redundancy in the versions for blockchains, by extracting the semantically essential design changes between open BIM versioning. However, the current IFC schema cannot store, verify, or utilize such changes. This paper presents an IFC schema extension of a blockchain-ready change history for BIM versioning. The proposed extension supports both local storage of the changes and remote blockchain verifications. Firstly, the extended IFC entities define a mapping from the SDT model to the up-to-date IFC schema. Then, the new entities are managed and structured by a Java-based compiler-compiler approach; Meanwhile, the extended BIM files are verifiable using the official IFC verification tools. Finally, a pilot study is conducted to validate the technological feasibility of the proposed IFC schema extension.

Due to the possibility of data tampering in a centralized paradigm, current cost information management platforms are concerned with data accountability (i.e., data integrity and traceability). Blockchain is a promising technology that offers decentralized data storage with immutable and traceable data. However, the blockchain's transparency contradicts the cost data's requirement of confidentiality. In order to maintain accountability and confidentiality of construction cost information, this study provides a new approach based on blockchain and proxy re-encryption (PRE), including a cost data model for blockchain recording and a PRE-based method for cost data access management among project members. An illustrative scenario is used to show the feasibility of the proposed approach in securing construction cost information.

Modular integrated construction (MiC) has been increasingly adopted for buildings. Different MiC systems engage different levels of modularization (LoM), which however has not been explored in the literature. This paper aims to develop a novel index, modular integrated rate (MIR), to quantitatively measure the LoM of modular buildings. MIR is a composition of modularization rate (MR) which indicates the modularized area of a floor plan and integration rate (IR) which specifies the integration of architectural, structural, and building services works into modules. Using a real-life MiC building project, MIR is demonstrated effective in comprehensively reflecting LoM. The study also indicates a strong impact of LoM on construction efficiency, e.g. a strong negative correlation of MIR with construction time. Notably, the case scenario with the highest MIR (71%) could reduce the floor cycle to less than 4 days. The paper contributes a novel approach to quantitatively measuring building modularization, which should facilitate a step change in MiC system design, evaluation and selection for enhanced efficiency.

This article proposes a method to measure occupants’ activities-generated carbon emissions in healthcare facilities using deep learning. The method employs a Restricted Boltzmann Machine (RBM) and a Deep Belief Network (DBN) within the SGAM framework to extract useful features from high-dimensional data and generate predictive and evaluation models. A motivating case in a hospital in Tianjin, China is used to demonstrate the necessity of measuring occupants’ activities, which involves the development of an information integration system with collection, monitoring, operation, and control functionalities. The platform collects data from IoT devices and O&M platforms to form a database, which is updated hourly. The evaluation model is used to determine whether the model needs to be updated. The proposed method provides a way to monitor building operations and control strategies to reduce carbon emissions.

Urban regeneration is a powerful measure to promote the sustainable development of cities. Effective urban renewal actions would improve the efficiency of land resource utilization, optimize community functions, improve the living environment, protect the inheritance of historical culture. Guangzhou carried out the regeneration of historical and cultural districts after more than ten years of urban renewal exploration from 2009. Urban regeneration has changed the original living environment and daily life of residents. How about the change of residents’ perception of the reconstructed living environment? To answer this problem, based on existing research and correlational studies, such as the theory of place, behavioral geography and stakeholder theory, this paper selects 2 typical historical and cultural districts in Guangzhou as the cases to conduct field research and issue questionnaires. A total of 121 valid questionnaires were collected to explore the change of residents’ sense of place in historical and cultural districts under the background of urban regeneration.

This paper presents a Geographic Information Systems (GIS) based framework of fire risk assessment and emergency route planning for heritage buildings in historic centers. The framework addresses the specific challenges faced by heritage buildings, such as fragility, insufficient risk awareness, and inadequate emergency response. In the framework, potential risk factors are first identified and categorized through a literature review, forming a fundamental instrument for assessing the vulnerability of heritage buildings. Next, GIS technology is adopted to visualize the risk levels on spatial maps. Open-source route data is further collected and merged into a virtual prototyping engine to develop an emergency route simulation application. Specifically, a city-level navigable network is constructed to calculate the possible emergency routes between heritage buildings and fire stations using Dijkstra’s algorithm. The proposed framework is carried out with a case study of two heritage buildings (i.e., the Ruins of St. Paul’s and Kuan Tai Temple) in Macao, which showcases that the framework can indicate quantitative fire risk levels and optimized routes to enhance fire safety and heritage conservation.

Excavation slope protection and reasonable slope cutting are crucial for construction safety in foundation pits. Currently, manual on-site inspections are frequently used to check whether slope protection and slope cutting (SPSC) meet safety requirements. However, due to the dynamic changes in the excavation face of the foundation pit, human negligence, or delayed detection, it is challenging to ensure that SPSC are executed according to requirements, which poses a safety accident to construction. To address this issue, a method for identifying SPSC in foundation pit construction based on deep learning and binocular vision is proposed. Firstly, the YOLOv7 target detection model is used to recognize slope protection facilities and determine the slope area range based on the video monitoring images at the job construction site. Then, using the binocular stereo vision method, the 3D spatial coordinates of each pixel in the slope area range in the images are obtained. Finally, the adjacent constraint detection method is used to optimize the point cloud data of the foundation pit slope, fit the slope surface and calculate the slope angle, which is used to determine whether the slope protection facilities are in compliance and whether the slope cutting meets specification requirements. A case analysis result shows that the recognition accuracy of the foundation pit slope and protection facilities calculated by this method is 0.93, the measurement error of the slope angle is 0.64°, and the relative error is 1.51%. It meets the practical accuracy requirements and provides an automated intelligent means for safety management and control of foundation pit construction.

Construction robots could offer great opportunities to address various issues facing the construction industry and catalyze its transformation. However, their widespread adoption in building construction remains limited. This paper aims to investigate the current application status and challenges of robotics in building construction in China. The research was carried out through a critical review, interviews and a questionnaire survey with 140 relevant practitioners in China's construction industry. The two most common usage scenarios were identified as the use of robots for concrete construction and drones for construction sites. Ten critical challenges were identified, and the most critical ones perceived by industry practitioners were immaturity of business models of construction robots, uncertainty of economic benefits and lack of inter-disciplinary talents in both construction and automation. The potential impact of construction robots on unemployment rate and the general single-task function of construction robots were not considered as significant challenges. Drawing on the findings, this study puts forth several recommendations for users, developers, and policymakers of construction robots to facilitate the successful applications of robotics in building construction.

The extreme climate change is universally recognized as one of the most exigent challenges currently confronting the world. Carbon neutrality is a key strategy to help mitigate the effects of climate change while maintaining sustained economic growth. Many countries have established long-term goals for achieving carbon neutrality, and proposed a variety of related concepts, such as carbon neutrality and climatic neutrality. This paper effectively combines these ideas and summarizes the main ways to achieve these goals, and analyzes the differences in carbon neutrality goal timelines among various countries. Finally, this paper explores the key factors that contribute to these differences in order to alleviate the negative impacts of global climate change.

County is a key spatial unit for achieving emission reduction and carbon control. Studying and revealing the spatiotemporal evolution characteristics of carbon emissions in counties is of great significance for achieving the “dual carbon” goal. This article is based on carbon emission data from 147 districts and counties in the Chengdu Chongqing region from 2007 to 2017, and uses coefficient of variation and spatial autocorrelation analysis methods to study the spatiotemporal pattern of carbon emissions in counties in the Chengdu Chongqing region. The results show that: (1) the total carbon emissions of each district and county in the Chengdu Chongqing region show a rapid growth to a stable trend; (2) The coefficient of variation indicates that the gap between counties in the Chengdu Chongqing region is gradually narrowing; (3) County carbon emissions have significant spatial correlation, and the Moran's I index of global county carbon emissions shows a fluctuating upward trend. The Chengdu Chongqing region as a whole shows a relatively obvious phenomenon of hierarchical agglomeration, and the county level carbon emissions have a high spatial dependence pattern in local areas. Most high (low) carbon emitting counties are adjacent to high (low) carbon emitting counties, but the spatial distribution of carbon emissions has not changed significantly. This study to some extent reveals the evolutionary characteristics of carbon emissions in counties in the Chengdu Chongqing region, which helps to optimize the spatial implementation path of the “dual carbon” goal in the Chengdu Chongqing region.

Super high-rise buildings are characterized by high density of occupants and long vertical evacuation distance in staircases, which can easily cause congestion and lead to fatigue. Therefore, without effective evacuation guidance, crowding and trampling accidents are likely to occur in the vertical evacuation. Recently, intelligent guidance technology combining real-time monitoring on fire spread and personnel evacuation has become a research hotspot in the field of building safety evacuation. However, the focus of current research is on low-rise public buildings where horizontal evacuation is the main concern, and the existing intelligent guidance technology can hardly meet the demand for vertical evacuation in super high-rise buildings. This paper proposes five principles and a framework for the development of intelligent evacuation guidance systems in super high-rise buildings based on the digital twin technology. The system framework contains five dimensions: physical entities, virtual model, digital-twin data, optimization algorithm and application services, and can dynamically generate evacuation guidance paths for occupants based on the real-time situation of fire spread and people evacuation.

Smart cities, resilient cities and sustainable cities are three city models that are currently the focus of academia and society. Smart cities focus on technology and science, resilient cities emphasize resilience, and sustainable cities represent the future of a city. There is a relatively large amount of urban research on the three domains. Still, there is a gap in the academic community regarding the common influence of the three urban attributes on the city and the relationship between the three urban attributes. While multiple attributes and functions exist in a city, this study uses NVivo software for qualitative analysis and Vensim, a system dynamics software, for causal loop diagram (CLD) demonstration to reorganize these three urban models. Thus, this study fills a gap in the current field by integrating these three city models and creating a new triangular stable city model through CLD. Overall, the results of this study propose a new triangular stable city model and use systems thinking to provide insights for urban planning and construction.

The implementation of urban renewal and reconstruction requires a heightened focus on the harmonious coexistence between humans and the city, humans and buildings, as well as humans and spaces. In this paper, we conducted manual and Nvivo coding to analyze the specific nature of source materials, which included field research, academic literature, urban renewal and reconstruction technology implementation guidelines, work implementation opinions and other related policy implementation supporting documents, in order to learn and enhance residents’ satisfaction more comprehensively. In result, 8 first-level evaluation indicators were identified, including public service facilities, public infrastructures, building noumenon, social and cultural environment, community space and environment, community governance and property management, project implementation process and requirement of the effect after renewal, as well as 42 second-level evaluation indicators. A comprehensive and multi-level index system of resident satisfaction in urban renewal and reconstruction has been established based on these indicators. It provides reference standards for the continuous enhancing of resident satisfaction.

Predicting hazard identification performance (HIP) is important in improving engineering project management. However, prediction based on unimodal data may overlook certain important information. Therefore, this study aims to predict HIP using multimodal data. After an experimental study, we found that (1) the prediction results of hazard identification change over time and the optimal time segments for predicting the identification performance of different hazard types are different; (2) the identification of electrical-related and fire-related hazards requires more attentional resources and longer time than fall-related hazards; (3) the physiological mechanism of hazard identification is that the occipital lobe of the brain processes visual information first, while the occipital lobe of the brain transmits visual information later. This study further explores the basic principles of hazard identification and provides a reference for improving safety management in engineering projects.

Cost overruns are a previlent problem in highway construction projects. Principal contractors normally add cost risk allowances to tender prices to reduce cost overruns. However, the estimate of cost risk allowances mostly depends on their estimators’ judgements, which are criticised as subjective and imprecise. Therefore, this research aims to investigate how estimators judge cost risk allowances to improve their judgement-making process. A critical literature review was first conducted to understand the process of expert judgement from three aspects: the underlying cognitive mechanisms, the sources of bias involved, and the forms of knowledge mobilised. Then, through conducting interviews with twelve estimators working for principal contractors in the UK construction industry, the cognitive process of how estimators judge cost risk allowances for highway construction projects was analyzed. The findings suggest that estimators’ judgement process involves an interplay of Type 1 and Type 2 processing, which are mainly underlay by the cognitive mechanisms namely attention, encoding, pattern recognition, working memory, and long-term memory. In addition, eleven sources of bias affecting estimators when judging cost risk allowances were identified, among which estimators’ previous project experiences, risk attitudes, and assumption of the controllability of risks were the most significant ones. The research also finds that techne and phronesis play primary roles in estimators’ judgements, while episteme plays a negligible role. The research findings enable contractors to better understand the judgements made by their estimators, in order to enhance their reliability and thus reduce cost overruns.

Blockchain technology has been touted as a technology that has the ability to change the built environment, particularly in the area of logistical management. This is as construction logistics is being regarded as very complex process. Also, several studies have highlighted the importance of blockchain technology in logistics management in the built environment. However, none of these studies have considered the rationale behind the adoption of the technology in logistics management within the built environment sector, particularly within the South African context. Hence, this study seeks to examine the rationale behind the technology adoption in construction project logistics in South Africa. A Survey design was adopted. A total number of one hundred and twenty-five (125) questionnaires were administered to construction professionals in Guateng Province, SA and ninety-eight (98) were returned and found suitable for analysis. Collected data was analyzed using percentage, mean item score, standard deviation, one sample t-test, and Kruskal–Wallis. The findings reveal the factors influencing the adoption of this solution in the management of construction logistics in SA which are inter-organizational trust, security, transportation cost, business process, customer/client service, data transparency, compatibility, technology strategy and implementation, organizational size, relational governance, organizational readiness, amongst others. Based on the study’s findings, it is obvious blockchain technology is expected to transform corporate operations and help construction logistics achieve client expectations for accuracy, clarity, and promptness by creating an ethical working environment to win clients’ trust. Hence, the study recommends the consideration and inclusion of these influencing factors for blockchain adoption in construction logistics strategy while putting the technology into practice.

Delays in construction projects are identified as a worldwide issue as it has been addressed in many recent empirical studies. This paper aims to investigate the causes and effects of these delays and analyse whether these delays can be mitigated with a well-managed strategy. This study utilises a qualitative approach by first, conducting a critical review of the literature to analyse the causes and effects of construction delays. The study then conducts in-depth interviews with construction professionals to verify the industry’s position on this topic and to make recommendations about the mitigating measures. The interviewees sample represents a cross-section of the industry and its project roles. The key findings of a total of 12 interviews highlighted that ‘financial related effects’ are the most crucial ones that requires close scrutiny. Among the parties involved in a construction project, ‘client’ is in the highest risk of being penalized for these financial implications of delays. Although the contractor is generally required to try to prevent or mitigate delay, even where the fault is not their own, clients have the highest level of responsibility in delay-related decision making. However, depending on the procurement route this can vary. In terms of the causes ‘communication, coordination and planning’ reports as the highest contributor for to delays. The effects of these are mostly lost profit claims and delay and disruption claims. However, Building information modelling possesses a potential to mitigate the latter and. Nonetheless, the interviewees also highlight the need for training and awareness in above digital technology over traditional management practices, to perceive desired value.

Green regeneration of industrial heritages is vital for the sustainable development of relevant urban areas. Although previous researchers have paid attention to the assessment of sustainable regeneration of industrial heritages as well as the critical success factors, few of them have made thorough analysis on the variety of ecological approaches that contribute to the green regeneration of industrial heritage, which makes the fulfillment of green regeneration of industrial heritages inefficient. To bridge this research gap, this study aims to make a detailed exploration on the variety of the ecological approaches adopted by industrial heritage regeneration projects and propose a scheme for industrial heritage regeneration projects to achieve green regeneration. Based on case data collected from four industrial heritage regeneration projects which are outstanding in their green performance, this study analyzed the regeneration principles and measures adopted in these projects in the aspects of green building technology and ecological design. Finally, a scheme on green regeneration of the industrial heritages has been proposed, including three specific measures about building technology, which are (1) electricity saving, (2) water saving, (3) structural design, and 4 means about the ecological restoration, which are (1) water pollution treatment, (2) soil pollution treatment, (3) planting and (4) recycle of wasted materials and reuse of abandoned places. This study could provide practitioners with a detailed scheme to achieve the green regeneration of industrial heritage in a more targeted manner, so as to achieve sustainable urban regeneration more efficiently.

The construction industry is essential in the development of the economy of any country. The performance of the built environment must be in a good state to ensure a higher contribution to the country's economy. However, Unmanned Aerial Vehicles being one of the innovative and emerging technologies have barriers that hinder the implementation of this technology within the construction industry. Consequently, this study aimed to identify and explore the barriers hindering the application of Unmanned Aerial Vehicles in the construction industry. This study adopted a quantitative approach and evaluated the construction professionals via well-structured questionnaires which were distributed on online platforms. The questionnaire was analyzed using Statistical Package for Social Science and Principal Analysis Component, t-test, and a mean score was used to present the data analysis. The results indicate that barriers hindering the application of Unmanned Aerial Vehicles include potential legal issues, and possible hijacks by criminals, to mention a few. It is believed that this study will assess the barriers to this technology and add to the body of knowledge by highlighting the importance of Unmanned Aerial Vehicles within the built environment. In addition, it is believed that this study will recommend ways to improve the adoption rate of Unmanned Aerial Vehicles in the construction industry.

A new round of technological revolution and industrial transformation is sweeping the world, and urban industrial transformation and upgrading are becoming increasingly important to every city. As a new type of policy tool, government venture capital (GVC) has received high attention from governments and is used to promote the transformation and upgrading of urban industries. Based on the theoretical discussions on the impacts and mechanisms of GVC on urban industrial transformation and upgrading, an empirical study with the evidence of a panel data of 277 cities in China from 2016 to 2020 is conducted. It is found that a significant positive correlation between GVC and the advanced level of urban industries and the rationality of urban industrial structure. Further analysis shows GVC can better promote urban transformation and upgrading at the advanced regions, while GVC can significantly improve the advanced level of industrial structure in the central regions, and improve the rationality of industrial structure in the western regions. Corresponding suggestions are given to the local governments to accelerate urban industrial transformation and upgrading through GVC.

China is highly susceptible to natural disasters, which cause widespread residential displacement and have long-lasting consequences on affected communities, emphasizing the importance of effective post-disaster housing reconstruction. This study aims to explore the barriers affecting post-disaster housing reconstruction in China. The research objectives include: (1) establishing a framework for the reconstruction process, (2) identifying and analyzing the barriers through a comprehensive literature review, and (3) categorizing the barriers based on the reconstruction framework. The workflow of post-disaster housing reconstruction is divided into five stages: disaster prevention and mitigation in pre-disaster areas, housing damage assessment, post-disaster housing reconstruction planning, coordination, and implementation. This research contributes to a more comprehensive understanding of the factors influencing post-disaster housing reconstruction in China, ultimately promoting resilience and sustainable development in affected areas. The findings will assist decision-makers in identifying key barriers and success factors that significantly influence the recovery process, thereby improving the overall efficiency and effectiveness of post-disaster housing reconstruction efforts in China.

In recent years, with the continuous advancement of artificial intelligence technology in the field of construction engineering, the use of computer vision methods to address issues in engineering management scenarios has become a major research focus. However, due to the complex environmental factors present in construction sites, the application of computer vision technology in this context is often affected to varying degrees. In this paper, we focus on common images in construction scenes that are affected by dust and exposure, which often have low target resolution, blur, and abnormal lighting distribution. Taking the task of license plate recognition for construction vehicles as an example, we propose a method based on the ESRGAN image super-resolution algorithm to improve the quality of license plate images and ultimately enhance license plate text recognition accuracy. We constructed a mixed dataset through on-site shooting and code synthesis for training the super-resolution model, and tested the model on a self-built license plate image test set. The accuracy of license plate text recognition in the verification experiment using the super-resolved license plate images reached 74.5%, which was a significant improvement compared to the 65% accuracy achieved with the original images. The test results show that the model can effectively improve the resolution of license plate images while addressing issues of blurriness and abnormal lighting distribution to some extent. The proposed method in this paper has a positive effect on downstream research tasks in engineering management based on computer vision and has significant research implications for enhancing the quality and efficiency of engineering management.

The construction industry is commonly recognized as one of the most significant contributors to global carbon emissions. Apart from carbon emissions from energy consumption during the building operation stage, embodied carbon, including construction materials and construction activities in the building construction stage, becomes more and more important for life cycle carbon reduction in the construction industry. However, the process of embodied carbon quantification is tedious and error-prone due to the difficulty of collecting massive carbon data. Although Building Information Modelling (BIM) has been applied in this field to extract material information for carbon quantification, the existing studies are still limited in building the semantic domain information related to embodied carbon. Therefore, ontology tools are used and integrated with BIM in this study to create a comprehensive approach in the field of embodied carbon. An ontology-based data model is proposed first to identify information requirements for embodied carbon quantification. After that, this study developed a BIM-based tool to (1) enrich BIM information, (2) map data attributes in the ontology data model to BIM models, and (3) automatically calculate embodied carbon results. A typical building from a construction project is used to validate the proposed approach, which illustrates both feasibility and calculation performance.

This paper proposes an improved CPP system based on building information modeling (BIM) and robotic configurations for indoor robots. Firstly, BIM is semantically enriched for CPP tasks, based on which semantic trapezoidal grid maps are constructed with accurate information. Secondly, a new concept called “Coverage Bonus” is integrated into the coverage pattern analysis, which can be applied in the scenario that robot’s coverage size is different from its body size. Finally, the coverage sequence is optimized by solving Cluster Generalized Traveling Salesman Problem (CGTSP), and the generated paths can observe the travel rules and obtain high efficiency. The CPP system is validated in a representative university building floor and a typical anti-epidemic robot. It is demonstrated that the BIM-based TGM generation method can improve mapping quality compared to Classical methods. In addition, the improved CPP algorithm and one of its ablation studies achieve the best and similar coverage performance (e.g., 97% coverage ratio). However, the improved CPP algorithm outperforms this ablation study in satisfying the coverage rules strictly.

The carbon emissions generated by energy consumption on the demand-side accounting for approximately 85% of the total global carbon emissions, which is essential in addressing global climate change. To achieve carbon reduction on the demand-side, the Carbon Generalized System of Preferences (CGSP) was proposed to utilize market mechanisms to encourage voluntary participation by residents in carbon reduction. This paper uses panel data from 2010 to 2020 to explore the effectiveness of the CGSP in reducing urban residents’ carbon emissions under different scenarios based on the difference-in-differences (DID) method. The results indicate that: (1) The CGSP in pilot cities has a significant carbon reduction effect, mainly relying on reducing electricity consumption, whereas spontaneously initiated cities have no significant effect. (2) The carbon reduction effect of residents in pilot cities show a positive and significant coorelation with the population agglomeration degree, urban scale and economic volume, but no significant relationship with residents’ income and industrial structure. These findings provide an important basis for the promotion and further development of CGSP.

This paper explores the application of automated machine learning (AutoML) techniques to the construction industry, a sector vital to the global economy. Traditional ML model construction methods were complex, time-consuming, reliant on data science expertise, and expensive. AutoML shows the potential to automate many tasks in ML construction and to create outperformed ML models. This paper aims to verify the feasibility of applying AutoML to industrial datasets for the smart construction domain, with a specific case study demonstrating its effectiveness. Two data challenges that were unique to industrial construction datasets are focused on, in addition to the normal steps of dataset preparation, model training, and evaluation. A real-world application case of construction project type prediction is provided to illustrate the accessibility of AutoML. By leveraging AutoML, construction professionals without data science expertise can now utilize software to process industrial data into ML models that assist in project management. The findings in this paper may bridge the gap between data-intensive smart construction practices and the emerging field of AutoML, encouraging its adoption for improved decision-making, project outcomes, and efficiency.

In the smart construction era, Heritage Digital Twin (HDT) is increasingly created as the digital replica of physical heritage buildings and relics. Extraction of the unique patterns and decorative elements on the HDTs is not only of academic interest to heritage conservation but also of business interest to fashion and design, such as the recent Hanfu fever. However, the patterns’ complex curvature surfaces and subtle protrusions make it challenging to extract and analyze them accurately and efficiently. This paper presents a Genetic Algorithm-based semi-automatic method for extracting decorative pattern texture from HDTs. This method has three steps: (i) extraction of cross-section contour as Non-uniform rational B-spline (NURBS) curves; (ii) Fitting of arcs and curvature projection based on Genetic Algorithm (GA); and (iii) clustering and extraction of patterns of interest. We tested the method on 3D data of a heritage building and a heritage bronze drum preliminarily. The high accuracy of the results, i.e., F1-value > 90% in all tasks, validated our automated extraction method for detailed patterns and decorations. The proposed GA-based method can enrich the literature of HDT in smart heritage and smart construction, whereas the extracted heritage’s patterns and decorations have the potential for cultural and business applications.

The construction industry holds a substantial responsibility for its environmental impact and has been subject to increasing regulatory pressures. Environmental, Social, and Governance (ESG) considerations have gained prominence as a framework for assessing sustainability risks and crucial opportunities in business operations. However, ESG research in the construction sector is still in its early stages. This paper aims to provide a comprehensive overview of recent research trends of ESG in the construction industry. An analysis of current research trends reveals several emerging areas within the ESG framework of the construction sector. First, there is a growing emphasis on environmental factors such as reducing carbon emissions, implementing green building practices, and adopting sustainable materials and construction methods. Secondly, there is an increasing focus on the social dimension of ESG, with a focus on promoting worker welfare, ensuring fair labor practices, and promoting diversity and inclusion in the construction workforce. Thirdly, governance practices in the construction industry are being reviewed, with a focus on transparent decision-making processes, ethical behaviour, and effective stakeholder engagement. By supplementing these recent trends with drivers, challenges, and benefits, the findings in this paper can provide holistic insights of the promising ESG integration to industry practitioners, policymakers, and researchers to understand and promote sustainable and responsible ESG practices in the construction industry.