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2023 | Buch

Trends on Construction in the Digital Era

Proceedings of ISIC 2022

herausgegeben von: António Gomes Correia, Miguel Azenha, Paulo J. S. Cruz, Paulo Novais, Paulo Pereira

Verlag: Springer International Publishing

Buchreihe : Lecture Notes in Civil Engineering

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Über dieses Buch

These proceedings address the latest developments in the broad area of intelligent construction integrated in the mission of the International Society for Intelligent Construction (ISIC) which aims to promote intelligent construction technologies applications from the survey, design, construction, operation, and maintenance/rehabilitation by adapting to changes of environments and minimizing risks. Its goals are to improve the quality of construction, cost-saving, and safety, exploring fundamental issues related to the application and use of Artificial Intelligence (AI) and Machine Learning techniques and technology.

ISIC 2022 is the 3rd ISIC international conference, held in Guimarães, Portugal on September 6–9, 2022, and follows the previous successful instalments of the conference series in China (2019) and USA (2017). It took a holistic approach to integrate civil engineering, construction machinery, electronic sensor technology, survey/testing technologies, information technology/computing, and other related fields in the broad area of intelligent construction. The respective contributions cover the following topics: Artificial Intelligence for Design and the Built Environment, Building Information Modelling (BIM) and Construction Automation and Robotics, Intelligent Construction, Sustainable Construction, and Sustainable and Smart Infrastructures.

Given its broad range of coverage, the book will benefit students, educators, researchers and professionals practitioners alike, encouraging these readers to help the intelligent construction community into the digital era and with a vision on societal issues.

Inhaltsverzeichnis

Frontmatter

Artificial Intelligence for Design and Built Environment

Frontmatter
A WGAN Approach to Synthetic TBM Data Generation

In this work we propose a generative adversarial network (GAN) based approach of generating synthetic geotechnical data for further applications in research and education. Geotechnical data generated by GANs shows similar characteristics as the original data, but still presents unique samples with no connection to the technical content of the original data. The data can therefore be made available publicly without any legal issues.A WGAN (Wasserstein GAN) algorithm is used to generate synthetic tunnel boring machine (TBM) operational data based on real data from a major European tunnel construction site. The demands on the synthetic TBM data are of a dualistic nature: on the one hand, the data has to be sufficiently dissimilar to the original data, so that it does not create confidentiality issues (demand for originality). On the other hand, it has to show the same patterns and follow the same rules as the original data, so that it can be used as if it were real TBM data (demand for conformity). The WGAN model describes how a synthetic dataset is generated, in terms of a probabilistic model based on real data. By sampling from this model, we are able to generate new, unique synthetic and realistic TBM data.We show that the demands for originality and conformity of the newly generated data are fulfilled.

Paul J. Unterlass, Georg H. Erharter, Alla Sapronova, Thomas Marcher
Digital Construction Strategy for Project Management Optimization in a Building Renovation Site: Machine Learning and Big Data Analysis

The average incidence of design variation and unexpected events related to the delivery of construction sites is about 13% compared to project planning. The objective of this study is to lower this percentage to about 10–11%, using methodologies aimed at configuring effective digital management strategies. The main purpose is to enhance process efficiency and optimization of construction management strategies, as BIM-based digital management approaches allow to predict unforeseen events, reducing negative variation in time and costs.The proposed application case concerns an applied methodology conducted on a 35,000 m2 historical building renovation project, in a central urban context of Rome, owned by a public institutional real estate company.The implementation of the proposed BIM-based digital information management strategies allowed to enhance efficiency in site management, reducing delay’s incidence on construction site delivery of about 3%. Such improvement is related to the reduction of delays deriving from prediction of unexpected events. The application of the proposed methodology radically improved the traditional site management strategy used by the construction company, generating a significant reduction of wastes in time and resources; in fact, the use of AI and ML systems promptly supported decision-making processes.The result is the configuration of a digital process allowing an optimized time and material management process through real-time monitoring of on-site activities, configuring an effective decision-making support system.Moreover, the information model was also developed according to Asset Information Model (AIM) requirements able to provide a reliable database for the operation and maintenance phase.

Sofia Agostinelli, Fabrizio Cumo, Riccardo Marzo, Francesco Muzi
Disruptive Innovation in AEC: The Case of Artificial Intelligence Applied to Project Management

Digitisation has gained industry-wide momentum and is changing how the sector operates. Emerging digital technologies such as Artificial Intelligence (AI) are increasingly implemented at the organisational, or most commonly the project, level. Notwithstanding, some industry leaders have failed to shape an environment of opportunity to disrupt; often used as a tool to pursue productivity, doing more of the same. We argue that there is an opportunity to disrupt the industry’s construct, rather than to replicate ancient habits at a faster pace. However, such a pursuit creates a complex paradigm. Leaders demand innovation that makes sense to humans, i.e., to themselves, creating an epistemological barrier that limits the aspirational goal of disruption. This is better classified as Sustaining Innovation (SI) as opposed to Disruptive Innovation (DI), as it does not displace the original ideals and fundamentals of the sector’s construct. A possible avenue to disrupt the current industry knowledge construct is to engage in a different level of consciousness, by generating insights through AI techniques at a size and speed impossible thus far. We present a case study in the field of Project Management and show results of disruptive insights in a portfolio of approximately US$ 20B, exploring the orthodoxies of such breakaway. We further discuss the leadership traits which could motivate DI in AEC, arguing that this requires an open engagement in an exploratory journey with limited certainty ex-ante, driven by awareness and vision. The realm of DI is not a replication of the observable world, rather an augmentation of it.

Ricardo de Matos Camarinha, David Porter, Cuong Quang
Neural Network-Based Model to Predict Permanent Deformation Induced in the Subgrade by the Passage of the Trains

The prediction of the permanent deformation and respective accuracy in the subgrade is one of the main concerns of the Railway Infrastructure managers to reduce the maintenance costs and operations. This paper purposes a new methodology regarding the prediction of the permanent deformation based on a parametric study performed using a hybrid methodology that includes the short and long-term performance. In this parametric study, the dynamic mechanism was considered as well as several important factors with impact on the stress levels and permanent deformations: wavelength of the unevenness profile, type of railway structure, train speed, mechanical properties of the subgrade and the geometric position of the analysis in the track development direction. This extensive parametric study allowed building a robust database used to predict the permanent deformation induced by the passage of the train. This database feeds a neural network model whose performance was evaluated based on different metrics: Mean Absolute error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), standard deviation, and regression coefficient (R). Moreover, to show the ability of the model, it will be tested and validated based on published experimental results from a physical modelling of ballasted and ballastless tracks. Its results show that the developed model is rapid and efficient to predict accurately the permanent deformation induced by the passage of the train. It can have the potential to be further implemented in a computational decision support system for railway track maintenance and renewal management, instead of conventional corrective maintenance and renewal.

Ana Ramos, António Gomes Correia, Rui Calçada
Prediction of Airport Pavement Moduli by Machine Learning Methodology Using Non-destructive Field Testing Data Augmentation

For the purpose of the Airport Pavement Management System (APMS), in order to optimize the maintenance strategies, it is fundamental monitoring the pavement conditions’ deterioration with time. In this way, the most damaged areas can be detected and intervention can be prioritized. The conventional approach consists in performing non-destructive tests by means of a Heavy Weight Deflectometer (HWD). This equipment allows the measurement of the pavement deflections induced by a defined impact load. This is a quite expensive and time-consuming procedure, therefore, the points to be investigated are usually limited to the center points of a very large mesh grid. Starting from the measured deflections at the impact points, the layers’ stiffness moduli can be backcalculated. This paper outlines a methodology for predicting such stiffness moduli, even at unsampled locations, based on Machine Learning approach, specifically on a feedforward backpropagation Shallow Neural Network (SNN). Such goal is achieved by processing HWD investigation and backcalculation results along with other variables related to the location of the investigation points and the underlying stratigraphy. Bayesian regularization algorithm and k-fold cross-validation procedure were both implemented to train the neural model. To enhance the training, a data analysis technique commonly referred to as data augmentation was used in order to increase the dataset by generating additional data from the existing ones. The results obtained during the model testing phase are characterized by a very satisfactory correlation coefficient, thus suggesting that the proposed Machine Learning approach is highly reliable. Notably, the proposed methodology can be implemented to evaluate the performance of every paved area.

Nicola Baldo, Fabio Rondinella, Clara Celauro
Prediction of Geological Conditions Ahead of the Tunnel Face: Comparing the Accuracy of Machine Learning Models Trained on Real and Synthetic Data

Risk assessment during the construction of an underground structure (e.g., tunnel) should rely on accurate information of the rock mass that will be excavated. Advances in engineering equipment allowed vast data collection and thus opened a possibility for data-driven prediction of geological conditions. For an accurate prediction, the integration of various data sources is required, which makes the predictions complex and time-consuming for specialists. The application of machine learning (ML) methods provides a shortcut in analyzing complex data to predict geological conditions.In this work, the authors present a new approach for predicting the geological information ahead of the tunnel face by using an ensemble of ML methods combined with an oversampling technique.Implicit dimensionality reduction is introduced to deal with nonlinear coupling between seismic data: unsupervised machine learning methods are used to cluster the seismic data from two underground construction sites. Obtained information on clusters is then integrated with various seismic and geological variables, and a supervised machine learning model is trained to predict the rock mass class and/or the rock type. The data is over-sampled to avoid biased results when the training datasets are imbalanced and a mix of real and synthesized data is used in training, while an accuracy check is performed only on real data.Our results show that the proposed ML ensemble model has high accuracy in predicting geological conditions. Furthermore, the application of the oversampling technique helps to improve the accuracy of the ML predictors further.

Alla Sapronova, Paul J. Unterlass, Thomas Dickmann, Jozsef Hecht-Méndez, Thomas Marcher
Predictions of Root Tensile Strength for Different Vegetation Species Using Individual and Ensemble Machine Learning Models

Vegetation is needed to improve soil slope stability. The roots of different species stabilize the ground by their tensile strength. However, how the tensile strength is governed by different root and shoot characteristics is less known. In this study, root tensile strength was investigated, and root and shoot characteristics were simultaneously measured. An experimental relationship between the root tensile strength and root diameter was developed. First, feature selection methods were applied to identify the critical characteristics affecting root tensile strength. Furthermore, the machine learning (ML) models were developed using individual methods (Sequential Minimal Optimization Regression (SMOreg), Instance-based Learning (IBk), Random Forest (RF), Linear Regression (LR), Multi-layer Perceptron (MLP)) and ensemble methods (ensemble via MLP and LR models). Results showed that the ensemble via the MLP outperformed all other individual models as well as the ensemble via the LR. The root mean square error of the ensemble via MLP was 3 times better compared to the experimental model based upon a power relationship between root tensile strength and the root diameter. The need for studying the complexity in relationships of various attributes of vegetation using ML models is discussed.

Tarun Semwal, P. Priyanka, Praveen Kumar, Varun Dutt, K. V. Uday
Towards the Development of a Budget Categorisation Machine Learning Tool: A Review

Engineering, procurement, and construction (EPC) contracts include time, budget, quality, and safety, among other issues. In budgeting, construction companies must assess each task's scope and map the client's expectations (expressed in the bill of quantities) to an internal database of tasks, resources, and costs. The results from this classification will determine the quality of the tenders issued by the company and are thus contractually binding. Construction companies must achieve their contractual targets in order to make a profit.In this paper, we review the literature and explore the latest advancements regarding the automatisation of these processes to find the methods that yield the best results in the classification of bills of quantities and works in the construction industry.Although full automation is not within our reach in the short term, especially due to the lack of standard construction specifications, machine learning can provide useful support tools. This communication is part of the authors’ study aiming to develop a framework and tool to automate the process of task classification in a construction contract.

Luís Jacques de Sousa, João Poças Martins, João Santos Baptista, Luís Sanhudo
Transforming Construction Entities from Traditional Management to Autonomous Management Using Blockchain

Digitalizing supports construction entities’ processes that aim to perform better. However, bureaucracy and excessively complicated procedures are still causing poor project performance. The traditional management authority model is centralising and pyramidal, where decisions come from the upper management and apply over the project regardless of the project-specific needs. Autonomous management processes are driven according to individuals or workgroups perspectives. The autonomous management approach allows for an accelerated decision process to improve construction project performance. This article investigates the management of construction entities and activities using an autonomous management system by exploring the possibility of developing Blockchain-based management tools. Blockchain refers to the distributed ledger technology where data are stored permanently on a multi-node network and connected using cryptography. Blockchain can help track activities, monitor individuals’ performance and enforce accountability to create an autonomous construction organisation. Decentralised Autonomous Organisations (DAO) is a Blockchain concept that aims to automate decisions and facilitate process workflow. DAOs aim to reduce the time needed for repetitive decisions and distribute the responsibilities among the project participant. The study aims to shed light on the DAO concept and explore the possibilities of adopting it within construction practices. The article presents a hypothetical example for using DAOs in construction using the DAO deployment platform to illustrate the creative workflow for non-coders.

Mohammad Darabseh, João Poças Martins

Building Information Modelling (BIM), Construction Automation and Robotics

Frontmatter
A Toolbox for the Automatic Interpretation of Bender Element Tests in Geomechanics

The small strain shear modulus is an important characteristic of geomaterials that can be measured using bender element experiments. However, most conventional interpretation techniques are based on the visual observation of the output signal and are inherently subjective. Other techniques, based on the cross-correlation of input and output signals lack physical insight, as they rely on the (faulty) assumption that input and output signals are similar. GeoHyTE is a new toolbox for the automatic interpretation of the output signal in bender element tests. GeoHyTE creates a digital twin of the experiment and determines the small strain shear modulus by maximising the correlation between the output signals obtained experimentally and digitally. It is objective, as minimal user intervention is required, and physically-meaningful, as the wave propagation process is modelled in full. Its results are successfully validated against well-known benchmarks reported in the literature.

Ionuţ Dragoş Moldovan, Abdalla Almukashfi, António Gomes Correia
Additive Manufactured (3D-Printed) Connections for Thermoplastic Facades

Additive manufactured thermoplastic facades are an emerging topic in the building industry. Such building envelopes have free-form designs, which can enable sustainable solutions. However, the connections between the panels of these thermoplastic facades have not been investigated. These joints are not yet integrated into the 3D-printed facade design. Consequently, there is a research gap in designing holistic connections in additively manufactured facades. This paper is a foundation study that aims to find suitable design parameters for such connections. It is an initial analysis in the emerging field of AM facades, aiming to tackle the fundamentals of designing for 3D-printed facade assembly. The study highlights a series of possible constraints and opportunities in connecting discretized 3D printed panels. This step is essential for identifying new design opportunities in the emerging topic of additive manufactured facades.

Ina Cheibas, Ringo Perez Gamote, Beril Önalan, Ena Lloret-Fritschi, Fabio Gramazio, Matthias Kohler
Auto(mated)nomous Assembly

The paper presents research on a hierarchical, computational design approach for the aggregation of dry-joint, interlocking building blocks and their autonomous assembly by robots. The elements are based on the SL Block system developed by Shen-Guan Shih. The work proposes strategies to assemble multiple SL blocks to form larger aggregations which subsequently turn into building elements on another scale. This approach allows reconsidering the resolution of architectural constructions. Building elements that have previously been considered as solid and monolithic can now be aggregated by many small SL-Blocks. Those dry-joint aggregations allow for easy disassembly and reassembly into different configurations and therefore contribute to a circular reuse of building elements. In order to facilitate such a permanent transformation, the research also includes first steps towards the autonomous assembly of building blocks through a robot including the planning for how to optimally place the parts, as well as ensuring feasible execution by the robot. The goal is a fully autonomous pipeline that takes as input a user-defined, desired shape, and the available building blocks, and directly maps to actions that are executable by the robot. As a result, the desired shape should be optimally resembled through the robot’s autonomous actions. The research therefore addresses handling the combinatorial search space regarding the possibilities to combine the available parts, incorporate the constraints of the robot, creating a feasible plan that ensures the stability of the structure at any point in the construction process, avoiding collisions between the robot and the structure, and in the case of SL-Blocks, trying to ensure that the overall structure is interlocking.

Yuxi Liu, Boris Belousov, Niklas Funk, Georgia Chalvatzaki, Jan Peters, Oliver Tessmann
BIM-to-FEM: Development of a Software Tool to Increase the Operational Efficiency of Dam Construction Projects

As the geotechnical engineering profession progresses into the era of Building Information Modelling (BIM), projects are requiring an efficient integration of numerical simulations into the BIM workflow. However, there still exists some large hurdles concerning the interchange of information between BIM software and numerical analysis software. The embankment settlement tool presented in this paper addresses these challenges by developing a multi-functional Python interface to link data sources from the geotechnical Finite Element Method (FEM) code Plaxis 2D and the alignment design software ProVI. The capabilities of the script are demonstrated based on a dam construction project executed in Germany, where the time-discrete analysis of settlements is of high interest in order to ensure a high level of collaborative efficiency between design and execution teams. The multi-functional Python interface improves the quality and the effectiveness of numerical predictions, not only during the design phase but also, and in particular, during the construction of dam construction projects. Moreover, it resolves typical difficulties in establishing BIM-to-FEM links.

Michael Giangiulio, Andreas-Nizar Granitzer, Franz Tschuchnigg, Jens Hoffmann
Converting Algorithms into Tangible Solutions
A Workflow for Materializing Algorithmic Facade Designs

Two main concerns drive the architectural practice: the design and the construction of buildings. This makes the creative practice highly dependent on construction viability, most design decisions having to consider, among others, the available materials and construction techniques and the associated manufacturing costs. Nevertheless, the desire to conceive complex geometries has always been present in architecture, often leading to innovative solutions and structures that go beyond what had been done to date. The emergence of computational design in the last decades has further accentuated this ambition by providing architects with unprecedented design freedom. The realization of such shapes, however, is not as easy as its 3D modeling due to limitations in the available manufacturing strategies. In this paper, we address this problem with Algorithm Design (AD), a design approach based on algorithms, presenting a design workflow that benefits from its (1) geometric freedom in developing facade design solutions and (2) expressiveness in converting and detailing the obtained solutions for manufacturing. We evaluate our proposal with an algorithmically developed prototype of a geometrically complex facade. The aim is to illustrate its potential in exploring design alternatives that consider multiple design criteria, while automatically detailing them for construction and producing the corresponding technical documentation. We also intend to demonstrate the importance of the proposal’s flexibility in considering different construction schemes that, in turn, result in different aesthetic outcomes and manufacturing needs.

Inês Caetano, António Leitão, Francisco Bastos
Development of a BIM Model for Facility Management with Virtual/Augmented Reality Interaction

This paper gives an insight on the development of new features for an existing Facility Management software solution while presenting the work done during one of the project’s case studies. The work includes the use of ‘Building Information Modelling’ approaches/software to feed relevant information to the facility management database. The generated graphical and non-graphical information is then the backbone for a virtual/augmented reality interface.The article begins with a general context of currently available FM solutions with BIM integration. Then, the case study and its requirements are presented as to set the bases for the adopted methodology. The connection between the project goals and the BIM uses are made.The workflow is demonstrated through the initial survey of the relevant building (industrial facility) with the laser scanning technique. On the modelling, special attention is given to the specialized equipment that exists on the building and the simplifying assumptions that optimize the efficiency of the final BIM model. The final model is exported into the project relevant formats according to their expected testing purposes for the defined uses: (i) feeding the FM database; (ii) virtual/augmented reality interface.

Filipe Finco, Andressa Oliveira, Nuno Sousa, Célia Pinto, José Granja, Miguel Azenha
Improv-Structure: Exploring Improvisation in Collective Human-Robot Construction

The emerging field of Collective Human-Robot Construction (CHRC) opens up vast space for human-robot interaction and collaboration in real-time for construction tasks, making the idea of improvisation a critical layer to explore. Compared to the traditional linear workflow of pre-planned structures, improvisational construction allows for a real-time collective building experience, giving the build team more space for creativity, flexibility, and immersive design. However, the concept of improvisation in an architectural context has not been fully explored yet, especially with a multi-robot-human team, despite rich literature on improvisation in art performance, management, and robotics. In this paper, we present Improv-Structure, a proof of concept for improvisational construction, where ~500 bamboo rods were assembled by two industrial robotic arms and several humans using a collective decision-making mechanism. The robotic arms functioned as guidance and structural support, while the humans led the design and construction process. Together, this heterogeneous team can create a structure that neither party can easily achieve alone.

Isla Xi Han, Stefana Parascho

Sustainable Construction

Frontmatter
An Exploration of Graph-Based FEM Optimization for Construction Industry

In AEC industry, the general process of structural design is not really objectified, so the modularity of cross-professional collaborative design is usually hard to be achieved.The study proposes, for both traditional and other materials, a new objectified data structure and corresponding calculation method. Through the elaboration of a new set of concepts centered on “initial-split-graph”, the study re-narrates the definition of structural damage and could truly realize the modularity of cross-professional collaborative design.Firstly, A graph-based data structure is established, and the corresponding plug-in is developed for cross-professional geometric modeling. It supports the equivalent data-driven-modeling on several AEC platforms and is greatly simplified comparing with IFC.Secondly, a subassembly is selected from a structural system, and a comprehensive mechanical analysis has been completed on ANSYS through the plug-in.Finally, As a weight function on the initial-split-graph, the stress-strain approximation function is tested. On each vertex, the function argument is expressed as the stress vector acting, and the calculation result weights include its displacement, rotation and its own comprehensive destruction level.The study shows: 1. Within “stable” initial-split-graph, structure verification can maintain its objectification, and finally the consistency of the data structure throughout the design phases can be achieved. 2. A new direction is provided for the theoretical verification of new materials with stable mechanical properties as structural components.

Luxin Luo
Assessing Hazardous Spills Impact on Road Surface Performances by 3D High Resolution Surveying Techniques

Surface texture of a road pavement is recognized as one of the most relevant parameters in driving safety. Spillages on the highway can lead to dangerous conditions for road users and cause damage to the road surface. Hazardous substance spills caused by traffic accidents threaten the safety road and request a specific clearance operation to reduce the risk of asphalt surface degradation. The aim of this paper is to evaluate the effect of usual hazardous liquids spilled during traffic accidents from vehicles on the asphalt properties and related safety parameters. The experimental application is performed via digital photogrammetry. The application of 3D geomatic techniques allows to calculate new texture indicators through geometric and colorimetric information more accurate than traditional survey techniques. The proposed approach can constitute a new useful investigation method, in addition to traditional surveys, which are slower and less precise. The texture reading by using three-dimensional analysis is an innovative way to obtain and compare the traditional performance indicators used to measure friction with new proposed indicators. In particular, the described experience evidences the mechanical behaviour and the structural damage of asphalt samples related to different exposure times treated with five hazardous liquids. Both traditional and innovative techniques are carried out to evaluate roughness (British Pendulum Number, BPN). The obtained results show how the various substances affect in different way the samples asphalt surface.

Valentina Alena Girelli, Luca Cotignoli, Navid Ghasemi, Claudio Lantieri, Maria Alessandra Tini, Rossella Vecchione, Gabriele Bitelli, Valeria Vignali
Integrating Smart and Sustainable Construction: A Review of Present Status, and Possible Opportunities

According to the construction leadership council: “smart construction is building design, construction, and operation that through collaborative partnerships makes full use of digital technologies and industrialized manufacturing techniques to improve productivity, minimize whole life cost, improve sustainability and maximize user benefits” [1]. So, referring to this definition, improving sustainability is one of its aims; thus, a significant relation between smart and sustainable construction is identified.Many researchers discussed the integration between smart and sustainable construction to achieve different objectives like selecting the best alternative green material, alternative model for a building, and suitable energy-saving method. As existing research utilized this synergy to gain improvements in the economic and environmental pillars of sustainable construction only, while many issues are there in the social pillar require improvements such as improving occupational safety and health during the construction phase, job security and welfare, improving the working environment and job satisfaction.Hence, this paper starts with an extensive literature review, that covers the status of the integration between smart and sustainable construction, and the existing application of this integration in sustainable construction’s three main pillars. Then the research focuses on highlighting the issues under the social pillar of sustainable construction that could be improved as a result of utilizing smart construction tools. This paper is supposed to fill the current gap in the literature, as it highlights the open issues and directions for future work associated with the integration between smart and sustainable construction.

Mai Ghazal, Ahmed Hammad
Integration of Spot Tests and Vibratory Roller Data in Rural Road Pavement Layers Made with Recycled Aggregates

Utilizing recycled aggregates of Construction and Demolition (C&D) and other wastes from different civil engineering activities in several layers of pavements, especially in low traffic roads, revealed a huge potential of improving sustainability in the infrastructures of rural transportation. Using modern smart technologies like Continuous Compaction Control (CCC) rollers for these materials needs performing studies to optimize the adopted engineering parameters and updating QC/QA assessment protocols. Spot tests such as in-situ densitometry, Plate Load Test (PLT), Light Weight Deflectometry (LWD) together with dynamic California Bearing Ratio (CBR), could contribute to the validation of the procedure of roller compaction. The correlation of these sets of data with the CCC results could present valid information on the stiffness development of different recycled materials from different sources and compositions, and their behavior under vibratory compaction. In this study, a number of spot tests were performed before and after constructing two superimposed layers of unbound and cement-bound recycled materials, compacted by CCC vibratory rollers, in order the evaluate the correlation of their results and the vibratory moduli. Despite the differences in the degree of correlation of spot tests and CCC data in sections of different recycled materials, similar trends were observed within the distribution of data from the field. The state of being unbound or cement-bound in trial pavements with recycled materials has effect on respond to vibratory roller and spot tests. Moreover, the correlation of various spot test results on different recycled materials expose the dissimilar responses related to their components, test contact area and test depth of effect on field pavement.

Sajjad Pourkhorshidi, Cesare Sangiorgi, Daniele Torreggiani, Patrizia Tassinari
Multiphysical Coupling Analysis of the Transition Zone Behaviour in Corroded Reinforced Concrete Structures

Corrosion is a multiphysical process that takes place at the steel/electrolyte interface causing a series of interconnected coupled phenomena. For reinforced concrete structures, electrochemical reactions are controlled by environmental, physical, and mechanical factors related to concrete and steel. The electrochemical nature of corrosion can manifest by cracking, local spalling, presence of rust, sectional loss of alternating stability, and serviceability of infrastructures. The different nature of the constituent materials creating the structure’s system causes an interface problem in the interaction zone considered as the transition zone (ITZ). This work aims at studying the transition zone problem of a concrete beam reinforced with a single steel bar subjected to corrosion. The objective is to assess the beam system’s durability and behaviour over time under the influence of corrosion, first with mechanical analysis and then by combining corrosion into a mechano-electrochemical study using coupling techniques to model the steel/concrete transition zone (ITZ). The development of this model has allowed us to better understand the importance of modelling the transition zone in the assessment of multi-material system interactions, as well as the contribution of coupling techniques in numerical modelling of corroded infrastructures.

Yasmine Meterfi, Habib Trouzine, Youcef Houmadi
The Influence of the Visual Factor on the Efficiency of Visualization Method in the Production Environment

We review the concept of negative visual factors as an important part of the new approach to assess the safety of workplaces. As the world analysis shows, the visible environment of the city is one of the main factors, that is determining the influence of negative factors at the stage of danger prevention. The key role of the environment influence on psychological state and human health is played by visual factors. Urbanization caused deterioration of the visual quality in human habitats. Urban space is formed by unnatural spatial structures which are source of homogeneous and aggressive visual fields. The negative consequences of the outdated approach to the formation of a safe working environment was the lack of consideration of modern scientific achievements in the field of visual ecology [6, 7]. The predominance of uniform in color and texture of large planes – which create homogeneous visual fields, or, conversely, its supersaturating with the same elements – which, accordingly, create an aggressive visual fields. Visual stress that may caused variety of neurological conditions and increased the risk of traumas, myopia, nystagmus, neurosis, fatigue, aggressive behavior. Improving of production and life safety can be achieved by using tools and methods of visualization in complex with optimizing the effects of the working environment on the human visual system. To reduce the risk of negative psycho-physiological effects of visual factors in the workplace and to increase the effectiveness of safety equipment through the use of visualization tools and methods, it is necessary to expand the list of dangerous and harmful production factors, supplementing it with physically harmful and dangerous visual factors.

Valeriia Zhurbenko, Anatoliy Belikov, Petr Sankov, Pavlo Nazha
Waste Elimination and Value Management Framework Based on Lean Design Methods

Complex design processes depend on vast amounts of information, decline productivity, and cause multiple interruptions in resources and information flows. Design-related problems have been commonly studied from Lean Design Management (LDM) perspective that involves customer value, design processes, and cognitive management issues. LDM is built on the lean construction paradigm, which adopts lean production theories, principles, and practices. That provides stakeholders with problem-solving techniques and knowledge management methods at various levels of analysis (organizational, strategic, and tactical levels). Based on LDM, several social and technical measures can be applied to tackle waste and provide value for the final customer. This paper briefly reviews design-context wastes according to LDM perspectives. In this context, a conceptual model is provided that assembles LDM methods Target Value Design (TVD), Last Planner System (LPS), and Building Information Modelling (BIM). Three research gaps were detected in the reviewed methods. Firstly, there is a lack of adaptations of LDM methods toward standards for managing BIM digital assets. More research is needed to expand those methodologies to assist the decision-making processes of the involved parties in managing the construction of digital assets. Secondly, the current design task management methods adapted from agile software management and LDM are “far away from the addressing construction design peculiarities. Third, transactional audit using blockchain technology is required to secure money flow among cross-functional organizations. This research provides opportunities for applying LDM during design decision-making processes, especially TVD and LPS.

Mahmoud Karaz, José Cardoso Teixeira

Intelligent Construction

Frontmatter
Aggregating High-Precision GNSS Intelligent Construction Data for Quality Asphalt Pavements

Intelligent Construction Technologies (ICT) collect information, store, analyze, and process data, to aid in executing an appropriate action or decision that results in quality construction. Using ICT improves quality, efficiency, and safety during the construction process. These technologies produce large data sets that may be analyzed to reveal patterns and trends and provide construction companies and agencies with vast amounts of information to help solve construction problems. Despite these benefits, the large data sets also pose a challenge when collecting, viewing, analyzing, sharing, and using data from multiple technologies. Since ICTs have proprietary software specific to each vendor or equipment type, it is challenging to evaluate different data types simultaneously and efficiently using vendor software alone. Veta is a map-based geospatial software to overcome this challenge. Asphalt paving ICT data from different equipment types that use high-precision Global Navigation Satellite System (GNSS) can be combined to show project data using Veta software. Aggregating the data allows for efficient identification and mitigation of construction risks. Currently, Veta can import data from intelligent compaction (IC) machines, paver-mounted thermal profiler (PMTP), and dielectric profiling system (DPS) into the same analysis project to perform viewing, filtering, sublotting, spot test importing, and analyzing. Veta displays both input data and output results in easy-to-read formats, including graphs and maps. This comprehensive display allows users to view many aspects of asphalt pavement construction for quality assurance. A real-world case study shows how Veta can aggregate asphalt pavement construction data to evaluate construction quality, troubleshoot construction issues, and support digital as-builts (DAB) and Digital-Twin concepts.

George K. Chang, Amanda L. Gilliland, Abbasali TaghaviGhalesari
Construction Sector Transformation: Developing a New Learning Paradigm

Architecture, Construction and Engineering (AEC) has historically reported low levels of productivity and performance, especially when compared with other sectors and industries such as automotive, aviation, manufacturing and ICT/telecoms. This is particularly concerning, given that this sector is a significant contributor to many countries Gross Domestic Product (GDP). Acknowledging this, and the fervent need to improve productivity as part of AEC’s digital transition to Industry 4.0 – the so-called Construction 4.0 - this paper explores some of contributory forces affecting this journey. One aspect of this transition is the examination of industrialisation, or indeed the level of technological sophistication applied to this sector, including the ‘success’ indicators used. A historical reflection on the industrialisation process is presented, including traditional industrialisation approaches from Late Industrialisation. Developing economies undergoing late industrialisation are seen as unique because they did not base their material development on inventions, but rather on the basis of learning – i.e., using ‘borrowed’ technology. From this literature, a qualitative [explorative] research approach was adopted based on the principles of Critical Realism. Findings from this were then applied to four focus groups with 23 industry professionals. Research findings highlighted that AEC was not industrialised per se, but simply modernised. Moreover, that access to modern techniques and technological sophistication were insufficient to support sector transformation through such conduits as industrialisation, knowledge management or indeed innovation. Findings also indicate the need to more purposefully align transformational thinking to socio-economic transition models, principally those underpinned by grounded learning paradigms aligned to key industrial policy institutions.

Ricardo de Matos Camarinha, Jack Goulding, Camaren Peter
Digitalization of the Asphalt Paving Workflow and Process – Its Challenges and Opportunities

Value Chains and workflows in asphalt road construction are characterized by the high dynamicity and complexity of the product, production process and environment. On the other hand, there is an extremely small tolerance with regard to the quality to be produced.Defects in road construction – and in particular in the production of the surface layer – are always associated with significant costs in terms of time and money, since the previously constructed product needs to be laboriously removed and repaved. An increase in the degree of automation with the aim of automating asphalt road construction and enhancing its process stability serves to substantially increase the quality of the road surfaces being produced.The result is a significant extension in the sustainability of the road’s service life and a major reduction in construction and redevelopment costs. This, in turn, improves operational efficiency and increases the macroeconomic benefits. This potential can, however, only be fully realized if individual systems are automated and networked based on standards and whether they can exchange information bidirectionally with the digital construction model. This enables quality-assured road construction through a quality assurance system integrated within the manufacturing process.Topcon has developed several software and hardware solutions to address this challenge. Knowledge from projects all over the world done with (all or parts of) these solutions push the development of digitalization – with a focus on user-friendliness, efficiency, and project economics. Our text will address the state of digitalization and highlight future possibilities of the whole paving process focusing on logistics, temperature and 3D paving or milling.

Christoph Bertsch
Discrete Aggregate Mass Calculation Method for Visual Detection of Aggregate Gradation and Elongated and Flat Aggregate Contents

Aggregate gradation and elongated and flat aggregate contents strongly affect the performance of asphalt mixtures. During the visual detection of these two indexes, the morphology in a single view is typically used for mass calculation. However, it has a significant error and affects the detection accuracy. Therefore, in this study, the morphologies of an aggregate from multiple views were collected during falling. Size features were also extracted for mass calculations. Two mass calculation methods, the multi-view equivalent volume models (MEVMs) and ensemble regression learning model (ERLM), were proposed in this study. MEVMs were constructed using multi-view shape features. The relationships between pixel volumes of MEVMs and actual aggregate mass were established through the least square method for mass calculations. Correlation analyses of multi-view size features were conducted and weakly correlated features were eliminated. The ERLM was combined with the K-nearest neighbor algorithm, multi-layer perceptron neural network, support vector regression algorithm, and ensemble decision tree using an adaptive weight assignment algorithm. The ERLM was trained with processed multi-view features for mass calculations. Finally, the feasibility of MEVMs and ERLM were verified through mass calculations of aggregates with different particle sizes and shapes. Both methods showed significantly improved correlation and accuracy, with the ERLM showing stronger generalization ability in particle size and shape scales than that of MEVMs. Therefore, the ERLM could be effectively applied for the visual detection of aggregate gradation and elongated and flat aggregate contents. The application of the proposed methods was verified in practical road engineering.

Zeqi Chen, Ying Gao, Jiupeng Zhang, Siyu Chen, Tao Ma, Xiaoming Huang

Sustainable and Smart Infrastructures

Frontmatter
A New Sustainable System for Piped Water Cooling of Mass Concrete Structures

In mass concrete casting applications/structures, the heat evolved during the hydration process at early ages may not dissipate appropriately due to the large size of the pours and the low thermal conductivity of concrete. Embedded pipe cooling systems for circulating water are frequently used for heat removal (control the temperature rise) from the interior of the concrete mass. However, in current practice and design of post-cooling applications, the piping for cooling remains within the concrete permanently, even though the need for cooling only exists during the construction phase. This paper presents and discusses the development of a pipe cooling system in which the initial pipe, used to establish the hole for circulation of water, can be removed a few hours after casting. Despite the removal of the initial pipe, water can be circulated through a flexible sleeve placed within the remaining concrete cavity. The novelty of such an approach is that all the components and materials during such process are fully reusable which makes the methodology sustainable with important cost-saving and less carbon emission impacts associated with the pipe cooling in mass concrete structures.

Mohammad Kheradmand, Romeu Vicente, Miguel Azenha, José Luís Barroso de Aguiar
A Novel Graphene-Based Geotextile for Use in Smart Pavements

Monitoring pavement mechanical, hydro and thermal loading is crucial in pavement reliability modelling and maintenance operations. Monitoring mechanical loading is especially important for effective traffic management and to prevent fatigue damage by controlling increased distress caused by vehicle overloading. Nevertheless, most of the existing techniques have several limitations, in terms of providing only discrete partial information, being destructive to the pavement being monitored and incurring high installation and maintenance costs. Therefore, there is still a deficiency in a monitoring method that enable spatially continuous and complete information capabilities. In the current project, a novel graphene-coated conductive geotextile, with sensing capability, is evaluated for use in road infrastructure. Being used for leak detection in waste, containment and landfill applications, the current project will extend its use to applications involving mechanical, hydro and thermal loading with special focus on its potential as a spatially continuous sensor to detect response and damage in pavement layers. In this study, various tests were carried out to characterize the electrical response of the material in terms of mechanical loading. The results showed a notable electro-mechanical behavior in the geotextile and proved the versatility of the material for use in a vast range of pavement applications.

Harini Senadheera, Abdelmalek Bouazza, Jayantha Kodikara, Daniel Gibbs
Adaptive Fuzzy Inference System for Automated Pavement Condition Evaluation of Large Pavement Sections from Ground Penetrating Radar (GPR) Thickness Data

Monitoring pavement sub-surface layer thicknesses is essential to ensure stable pavement performance under heavy traffic loading. In addition, accurate estimation of pavement subsurface layer thicknesses is required for pavement condition evaluation and remaining life analysis. Traditionally this vital information is ascertained using conventional techniques such as coring/drilling at discrete locations, which are often destructive. In contrast, ground-penetrating radar (GPR) is a non-destructive proximal sensing technique gaining popularity in pavement structural condition monitoring and thickness estimation. In this work, data collected using a 1.5 GHz ground-coupled GPR system is used to estimate asphalt layer thicknesses for a 3 km long tollway in Queensland, Australia. An automated adaptative fuzzy inference system is proposed to evaluate pavement conditions. Specific parameters need to be considered before feeding inputs to the fuzzy block. The segmentation of a large section is based on mean, standard deviation, and variation in thicknesses. The inputs to the fuzzy module are boundary limits in thickness variations and thickness counts that fall within the standard distribution curve. The fuzzy module uses Mamdani fuzzy inference with triangular and trapezoidal membership functions. The rules are designed to determine the priority of the expert system, which is input dependent. The output from the fuzzy module is a pavement condition classification rating which is a pavement performance indicator. Successful implementation of this algorithm is envisaged to benefit the pavement engineers in planning rehabilitation and maintenance of existing infrastructure.

Nikhil Singh, Kaushal Kishore, Ravin Deo, Ye Lu, Ernesto Urbaez, Jayantha Kodikara
Climate Change: Evaluation of a Failed Roadway Embankment with Expansive Soils Using Unmanned Aerial Vehicle (UAV) Inspection

The application of unmanned aerial vehicles (UAVs) for monitoring infrastructure conditions has been a major focus of many transportation agencies across the United States. Monitoring of geotechnical assets such as slopes, embankments, roadways, and other earth structures poses challenges due to their extent and access. The increase in the applications of UAVs for monitoring those assets can be attributed to their ability to quickly and safely access hard-to-reach locations and to cover large areas. This paper presents a case study of a roadway embankment that failed due to the issues caused by the wetting-drying cycles of problematic clayey soils caused by the climate change phenomenon. Due to this, shallow slope failures are becoming more common in expansive soil slopes. Understanding the soil conditions leading to failure is critical for designing resilient embankments. For this case history, a 3D model of the failed embankment built from optical images was used to obtain the embankment slope before and after failure. Both pre-and post-failure surface geometries were used in conjunction with a range of field conditions to estimate the material properties for the embankment slope at the time of failure. The material properties of the embankment at failure were estimated through two- and three-dimensional back analyses. The approach adopted in this study is expected to provide better insight into soil slope failures, and the design of slopes with clayey soils.

Surya Sarat Chandra Congress, Omar Ulloa, Prince Kumar, Navid H. Jafari, Xinbao Yu, Anand J. Puppala
Evaluation of the Temporal Moisture Variations in Flexible Pavements with Thin Seals Under Melbourne Climate

It has been identified that the accurate prediction of temporal moisture variation in pavements throughout the service life under prevailing climatic conditions could enhance the current pavement design in Australia to develop a more economical and safer pavement design. Thus, this paper presents a practical approach to predict temporal moisture variations in terms of degree of saturation (DOS) at different depths of pavement layers in the unbound pavement with thin sprayed seals by incorporating climatic factors. The numerical modelling of moisture, vapour and heat flow through unsaturated media was employed to obtain the DOS variation at each depth in pavement layers. A one-dimensional finite element model was developed to simulate the moisture movements in a typical pavement under North Melbourne climate using Hydrus 1D, an open-source software package. The model developed was then employed to evaluate the DOS variation in each layer. Furthermore, this paper discusses how the model developed can be utilized to advance the current pavement design by incorporating temporal moisture variations.

Chathuri Maha Madakalapuge, Troyee Tanu Dutta, Jayantha Kodikara
Intelligent Metro Shield Tunnel Structure Assessment Based on Knowledge Graph

With urban metro being put into service, various structure defects, such as dislocation, leakage, crack, spalling, gradually happen. Traditionally, engineers or experts are required to inspect and assess the tunnel structure on site regularly. But most of the assessment are subjective and qualitative, failing to achieve reasonable assessment and accurate tunnel maintenance operation. Therefore, some quantitative methods, such as Tunnel Servility Index (TSI), are proposed. However, TSI method only assesses the current condition of a tunnel, without considering its historical monitoring data. With the development of geotechnical engineering, large amount of monitoring data has been recorded. These data are multi-source heterogeneous, making it rather difficult to store and analyse. Database technology, especially Knowledge Graph (KG), is good at storing, managing and mining multi-source heterogeneous data. Based on KG, all data related to tunnel structure can be stored and then taken into account when making assessment; besides, the information of a tunnel from its construction to the present can be recorded in the knowledge graph. In the paper, monitoring data of Shanghai Metro Line 1 is stored in Neo4j, a graph database, to form the metro tunnel knowledge graph. Based on the KG, the metro tunnel is assessed by a dynamic assessment model based on TSI method. The hided statistic relationships among defects are also deduced. Through the application of KG on Shanghai Metro Line 1, the dynamic assessment model is proposed to be effective, reliable and advanced.

H. J. Pang, S. Y. Li, L. F. Dai, J. T. Kong, M. K. Liu, F. Jia, Y. D. Xue
Laboratory Investigation of Sensors Reliability to Allow Their Incorporation in a Real-Time Road Pavement Monitoring System

Unlike other engineering structures, road pavements have a greater monitoring complexity due to their heterogeneous composition and the diverse and increasing loads they are subjected to, hindering the preventive maintenance operations. For that reason, real-time monitoring systems are extremely useful to measure strains/displacements and temperature. Most of the currently applied systems use electric sensors, known as strain gauges, but a new monitoring technology has gained popularity in the last decades. Fibre Bragg grating (FBG) is an optical sensor with great potential that has been introduced on monitoring systems. The attempts to include FBG sensors in pavement monitoring have shown that further investigation is needed. Some factors to be studied are the interaction between the sensor and pavement, the use of coatings, the application method, and the influence of external conditions (e.g., temperature). This article presents preliminary laboratory work on FBG optical sensors vs electric sensors as part of the Rev@Construction project that aims to digitalise the construction industry in Portugal. This work will be essential to assure the reliability of FBG sensors before they are installed in a pavement monitoring system for a highway section. Through a series of controlled strain tests in a four-point bending apparatus, it was possible to conclude that the FBG sensors have a better quality signal than electrical strain gauge (SG) sensors. Furthermore, FBG sensors are not affected by magnetic fields, a clear advantage compared to SG sensors. The importance of temperature calibration on FBG sensors was also demonstrated in this work when analysing the data collected.

Francisco Rebelo, Asmasadat Dabiri, Hugo Silva, Joel Oliveira
Lessons Learned: Monitoring Dam Infrastructure Assets Using Unmanned Aerial Vehicles (UAVs)

Embankment dams are built with various compositions of soil, sand, rock, and clay to hold excess water and help in channelizing its flow. They play a key role in suppressing the floods and safeguarding the lives and property in the surrounding areas. Hence, they are classified as critical infrastructure. The increase in the frequency of high-intensity rainfall events and varying soil characteristics warrant the frequent inspection of these vast infrastructure assets. Traditional inspections use ground-based operations that require more human power, time, and are cost-intensive. The advancements in unmanned aerial vehicle platforms and lightweight sensors have provided a solution to conduct proactive monitoring of these assets. This study inspected two dam assets, embankment and spillway, using a low-cost UAV for conducting condition assessments. Multiple sensor data was collected to conduct qualitative and quantitative assessments of their conditions. The dam embankment was mapped to identify any surficial failure and seepage. The spillway was mapped to build a digital model, which provided an immersive experience to the inspector. There were many lessons learned during the inspection of a vast area like an embankment and a vertical structure like a spillway. This study outlines those best practices and provides a guide for proactive monitoring of these dam infrastructure using unmanned aerial vehicles.

Surya Sarat Chandra Congress, Anand J. Puppala, Louie Verreault, Dorota Koterba, Jason Gehrig
Rail Track Monitoring Using AI and Machine Learning

Monitoring of high speed railway track is the focus of this paper. To achieve a more efficient and targeted maintenance schedule, engineers have identified different methods of evaluating the safety of tracks. Research and testing off-track has been successful in evaluating track components. Testing on-track is key to developing a holistic over-view of a track. This paper reports a literature review followed by a successful pilot study of on-track testing using geometry measurements collected from geometry cars/moving measurement trains. The pilot study reported herein looks at a larger data set from railway lines and aims to identify similarities in the response of their geometry. Data analysis techniques used include the following: Statistical Models – deterministic, stochastic, and probabilistic models; Artificial Intelligence (AI) and Machine Learning (ML) techniques - Artificial Neural Networks (ANN), Support Vector Machines and Random Forest. The pilot study has successfully demonstrated that these tools, programmed in Python, will address the effect that different factors have on the wearing of the track and the scheduling of targeted cost- effective maintenance intervention.

Konstantin Popov, Robert DeBold, Hwa-Kian Chai, Michael C. Forde, Carlton L. Ho, James P. Hyslip, Paul Long, Sin Sin Hsu
Structural Health Monitoring with Artificial Neural Network and Subspace-Based Damage Indicators

In recent years, different structural health monitoring (SHM) systems have been proposed to assess the actual conditions of existing bridges and effectively manage maintenance programmes. Nowadays, artificial intelligence (AI) tools represent the frontier of research providing innovative non-invasive and non-destructive evaluations directly based on output-only vibration measures. This is one of the key aspects of smart structures of the future. In the current study, an artificial neural network (ANN) method has been proposed in order to perform damage detection based on subspace-based damage indicators (DIs) and other statistical indicators. A numerical case study example has been analysed with simulated damaged conditions. Based on a comparison between a reference situation and a new one, the greatest advantage in adopting these particular DIs is because they are able to point out significant changes, i.e. possible damage, without requiring a beforehand modal identification procedure, which may introduce further noise and modelling errors inside the traditional damage detection process.

Marco M. Rosso, Angelo Aloisio, Raffaele Cucuzza, Dag P. Pasca, Giansalvo Cirrincione, Giuseppe C. Marano
Using a Novel Instrumented Roller to Estimate Soil Dry Density During Compaction

Capturing evolution of density or void ratio during the compaction of geomaterials (soils and unbound granular materials) is essential for improved performance. This study developed a framework where the density evolution during compaction can be estimated using advanced instrumentation. The framework’s suitability was validated using a simulated large-scale soil box (dimensions: $$7.5\,{\rm m}\,\times\,4\,{\rm m}\,\times\,0.8\,{\rm m}$$ 7.5 m × 4 m × 0.8 m ) experiment mimicking the field conditions. Well-graded sand was compacted in 5 layers of 125 mm using a 1.5-tonne mini roller instrumented with Light Detection and Ranging (LiDAR) systems and a total station tracking system for positioning.The sand’s moisture content was homogenised at 8% (w/w) using a concrete truck. The in-situ sampling for measuring density was carried out using Nuclear Density Gauge (NDG) and sand cone test. The data from sensors were collected using a Data Acquisition (DAQ) system connected to a laptop. The measurement of the deformation in real-time provided an opportunity to estimate the density in real-time, and it was estimated using a machine-learning artificial neural network (ANN) model. The estimated density from deformation measured and NDG at the end of compaction shows that estimated density NDG density with an R = 0.9 for one layer, and for other layers, R was more than 0.8. This novel instrumentation allows the density to be measured during compaction with very high accuracy, which has a massive advantage over conventional approaches and contribute to the true Intelligent Compaction (IC) with an advancement of automation in construction.

Amir Tophel, Jeffrey P. Walker, Troyee Tanu Dutta, Jayantha Kodikara
Backmatter
Metadaten
Titel
Trends on Construction in the Digital Era
herausgegeben von
António Gomes Correia
Miguel Azenha
Paulo J. S. Cruz
Paulo Novais
Paulo Pereira
Copyright-Jahr
2023
Electronic ISBN
978-3-031-20241-4
Print ISBN
978-3-031-20240-7
DOI
https://doi.org/10.1007/978-3-031-20241-4