Advancements in Architectural, Engineering, and Construction Research and Practice

Technology has become an integral part of our daily routines and has permeated every aspect of our lives. The advancements in digital technologies have brought about significant changes to people’s lifestyles and work practices. Urban planners face unique challenges in managing cities, including population growth, climate change, infrastructure, and urban expansion. Understanding the impact of digitalization on urban environments is crucial for developing effective urban planning strategies. In today’s rapidly moving society, we often face an excessive inundation of information. The implementation of technology in cities across the globe is becoming increasingly common. This advancement aims to increase the eminence of life for people. These innovative solutions range from energy-efficient buildings and transportation systems to advanced waste management and public safety measures. At the same time, industries are undergoing significant digital transformations that are revolutionizing operating, communicating, and interacting with their customers. Digital tech improves business effectiveness, efficiency, and innovation for quick market adaptation. Digital tech can enhance business efficiency, productivity, and innovation, enabling them to adapt quickly to market changes. The construction industry has embraced digitization, showing resilience during tough times. Digital platforms are in demand to thrive in competitive environments. AR/VR tools have revolutionized urban planning by improving communication and finding more effective solutions for city challenges. A challenge for architects is convincing clients that virtual views mirror the final design. The digital revolution has had an impact, especially in the construction industry which is rapidly becoming more digital. This research aims to understand and investigate the potential applications of interactive digital platforms and software for architecture and urban planning.

Conservation issues in architectural heritage require negotiating complex data encompassing geographical, architectural, social, and economic aspects of a historic urban area. Various types of advanced digital technologies can be used for such detailed work. The purpose of this study is to demonstrate the utility of these techniques in the re-discovery of the original architectural character of a mostly collapsed sixteenth-century Ottoman caravanserai, Ali Paşa Caravanserai, which was constructed under the supervision of Mimar Sinan (the major Ottomans’ Major Architect) within the Historic Trade Center of Bursa as one of the World Heritage Sites (WHS) in Türkiye. Meanwhile, it also aims to represent the changes in its form and function with the urban transformation activities that appeared in the city center since the end of the nineteenth century. For this purpose, the prevalent use of digital techniques to preserve a hardly damaged architectural heritage is described initially. Following brief information about the spatial character of the study area, including this historic monumental building, the method prepared in different phases of its rehabilitation project is designated by using digital tools to document its original structure and evaluate the physical changes in parallel with manmade deformations. At the end of the study, the necessity of digital technologies to express the authenticity of architectural heritage is discussed, while using them in finding solutions for the preservation and perception of its surrounding traditional urban texture.

The continuous progress in technology is leading to the widespread existence of data streams with high dimensions. Identifying outliers in this particular scenario presents a notably difficult task. The unique characteristics of data streams, combined with the effect of the dimensionality curse in high-dimensional space, create constrained mining requirements, and a current challenge is to simultaneously address them. A common approach to handle high dimensionality is to identify outliers only within subspaces of space of features that contain interesting knowledge, where outliers are typically found. However, in the realm of data streams, this area of study has not been well explored. In this article, our objective is to discover interesting subspaces for outlier detection while accommodating the needs of data streams, including limited time and memory, and addressing the adaptation to data changes (concept drift), as well as providing better performance than the closely related approaches. In this context, we used a metaheuristic-based approach (Adapted Binary Gravitational Search algorithm) to discover high-contrast subspaces comprised of independent features, within which the outlier detection will be performed. To deal with data streams, we adopted the sliding window structure together with a modified version of the N-Dimensional Kolmogorov–Smirnov WindoWin (NDKSWIN) concept drift detector. We conducted experiments on both synthetic and real-world data and the results demonstrated its effectiveness and superiority over the competitors.

Environmental resilience is intrinsically tied to the conservation and promotion of biodiversity at multiple scales, spanning from local ecosystems to the global biosphere. Biodiversity assumes a pivotal role in the capacity of ecosystems to endure and recuperate from diverse perturbations. Human-induced stressors are causing unprecedented losses to biodiversity. Preventing and reversing the global biodiversity crisis necessitates targeted conservation endeavors, yet monitoring efforts are expensive, and conservation resources are limited. This lack of information on biodiversity statuses and trends may obscure population declines and potential extinctions. As a result, there is a pressing need for cost-effective and scalable solutions to monitor biodiversity. Here, we carried out a systematic literature review focusing on the use of artificial intelligence (AI) methods to assess social media data for biodiversity and conservation, identifying 32 articles. Our review focused on capturing which AI approaches were used, and where relevant how studies used multiple AI methodologies for a multimodal approach. Our results highlight significant recent developments in computer vision, natural language programming, and spatial analysis, and discuss their exciting applications to big data from social media for biodiversity monitoring, which hitherto have been underexplored. Social media uniquely allows for multimodal analysis offering a rich understanding of conservation issues by combining multiple data types, such as audio, video, and text. Compared to previous ecological research harnessing AI and social media, a multimodal approach offers additional insight relevant to biodiversity monitoring, including tracking the changes in timing and distribution patterns of biodiversity events and identifying areas affected by invasive species. By harnessing the capabilities of computer vision, natural language processing, and spatial–temporal analysis, we can unlock valuable insights from social media posts and guide conservation strategies for enhancing environmental resilience in an efficient and scalable manner.

The pressing need to provide housing for a projected global population growth mandates the construction of 327 million new independent houses over the next three decades. Achieving this formidable task entails either a tenfold augmentation in the population of practicing architects or a tenfold acceleration of design and construction processes. Notably, the latter objective emerges as an attainable target when compared to the former. Nevertheless, the Architecture, Engineering, and Construction (AEC) industry grapples with an array of challenges emanating from the prevailing Architectural Design Process (ADP). These predicaments encompass substantial investments in software skill acquisition and maintenance, the absence of real-time feedback bridging the conceptual and construction phases, the dearth of real-time physics simulations, the challenges of translating architects' visions into on-site constructed projects, the absence of automation for repetitive tasks, the complexities associated with converting drawing typologies, to name a few. This research adopts a qualitative research paradigm, characterized by an exploratory investigation grounded in personal experiences, empirical observations, dialogues, and experimental data. The study introduces a Smart System poised to redefine the ADP by integrating Artificial Intelligence (AI) comprehensively. This innovative system aspires to be both foundational and open, fostering continuous development and improvement. To facilitate this vision, the realm of Building Information Modeling (BIM) and the Industry Foundation Classes (IFC) file format are extended, capitalizing on the Python programming language. The process of prototyping and experimentation unfolds within the open-source 3D software Blender, exemplifying the viability of an open system. It is crucial to underscore that the research places a distinct emphasis on the refinement of extant AI models, prioritizing this avenue over the creation of new models. This preference is underscored by the formidable time and computational resources requisite for training novel AI models, constituting a central constraint within the scope of this inquiry.

Our work builds upon general-purpose sketch recognition research, proposing a framework to integrate sketches within architectural design software. The use case being freehand-drawn floor plans as input for the Building Information Modelling environment. For that, we developed the SketchPLAN process, which recognizes, vectorizes, and contextualizes floor plan sketches. For recognition, SketchPLAN leverages the power of conditional Generative Adversarial Networks (pix2pix) to generate semantic segmentation maps out of floor plan sketch inputs, after training it with our own annotated dataset. The next step consists of vectorizing the segmented raster output through a floor plan vectorization library. A graphical user interface was designed to streamline the process. Bringing its output into Revit environment goes through the pipeline of “Rhino.Inside”, converting geometry objects into Revit type instances. SketchPLAN demonstrates how artificial intelligence tools can be harnessed to bring floor plan sketches as input into BIM environments. In terms of contribution, we have collected and annotated an image dataset of floor plan sketches. The annotation system was thought right from the start as a means to vectorize those images. Moreover, our developed floor plan vectorization library performed well in converting those segmentation maps into usable geometry. The proposed solution shows nevertheless some deficiencies: struggling to omit all background noise when recognizing, and not considering some special cases when vectorizing. SketchPLAN holds however a bigger potential, considering the possible improvements on the process in terms of data collection, model architecture, more comprehensive vectorization, as well as implementation in other scenarios such as design recommendation tools.

Since recent years till now, a lot of research has been conducted on generative design to help architects in the design process to save time and cost, and produce multiple and better solutions for a given problem. Such solutions have been generating architectural plans based on users’ preferences, generating architectural plans that reduce the dependence on energy for cooling by improving natural ventilation, and generating façade systems that reduce harmful sun light and increase natural light. However, there are a few number of research studies that consider the quality of spatial configuration in terms of privacy hierarchy and the spatial relationship of the automatically generated architectural plans. Spatial configuration is one of the most important features in the architectural design process, and using generative techniques in relation to Machine Learning (ML) in this process has become a requirement in recent years. Although a lot of studies have been carried out about shape grammar and ML relationships, there is not a study which combines spatial configuration using Space Syntax (SS) with ML, which can create a potential for this requirement. Therefore, in this paper, a computational framework has been developed to evaluate the spatial configuration of the generated architectural plans by training a supervised neural network on some spatial feature values of three Syrian houses (post-independence from the French colonization period). These values have been gained by analyzing these houses using the DepthmapX software, which is based on Space Syntax theory. The trained model has been tested on another Syrian housing plan from the same typology. The outcomes of the study demonstrate the potential of the trained model to predict the suitable space function with few errors caused by the strong similarity in spatial features of some spaces and the lack of training samples. The trained model can then be integrated into any plan-generating algorithm or used as a separate tool to enable architects to enhance their spatial configuration in the early design stage. Although the trained model is still under development without accomplishment, it creates a base for further investigations in terms of spatial conditions and ML.

Promoting the use of strong AI tools in computational drug designing is a promising way to avoid early-stage failures of cancer drug discovery process. We build an inhibition targeted machine learning classifications, aiming to model the structure/activity relationships for PIM 1/2/3 protein kinases inhibitors, using different decision trees-based algorithms, starting from the data curation and analysis of previous experimental measurements. The therapeutic targets being studied are a family of serine/threonine protein kinases directly involved in various cellular processes, they have been implicated in cancer progression and identified as highly oncogenic. The constructed models showed Random Forest (RF) performances slightly better than XGBoost for the PIM 1 (+1% of difference in the accuracy scores), and XGBoost significant robustness for the PIM 2 and 3 datasets (+2% and + 4%, respectively), whereas the SVM algorithms were found to present a poor predictive ability from our datasets, either with a linear or a radial basis functional kernel. The benchmarking led to the selection of the strongest models: 85% of prediction accuracy for PIM 1 and PIM 2 datasets and 82% for the PIM 3 dataset. Data modeling along with technical methodology are discussed in details and the predictive strength of both RF and XGBoost algorithms on these data types is examined.

This study presents a comprehensive approach to enhance the energy performance of a multi-story residential apartment in a temperate region of Turkey. The strategy focuses on cost optimization in compliance with EPBD Directives and CEN EN 15459-1:2017 standard. It addresses energy consumption and environmental impact associated with glazed building components and openings, considering key factors like primary energy use, energy costs, CO₂ emissions, and annual heating, cooling, and lighting energy consumption throughout the year. The building energy simulation (BES)-based optimization process involves the design variables that exert a significant impact on energy efficiency. These variables encompass window size, window-to-wall ratio, solar shading systems, glazing material properties, window frame composition, and profile dimensions. EnergyPlus and DesignBuilder with genetic algorithms facilitate this process. The study explores trade-offs among design alternatives and identifies building envelope configurations that improve energy performance and reduce costs compared to the reference building. Compared to the reference case, the energy-efficient cost-optimum solution achieved savings of 23.7% in primary energy (29.1 kWh/m²), 21.4% in global costs (44.4 €/m²), and a 24.4% reduction in carbon emissions (above 8 kg/m²). These results emphasize the benefits of using simulation-driven optimization in early design stages to meet energy efficiency and environmental goals.

Carbon emissions have greatly affected global warming and global climate change, which made environmental protection institutions repeatedly call for the importance of reducing carbon emissions. Urbanization is considered one of the most important causes of the increase in these carbon emissions because it consumes non-renewable energy to reach thermal comfort rates inside and outside buildings, in addition to the carbon emissions resulting from the building and finishing materials used in it. Thus, urban design is considered one of the most important factors affecting the quality of life in residential complexes, not only on the extent of road planning and open and closed spaces but also on the scope of the impact of urban design of residential complexes on environmental performance and the proportion of carbon dioxide emissions, which is what the research deals with to study. The research aims to study the effect of urban design on thermal performance and the proportion of carbon dioxide emissions into the air within residential complexes. The importance of the research is to shed light on the importance of urban design for residential complexes in influencing the efficiency of environmental performance within those communities, which will be directly reflected on human health, and the surrounding environment, and its repercussions on global warming to achieve more sustainable urban communities. The research uses the experimental and analytical approach through an urban design for three models of urban communities, in which the same variables are combined (heights, percentage of built areas, percentage of open areas, percentage of green areas, type of plants used in open spaces, finishing materials for the outer cover of the building as well as roads and corridors with open spaces), with one variable differs in it, which is the urban design for the distribution and shape for (three urban design models), with the aim of knowing the extent of the impact of the urban design of buildings on environmental performance and the proportion of carbon dioxide, as previously mentioned Envi-Met program was used to simulate this relationship. The results of the research have been concluded that the model (3) (the courtyard design) has the best thermal and environmental performance among the three models (from achieving thermal comfort rates and carbon dioxide emissions), followed by model (2) (Stripe and vertical design).

The structural failure of medical implants commonly happens due to the intense and dynamic stress profile generated by daily activities. To ensure long-term service life, an implant should demonstrate high fracture resistance under varying forces. This study investigates the behavior of a stationary crack embedded on the circumference of a Ti–6Al–4V made locking compression plates (LCP), under tension and bending loadings. The criteria for crack simulations and fracture propagations were reviewed, and the fracture modeling was performed using the contour integral approach available in Abaqus CAE software. Parameters, namely crack orientation and loading types, were selected to study their individual effects on stress intensity factors and for comprehensive fracture simulation purposes. The importance of mesh refinement at the tip of the crack was discussed. The simulation results revealed increased crack severity under bending loads, followed by tensile loads. As a noteworthy observation, the LCP implants should be designed and developed for future applications by accounting for the effect of bending stresses.

Concern about indoor air quality (IAQ) in schools has grown in recent years, particularly in the aftermath of the COVID-19 pandemic, which underscored its impact on children's health. Existing educational buildings employ simple strategies such as opening windows to ventilate classrooms. While this approach achieves the goal of air renewal, it compromises energy efficiency and user comfort. In this context, there is a need to address ventilation in schools from a new perspective, providing innovative technologies that allow quick and simple installation while guaranteeing high standards of air quality, energy efficiency, and user comfort. In response to this challenge, a new solution has been developed consisting of autonomous equipment installed inside each classroom and featuring independent intelligent control. The objective of the present study is to evaluate the applicability and the social impact that the widespread implementation of this alternative technology could have compared to conventional methods. The general characteristics and the specific peculiarities and needs of schools in the Basque Country (CAPV) in Spain were determined. The study first approached the topic theoretically through bibliographic references and statistical analysis, and subsequently, fieldwork to assess the reality of existing buildings. Additionally, an air quality monitoring campaign was carried out in pilot schools, conducted in two stages: first without ventilation and later with the new solution. The study evaluated the benefits in terms of improved air quality achieved, as well as the improvements in the implementation and operational processes. These results were extrapolated to Basque educational buildings, providing an estimation of the potential impact of this new ventilation approach. Highly positive results were yielded in terms of acceptance, feasibility, and ultimately, addressing the identified challenges.

Creating sustainable futures is one of the grand challenges of our time and one that requires a suit of disruptive solutions to act in concert towards the shared goal. For too long now, businesses have focused disproportionately on maintaining the status quo through sustaining innovations. Our current technologies, with their interest in existing users’ needs and product-market fit, miss opportunities to disrupt at scale. What is needed are disruptive solutions that tackle the sustainability deficiency. In the Science and Technology Studies field, it is well established that technology and society mutually shape each other. Thus, focusing on collective social needs, businesses can shape technologies such that they become fit for tackling sustainability issues. Today, businesses have opportunities to develop economically viable sustainable solutions. Based on signals from both industry and academia, we believe the time is ripe for disruptive solutions that incorporate social actors as active agents in the sustainability transformation. We propose a conceptual study that addresses the following research question: How might social interactions shape and drive disruptive solution development in businesses? To operationalize our research question, Christensen’s theory of disruptive innovation sheds some light into the social aspects of technologies, for instance by focusing on the process rather than the product or service. Additional perspectives are needed to grasp the complex and systemic phenomenon of purposefully crafting disruptive solutions in the digital age, in particular around how technologies can be co-created among social actors with competing interests but united by the drive of solving grand challenges. By combining disruptive innovation theory with social shaping of technology and social construction of technology, we seek to understand how businesses might initiate, craft, and shape disruptive technologies together with social actors rather than just adopt otherwise sustaining innovations.

The market for non-fungible tokens (NFTs) has been experiencing significant growth recently, resulting in an increase in the number of platforms offering NFT services and intensifying competition amongst them. Despite the rapid adoption of NFTs and their crucial role in retaining purchasers, empirical investigation or understanding of NFT purchaser retention in the literature is scarce. Our study aimed to develop a comprehensive research framework that encompasses the direct effects of purchaser satisfaction, trust, perceived usefulness, switching costs and lack of alternative attractiveness on the retention of NFT purchasers towards their main NFT platforms to address this research gap. We also examined the moderating role of two strengthening moderators (trust and perceived usefulness) and two constraining moderators (switching costs and lack of alternative attractiveness) in the satisfaction–retention link. Furthermore, we aimed to identify purchaser groups (single and multiple NFT platform purchasers) and examine heterogeneity in the satisfaction–retention link in these two purchaser groups. Our approach will reveal the previously neglected effects on the retention of NFT purchasers towards their main NFT platforms. We conducted a large-scale online survey of NFT retail purchasers in Hong Kong. Results showed that the five direct effects of purchaser satisfaction, trust, perceived usefulness, switching costs and lack of alternative attractiveness have a significant and positive influence on purchaser retention. Additionally, the two strengthening and two constraining moderators have significant moderating effects on the satisfaction–retention link. In the examination of the heterogeneity between single and multiple NFT platform purchasers, the two strengthening moderators only play a significant moderating role in the satisfaction–retention link for single NFT platform purchasers. Meanwhile, the two constraining moderators only play a significant moderating role in the satisfaction–retention link for multiple NFT platform purchasers. This study concludes with a discussion of the practical and theoretical implications of the findings.

Small and medium-sized enterprises (SMEs) are vital for a country’s economic growth. However, they often struggle with a persistent issue, namely, a funding gap. Alternative finance presents a solution to this problem but remains underutilized due to limited SME understanding and access. This research paper aims to explore the factors that influence small and medium-sized enterprises (SMEs) to access innovative alternative financing models. Through a literature review and conceptual framework, this study identifies the influential factors affecting SMEs’ access to alternative financing options. The research adopts the Technology-Organization-Environment (TOE) model as a theoretical framework to understand the factors that influence SMEs’ access to alternative financing. The TOE framework explains the process of adopting and implementing technological innovations in organizations and describes how it is influenced by technological, organizational, and environmental contexts. This study not only tests the TOE framework for SMEs but also extends it by adding the individual context, specifically the financial literacy of SME owners/managers, as a determining factor. The comprehensive conceptual model presented in this study offers a holistic perspective on the factors influencing SMEs’ access to alternative financing and delineates the dimensions of these factors. The model also provides avenues for future research to test and enhance it. This study makes substantial contributions to theory and practice by exploring emerging business models empowered by disruptive technology. This highlights the crucial role of alternative finance in fostering SME growth and underscores the significance of harnessing disruptive technology in creating innovative business models. The findings of this research are relevant to policymakers, regulators, and SME leaders as they provide valuable insights into fostering SME development and ultimately promoting economic prosperity.

This paper rejects the hypothesis that investment gardens, pools or sets present an imminent danger to the public. Gardens are eBay-style websites that allow anybody to create an investment product, and for it to be run on democratic principles rather than a principal-agent relationship. Gardens contain two inherent weaknesses: (1) no barriers to entry for the creators; and (2) retail customers being exposed to a highly niche and confusing area. This paper sources the limited research noting the opportunities for abuse that producer-managers of complex financial products can exploit against the unsuspecting public. This is considered against the complexity of DeFi products. This paper is a specific consideration of the underlying dialectic in the DeFi debate. How dare access to the riches of the finance sector be denied to anyone? Or is there an indisputable obligation for regulators to restrict DeFi access to protect vulnerable investors? Eight Gardens are regressed against the S&P 500, and Bitcoin and Ethereum prices. Additionally, the research compares the Sharpe Ratio of Gardens to 55 traditional investment fund products investing in DeFi. This leads to the paper proposing an extension of adverse selection from an agency-theoretic approach. The propensity in a Garden (and its circumstances) for adverse selection should determine whether retail investors are allowed to access a specific Garden.

The 50-year history of innovation, 1980–2030, has seen radical changes from technological to human-driven innovation and will move still further towards human-planetary well-being as the future goal. In our current world of multiple crises, we have only one way out: Nature Smart Design, based on systems thinking and creative thinking, and supported by human-like AI, geodesign, circular design, biophilic design, and a regenerative approach. Through these multiple perspectives and collective wisdom, it is possible to create all things artificial: cities, technologies, transportation, urban food production, culture, and societies that are more resilient, sustainable, equal, innovative, and creative than before. The future Innovation Atelier is a new type of innovation hub with an exciting co-location and diverse groups of creatives. These multi-sector innovation hubs span a range of business models, ownership structures, and physical spaces. Their goal is to create an innovative working culture and environment where businesses of all kinds can learn from each other, make connections, develop new skills, and become inspired to reach the next level. Many of these hubs occupy iconic buildings, including museums, warehouses, train stations, and navy yards, giving new life to micro-localities that have lost some of their vibrant life and attraction. These future Innovation Ateliers are also a great way to explore new ways to express creativity and creative thinking at large.