Journal of Civil Engineering and Management
The Journal of Civil Engineering and Management publishes original research that seeks to improve civil engineering competency, efficiency and productivity in world markets. More information ...
Copyright © 2021 The Author(s). Published by Vilnius Gediminas Technical University. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
An intelligent construction system based on digital twin and foundation model optimization
Construction sites routinely face multi-trade concurrency, spatiotemporal coupling, and high safety risk; relying solely on manual inspection and heuristic scheduling often leads to lagging detection and inconsistent execution. In response, recent practice has introduced digital twins (DT) to fuse video, sensors, and BIM and thus improve site visibility; however, most implementations remain at monitoring/visualization, lacking a mechanism to convert cognition into executable, verifiable decisions. Meanwhile, Transformer foundation models show strong capabilities in multimodal perception and representation learning, yet they are rarely closed-looped with engineering constraints and on-site execution. Against this backdrop, taking high-rise self-climbing platform (SCP) operations as a representative scenario, we build a DT×Transformer closed-loop system. We align video/sensor/BIM/text at the component level via “Component-ID + Timestamp”, train a multimodal Transformer for operation-state recognition and short-horizon risk prediction, and then explicitly encode safety, resource, and spatial precedence constraints in a policy module to generate feasible task sequences, which are delivered to crews via AR with acknowledgments to close the loop. The system integrates multisource perception, digital twin, foundation-model reasoning, and AR-assisted execution, and was validated on a highrise self-climbing platform project for its overall improvement of construction performance. The evaluation covered four key aspects – safety management, operational efficiency, communication and execution, and information transparency. Results show that the system significantly extends the lead time of risk warnings, reduces violation rates, stabilizes construction rhythm, shortens decision latency, and markedly improves the consistency between instruction delivery and on-site feedback.
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
Transforming railway transportation: the role of emerging technologies in efficiency, safety, and sustainability
This review explores the transformative role of emerging technologies in railway transportation, emphasizing their contributions to efficiency, safety, and sustainability. With the integration of Artificial Intelligence (AI), the Internet of Things (IoT), Digital Twin technology, and autonomous systems, the railway industry is transitioning towards intelligent and interconnected networks. These advancements address critical challenges such as predictive maintenance, energy optimization, and real-time decision-making, ensuring operational resilience and enhanced passenger experiences. The review methodically evaluates 199 studies, offering insights into regional and temporal trends, and highlighting innovations in automation, safety systems, and sustainability. Additionally, it examines the interplay between advanced technologies and environmental goals, underscoring the importance of green practices and resource efficiency. Despite significant progress, challenges in cybersecurity, regulatory compliance, and legacy infrastructure integration persist. By categorizing literature into thematic domains and identifying critical research gaps, this study provides a comprehensive roadmap for future advancements in intelligent railway systems. Ultimately, it positions emerging technologies as pivotal to addressing contemporary demands and fostering a sustainable and adaptive global railway network.
Physics-informed neural network–integrated point cloud decimation and 3D mesh modeling for thermo-mechanical simulation of rail fasteners in digital twin systems
High-density point cloud data of infrastructure components, such as railway fasteners, often contain excessive noise and redundant points, making them challenging to process for simulation and analysis. This study introduces an integrated framework that transforms raw point clouds into simulation-ready 3D meshes and couples them with PhysicsInformed Neural Networks (PINNs) for thermo-mechanical analysis, enabling applications within digital twin environments. The pipeline begins with a RANSAC-based iterative segmentation and outlier removal, achieving an 82.35% reduction in point count while preserving essential geometry. Poisson surface reconstruction and targeted post-processing then produce a high-fidelity, watertight mesh normalized for consistent simulation input. Leveraging this mesh, PINNs solve the steady-state heat equation to model thermal conduction and linear elasticity equations to estimate stress and displacement fields under defined Dirichlet boundary conditions. The resulting temperature and stress distributions are visualized directly on the mesh, providing interpretable, physics-consistent insights into fastener performance. By unifying geometric simplification with data-driven, physics-aware simulation, this approach supports accurate and computationally efficient digital twin development for railway asset monitoring and maintenance.
Recyclability and environmental impact of typical solid waste materials (rubber, RAP, SS) in asphalt mixtures: a state-of-the-art review
The utilization of solid waste material (SWM) in sustainable asphalt pavement is one of the key paths to achieving carbon peaking and neutrality. It is imperative to seek a balance between service performance and environmental impacts of sustainable asphalt pavement. This paper makes a state-of-the-art review on recyclability and life cycle assessment of SWM in sustainable asphalt pavements based on recent five-year literatures. According to scientometric analysis results, three typical SWM, crumb rubber (CR), reclaimed asphalt pavement (RAP) and steel slag (SS), are selected to be mainly discussed over the service performance and environmental impacts via life cycle assessment. Especially, influences of material properties or production design of SWM are critically highlighted, including the CR particle size and treatment methods, strategies of RAP to mitigate performance deterioration, and enhancement mechanisms of SS in asphalt mixtures. Further, the current major research gaps and application limitations associated with SWM utilization are concluded, including the trade-offs between the environmental impacts of CR production and the service performances, the uncertainties in binder blending and rejuvenation efficiency of RAP, and the volume expansion and heavy metal leaching of SS asphalt mixtures.
Evaluating the role of unmanned aerial vehicles to enhance construction project productivity
The construction industry, generating significant goods and services annually, remains one of the least digitalized sectors globally, with Malaysia’s construction industry particularly dependent on manual labor. This study investigates the implementation opportunities and challenges of Unmanned Aerial Vehicles (UAVs) in Malaysia’s construction industry to enhance productivity and efficiency. Through a mixed-method approach, the research combines qualitative data from 20 semi-structured interviews with quantitative analysis of 200 survey responses from construction professionals. Structural Equation Modeling identified four key opportunity categories: Site Surveying, Site Inspection, Constructing and Repairing, and Enhancing Manpower, Materials, and Machinery (3M) Coordination. The analysis revealed that Constructing and Repairing (0.62) showed the strongest influence on UAVs implementation, followed by Enhancing 3M Coordination (0.61). Five primary challenges were identified: Safety Concerns (0.74), Cost Challenges (0.69), Technical Difficulties (0.61), Regulation Factors (0.53), and Environmental Factors (0.47). The study developed a comprehensive framework for UAVs implementation in construction, highlighting critical success factors and barriers. Future research opportunities include investigating long-term economic impacts through longitudinal case studies and examining UAVs integration with Building Information Modeling systems and Radio-Frequency Identification to optimize technological coordination between aerial data collection and digital modeling.
Overview and analysis of safety management in prefabricated construction (2012–2022)
The stable development of the construction industry plays a significant role in national economic growth. With the continuous reform and progress of the construction industry, construction safety has become a vital issue of concern and emphasis. As an essential means for the sustainable development of the construction industry and industrialization of construction, prefabrication has the advantages of reducing waste of resources and improving construction safety and efficiency and is strongly promoted by the construction industry around the world. Although the safety management of traditional construction has been fully researched, the traditional safety management mode cannot be directly applied to assembled construction because of differences in their construction characteristics and management scopes. Thus, investigating the safety issues of prefabrication is necessary. Although numerous scholars analyzed safety in the different construction stages of assembled buildings, many connections and contents in the literature must be summarized, and systematic literature research must be conducted. Therefore, this study uses a systematic and comprehensive review approach to fill the research gaps. In the first stage, 107 papers related to prefabrication safety management published, meeting the requirements are selected through a literature search. In the second stage, scientometric analysis is conducted to identify the journal sources, scholars, regions, and keywords. In the final stage, content analysis is performed to achieve three main objectives: 1) to identify the differences between prefabricated and conventional buildings and summarize the characteristics of prefabricated buildings (PBs) to obtain the characteristics of prefabrication safety management, 2) to summarize the research topics, and 3) to discuss and provide signposts for potential research directions. This study provides a comprehensive knowledge framework that links current research areas with future research trends, thereby providing a multidisciplinary guide to researchers to gain insights from the latest research on prefabrication safety management.
Exploring the technology-organization-environment configurations to enhance stakeholder collaboration in the off-site construction projects
Stakeholder collaboration is critical in empowering off-site construction (OSC) projects to reduce the cost overrun, conflicts and quality problems. Prior studies have explored the factors affecting stakeholder collaboration but focused on the net effects, ignoring their interaction relations. Our study aims to explore the configuration conditions to enhance stakeholder collaboration in OSC projects using fuzzy-set qualitative comparative analysis (fsQCA). 103 valid questionnaires are collected to conduct fsQCA to explore the configurational conditions to achieve the high stakeholder collaboration. The findings reveal that there are no necessary conditions as a bottleneck factor constraining stakeholder collaboration emergence in the OSC projects. Three equivalent configurational conditions are identified to achieve high stakeholder collaboration, referring to environment-driven organizational configuration, technology-organizational configuration, and organizational-driven environment configuration. Our study enriches the literature on the antecedent identification of stakeholder collaboration in the OSC project. It also expands the underlying mechanisms between antecedents and stakeholder collaboration from the configurational perspective. Meanwhile, our study provides practical guidance for policymakers and project managers to make decisions by emphasizing the interaction effects of different project governance mechanisms.
Exploring the causes of contract disputes and their social network relationships in the megaprojects: a case of Jinan Airport Project
Contract disputes hinder the successful delivery of megaprojects. This study aims to identify crucial contract disputes and causes for dealing with contract disputes problems and improving megaproject performance. 107 contract dispute-related documents were collected and analyzed using a mixed method of text mining and social network analysis. Text mining was used to sort out emerging contract disputes and causes. Social network analysis was used to identify crucial contract disputes and causes by analyzing their network relationships. Our study obtained 12 contract disputes and 10 corresponding causes in the megaproject context. The results show that payment dispute is the most prevalent contract dispute in megaprojects. Our study also found that payment dispute and settlement dispute have high degree of centrality; incomplete survey and design content and unclear contract terms are critical causes as the bridging roles in the relationship network of causes in megaprojects. This study expands the literature on the identification of contract disputes and corresponding causes in megaprojects. Meanwhile, it provides practical guidance for project managers on designing contract items and dealing with contract disputes in megaprojects.
The impact of environmental turbulence on construction enterprise performance: unveiling the systemic response mechanisms
This study addresses the overlooked dynamic interaction between strategic agility and absorptive capacity in turbulent environments, particularly within project-based enterprises where knowledge flows are fragmented. By constructing an integrative framework, it examines how strategic agility influences enterprise performance via absorptive capacity under varying environmental turbulence levels. Using data from 198 Chinese construction industry practitioners and PLS-SEM analysis, results reveal that strategic agility dominates enterprise responses, with absorptive capacity mediating this effect. Notably, in high turbulence, the mediation path buffers the negative impact. The study advances organizational adaptability theory and offers guidance for capability development in turbulent contexts.
Influence of institutional isomorphism on unethical pro-organizational behavior of construction teams based on the mediating effect of organizational routines
Unethical behavior that favors the organization at the expense of unethical conduct is considered an example of unconventional motivation. Unethical pro-organizational behavior (UPB) reduces the project quality and safety in the construction industry and may also lead to increased construction costs and investment losses. By conducting a crosslayer analysis and administering a questionnaire survey with 352 construction team employees, this study examines the influence of institutional isomorphism on UPB and the path between them. Moreover, the study explores the mediating effect of organizational routines. Results indicate that institutional isomorphism has different effects on implicit and explicit routines. Organizational routines play a crucial role in institutional isomorphism and UPB. Several mediating paths exist between institutional isomorphism and UPB. Furthermore, UPB is mainly implemented by strengthening the mediating effect of implicit routines and weakening that of explicit routines.
Factors influencing sustainable construction in Egypt
The concern about sustainable development has become a global goal, and as such, the United Nations has adopted the seventeen sustainable development goals that are targeted to be achieved by 2030. Furthermore, to address the energy and Greenhouse gases (GHG) crisis, the world has moved towards developing policies concerned with rationalizing energy consumption and reducing GHG emissions. The main idea of sustainable construction is to avoid the depletion of natural resources for both the present and future generations. Therefore, this study aims to investigate the factors that prevent and delay the adoption of sustainable construction practices in Egypt. Using the survey method, a questionnaire was filled out by professionals and practitioners in Egyptian construction projects. The findings of this study indicate that the most critical factors affecting sustainable construction in Egypt are the “poor market demand for green and recyclable construction materials”, and “fear of higher investment costs”. The study recommends raising awareness to overcome these factors.
Dynamic risk assessment of international engineering projects from the life cycle perspective
Compared to other types of engineering projects, the development of international engineering projects (IEPs) is particularly challenging due to the uncertainties and risks posed by external environments. The risks of IEPs arise from both external threats and inherent vulnerabilities. As projects progress, these risks evolve dynamically, posing significant challenges for risk management. Existing studies on IEPs have focused mainly on the static assessment of risks at a certain stage or types of risk, but few have considered the dynamic and interconnected nature of risks. This study employs a comprehensive risk assessment framework for IEPs using dynamic Bayesian network. Specifically, the fuzzy set method and Monte Carlo simulation create the prior probabilities of root nodes and conditional probabilities of intermediate and leaf nodes. Then, propagation analysis is conducted to uncover the dynamic evolution of risks throughout the project life cycle. The proposed framework can capture the evolution of critical risks in life cycle. Internal and external risks in the three phases of construction, operation, and maintenance have been identified. Based on the findings from a case study, targeted management strategies are proposed to address critical risks, providing practical guidance for project managers to optimize risk management practices.
YOLOv11n-CDL: accurate and lightweight pavement defect detection via enhanced multi-scale attention and feature fusion
Pavement defect detection requires both high accuracy and real-time performance in complex road environments, yet existing lightweight models often struggle with blurred textures, background interference, and small cracks. To address these limitations, this study proposes YOLOv11n-CDL, an enhanced lightweight detector integrating three targeted improvements. First, the ConvSmart module expands the receptive field and strengthens multi-scale feature extraction, improving the representation of defects of varying sizes. Second, a Double-Stage Attention (DSA) mechanism, embedded at the deepest backbone stage, iteratively highlights discriminative crack patterns while suppressing shadows, markings, and texture noise. Third, a P2-level small-object detection path provides high-resolution features that significantly improve sensitivity to fine cracks and micro-potholes. Experiments on IRRDD show that YOLOv11n-CDL achieves 75.3% mAP@0.5 and 44.6% mAP@0.5:0.95, outperforming the baseline by 3.0 and 1.1 percentage points, and exceeding YOLOv8n and YOLOv7-tiny in both precision and recall. Additional results on RDD2022 and low-power devices confirm strong generalization and real-time deployability. These improvements demonstrate that YOLOv11n-CDL offers an effective balance between accuracy, robustness, and efficiency for practical pavement inspection applications.
Determining safety-related critical success factors in petrochemical projects through an extensive fuzzy risk assessment approach
The construction phase of petrochemical units encounters many risks, a large number of which leads to death and disabling injuries. The purpose of this study is to provide a framework to determine safety-related critical success factors (CSFs) in petrochemical construction projects through risk assessment. The proposed approach involved two phases. At first, 15 potential risks were identified through a review of previous studies and interviews with experts. Then a combination of failure mode and effect analysis (FMEA), criteria importance through inter-criteria correlation (CRITIC) and technique for order preference by similarity to the ideal solution (TOPSIS) in a fuzzy environment was used for risk assessment, and the risks were classified based on their score. In Phase 2, safety-related CSFs were identified and ranked using the solutions provided for each risk and by consulting with experts. Results showed that falling from height and structural collapse due to excavation operations were the most and least important risks, respectively. Top management support was identified as the most important factor amongst the safety-related CSFs. The findings of this paper assist petrochemical construction project managers to adopt a structured approach for the risk assessment and determination of safety-related CSFs in their projects.
An integrated vision-based dynamic collision risk assessment framework of workers and mobile machinery on construction site
The concepts of environmental impact index and spatial conflict degree have gained prominence in enhancing the controllability of collision accidents and mitigating the likelihood of collisions during construction processes. Nevertheless, prior studies predominantly focused on exploring collisions in terms of proximity and congestion between pairs of entities, thereby overlooking a comprehensive consideration of workers, construction machinery, environmental factors, and the spatial interaction of specific activities.To this end, this study aims to propose an integrated vision-based dynamic collision risk assessment framework by setting workers and mobile machinery as targeted research objectives and embedding a comprehensive risk assessment model in the proposed framework, thereby comprehensively assessing four types of risk factors (i.e., proximity, congestion, environmental impact index, and spatial conflict), and visualize the hierarchy of risk warnings. Firstly, a comprehensive risk assessment model was developed by using the fuzzy comprehensive evaluation method. This is followed by developing a dynamic risk assessment framework to extract the spatial information of the monitored objects by using computer vision as the underlying data of the risk factors. Finally, the proposed integrated framework was validated by an experimental study. This experiment’s safety risk assessment results are consistent with expectations, which largely illustrates the effectiveness of the evaluation model constructed in this paper. For the vision module, the accuracy of classification and monitoring is more than 95%, and the speed of the object detection algorithm to process the video is about 10 frames per second, which shows the feasibility of this study.
Integration of Lean Six Sigma into earned value management using system dynamics
A Few Lean Six Sigma (LSS) papers were studied during the construction project’s progress. This study proposes the integration of LSS, system dynamics (SD), and earned value management (EVM) as a comprehensive toolkit for datadriven decision-making. Significant waste- and quality-related interdependencies were identified using DEMATEL techniques with 27 Saudi construction experts. SD with two interdependent models of waste causes (WCs) and quality causes (QCs) were utilized to assess the project’s performance. Two metrics were employed: the sigma rating and the value-added ratio in the EVM generated by the developed SD model. The findings indicated that eliminating WCs had little impact on enhancing the project performance in the early stages of the project. The impact increased with the progress of the project. The improvement of the project quality was minimal for “increases of errors and omissions in design documents” and maximum for “increasing morale and attitude affect the quality of the project”.
Developing a data-driven failure decision-making framework for sustainable urban water management projects
Several degradation factors significantly impact the durability of water pipes in urban areas. However, limited research has comprehensively prioritized these factors to support data-driven maintenance and renewal decisions. Addressing this gap, this study identifies and ranks the failure factors affecting water pipeline infrastructure. A survey of 125 Egyptian water industry experts was conducted, and the collected data were analyzed using partial least squares-structural equation modeling as the decision-making framework. By incorporating insights from previous research and expert opinions, the research developed a robust failure decision-making model that provided significant insights into the primary factors contributing to water pipeline failures. Model analysis revealed that the “operational factor”, with an impact value of 0.543, was the most critical group of factors affecting pipeline failure. Following closely with an impact value of 0.480, was the “static factor”. Natural disasters (0.373), climate and weather conditions (0.325), and soil conditions (0.300) also contributed considerably. Following closely were “dynamic loads” (0.276), “aging and environmental factors” (0.250), and “third-party factors” (0.200), which had the least impact on the failure of the pipeline. This study has developed a novel failure decision-making model by synthesizing insights from previous studies, expert opinions, and empirical data on water pipeline failure.
Factors affecting knowledge sharing in blockchain technology in the Chinese construction industry
With the emergence of blockchain technology, knowledge sharing has become increasingly important for gaining a competitive advantage in any industry. However, it has not been fully implemented in the construction industry. This paper examines the factors influencing knowledge sharing in blockchain technology, using the Chinese construction industry as a case study. First, the study identified the key factors affecting knowledge sharing through a literature review and construction market research, classifying them based on the technology–organization–environment (TOE) framework. Next, the study employed focus group discussions to explore the relationships between pairs of factors and applied the interpretive structural modeling-matrix of cross-impact multiplication applied to classification (ISM-MICMAC) method to evaluate and prioritize these key factors. Finally, based on the empirical analysis, the paper discusses measures to promote knowledge sharing in the construction industry. This study contributes to the academic understanding of the factors influencing knowledge sharing and offers a fresh perspective for future research. Additionally, it provides practical insights to help the construction industry gain a competitive advantage and achieve sustainable development.
IOT-enabled framework for monitoring carbon emissions in the materialization phase of modular integrated construction
The potential of Modular Integrated Construction (MiC) to reduce carbon emissions (CEs) has led to increased attention on developing rigorous monitoring systems. Existing methods predominantly capture CEs of isolated MiC stages, overlooking nuances of arrangeable and reusable activities, thus hindering effective CE control measures. To overcome this limitation, this study develops an Internet of Things (IoT)-enabled framework for monitoring CEs of MiC, specifically focusing on the materialization phase, integrating across various MiC stages and facilitating detailed monitoring of CEs associated with arrangeable and reusable activities. This study builds up a CE measurement model tailored to the MiC materialization phase, providing a computational basis for a subsequent monitoring framework. By leveraging IoT technology, the framework is evaluated through case studies to confirm its feasibility and efficacy. The results indicate that the framework enabled improved monitoring capabilities, and the CEs of many MiC activities are successfully integrated into the system. The case study analysis demonstrates that the system's feedback-driven adjustments achieved a CE reduction of 732.04 metric tons.
Autonomous modular construction strategy using robotized crane based on deep learning and reinforcement learning
Modular construction offers significant advantages including faster construction time, higher quality control and less environmental impact. To further enhance its advantages, advanced robotic construction technologies are being developed. This research develops an automated modular construction framework that incorporates the robotic kinematics, deep learning and deep reinforcement learning using a robotized crane. The proposed modular construction strategy utilizes YOLOv5-S for modular container identification and localization. An improved proximal policy optimization (PPO-I) is developed and implemented in this strategy for collision-free three-dimensional (3D) lifting path planning and modular container transportation. States and rewards of the PPO-I and robot kinematics design of a real mobile crane are developed. The feasibility of the proposed modular construction strategy is verified through four case studies in 3D virtual environments. More than 97% success rate is observed meaning that the proposed strategy can be implemented in the robotized crane to localize the modular container and transport it to the target position with collision avoidance. The results indicate the potential of the proposed robotic-assisted modular construction strategy in the field of automated construction.
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
Identifying cyber risk factors associated with construction projects
As construction projects adopt increasingly interconnected digital technologies, their cyber-attack surface expands, making comprehensive cyber risk management essential to prevent incidents, mitigate risks, and minimize potential losses resulting from such attacks. However, the necessary risk factors for this purpose are lacking. Therefore, the study aims to develop a comprehensive set of project-level cyber risk factors tailored to the complexities of construction projects, identified through a systematic and flexible seven-step methodological framework: (1) a literature review of construction and cybersecurity sources to identify initial factors; (2) initial definition of risk categories; (3) internal evaluation and expert input to refine these factors; (4) distribution of a detailed expert questionnaire for rating; (5) expert evaluations through meetings and feedback sessions to enhance validity; (6) elimination of lower-scoring factors; and (7) establishment of quantitative scales for precise risk assessment. The findings include the 32 identified risk factors into five groups: project information, project structure, information technology (IT), operational technology (OT), and management and human aspects. The contributions include providing a set of risk factors that serve as cybersecurity management references and inputs for future quantitative risk assessments, offering a checklist used for proactive risk management, and introducing a framework adaptable for identifying factors of other risks.
The digital organization transformation (DOT) model: bridging digital transformation and organizational structures in construction firms
Digital transformation is a pathway to improve productivity in the construction sector, but organizational structures still need more adaptation. There are no fully developed models at the level of factors and evidence between organizational structures and digital transformation. This paper analyzes the impact of digital transformation on organizational structures within the construction sector, utilizing a semi-structured interview and field inspection methodology to validate key organizational factors and evidence influencing this process. The research focuses on Colombia’s medium and large construction companies and aims to validate an integrated conceptual model of digital transformation and organizational structures. Results indicate that while technological tools have been adopted, the full potential of digital transformation still needs to be explored. The study highlights the factors and evidence that affect the variables of digital transformation and organizational structures. While technological tools are essential, they are unlikely to create a long-term competitive advantage. This research advances the understanding that the integrated implementation of digital transformation and organizational structures, grounded in validated factors and evidence, can drive enhanced decision-making, reduce inefficiencies, and improve productivity within the construction industry.
A systematic review on safety practices in high-rise building construction: benefits, barriers, and strategic improvements
High-rise building construction has long been considered one of the most dangerous jobs globally. However, there is currently a lack of comprehensive systematic reviews that approach the implementation of safety practices in high-rise construction. This study aims to fill the following research gaps: first, to understand the significance and identify the barriers faced to implementing safety practices in high-rise construction; second, to propose a framework to improve safety performance. A total of 109 articles from databases were thoroughly reviewed in this study. The research identified six categories of benefits, five types of safety barriers, and 15 strategies proposed to overcome these barriers. In terms of the number of sources, “reduced accident rates” and “improved organizational reputation” were identified as the top benefits in terms of the significance of safety practices. “Lack of health and safety training” was deemed the largest barrier. To address these challenges, this paper explores the strategies and risk mitigation measures from the perspectives of six stakeholders: regulatory agencies, government, owners, contractors, consultants, and workers. The findings suggest that establishing strict market entry mechanisms are fundamental while strengthening worker safety training and conducting regular safety inspections and real-time site monitoring are the most effective methods.
Optimising scheduled maintenance on operational buildings: a microservice-based BIM framework
Operation and Maintenance (O&M) aims to preserve the quality of the building throughout its life, keeping maintenance costs within acceptable limits. Maintenance involves different tasks, from replacing air conditioning filters to restoring structural elements. Each task has an optimal frequency, which can be flexible within a specific time range, a cost, and a duration. These maintenance activities may disrupt building operations by repeatedly interrupting ongoing activities. This research seeks to reduce these disruptions by grouping tasks within reasonably close time frames to schedule preventive maintenance plans while respecting their frequency. We propose an optimisation model, solvable using a general-purpose solver, which identifies the best time range for grouping O&M tasks. By penalising deviations from the optimal period, the model ensures that tasks are performed at the most cost-effective time. Integrated within a microservice-based architecture, the optimisation engine seamlessly links an input database and a BIM model, orchestrated using Dynamo for Revit. A case study illustrates the effectiveness of this system, consolidating multiple tasks into optimised work clusters and significantly reducing operational disruptions. The originality of this work lies in its innovative combination of optimisation techniques and BIM tools, providing a practical and scalable solution for efficient O&M management.
Prediction of cost contingency in construction projects by introducing machine learning algorithms
Construction projects are bound by uncertainties and changes by its nature. Thus, cost contingency needs to be allocated to construction project budget to cope with any deviation of actual costs from planned ones. However, existing methods for predicting cost contingencies, as studied and practiced, still present limitations in reliability and accuracy. Machine learning (ML) has gained popularity for enhancing prediction power in various fields. The paper aims to examine various ML algorithms to implement a cost contingency prediction model, employing both continuous and categorical predictor variables. To develop the model, construction transportation project datasets, which were bid between 2013‒2017, were collected from the Florida Department of Transportation (FDOT) website. To address imbalanced regression dataset issues, the synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN) algorithm is introduced. ML random forest (RF) regression associated with random search hyperparameter optimization, achieved remarkably accurate predictions compared to extreme gradient boosting (XGBoost) regression and artificial neural network (ANN) models. The results also demonstrate that four parameters are significant factors in predicting construction cost contingency: project amount, project duration, and latitude and longitude factors. These findings provide new insights for researchers in developing models and for practitioners seeking more advanced method.
Collaborative analysis model of construction schedule and quality for RCC dam based on DES and deep learning
Construction schedule simulation is an effective method to analyze the progress of concrete rolling construction and its simulation accuracy is an important indicator that project managers care about. However, existing roller compacter concrete construction simulation research pays less attention to the impact of construction process changes caused by compaction quality on simulation accuracy. Based on the real-time monitoring system of concrete rolling construction, this paper establishes a simulation model of concrete rolling construction considering compaction quality. The model firstly reconstructs the property parameters of roller compacted concrete using the improved generated adversarial network, secondly, considering the nonlinear correlation characteristics of parameters affecting compaction quality, a concrete compaction quality analysis model based on improving grey wolf optimized convolutional neural networks (IGWO-CNN) was built. Finally, the intelligent analysis model of compaction quality was embedded into the simulation model of concrete rolling construction. Based on real-time monitoring data, the simulation model was driven and the simulation process was adjusted adaptively. Taking the construction of a roller compacted concrete dam in China as an example, the validity and superiority of this model are proved.
improved generative adversarial networks
improving grey wolf optimized convolutional neural networks
Advancing civil infrastructure with digital twins: a review of applications and challenges
The digital twins (DTs) technology has emerged as a ground-breaking approach in the management and maintenance of civil infrastructure, providing a virtual representation of physical systems which are continuously updated with realtime data from IoT sensors and simulations. Initially introduced in the manufacturing sector, the concept of digital twins has been extended to civil engineering, offering a significant potential for real-time monitoring, predictive maintenance, optimized asset management, and enhanced decision-making. This paper provides a comprehensive survey of the applications of the digital twins technology in civil infrastructure, with a particular focus on structural health monitoring (SHM), predictive maintenance, smart city frameworks, and disaster response systems. By reviewing existing methodologies, case studies, and practical implementations, this paper highlights the transformative impact of DTs in improving the efficiency, safety, and sustainability of infrastructure systems, including bridges, buildings, and transportation networks. Despite the numerous advantages of DTs, several challenges impede their widespread adoption in civil engineering. These challenges include high implementation costs due to the need for sophisticated sensors, high-performance computing, and advanced simulation tools. Additionally, data integration and interoperability issues between various data sources and platforms hinder seamless adoption. Cybersecurity risks associated with real-time monitoring systems and the protection of critical infrastructure are also discussed. This survey identifies these barriers and outlines the necessary technological advancements which may help overcoming the barriers. These include standardized data formats, enhanced AI-driven predictive models, and scalable cloud solutions, among others. This paper concludes by highlighting future research directions to address the identified challenges, emphasizing the need for collaboration across academia, industry, and government to fully unlock the potential of DTs technology. With continued advancements in machine learning, edge computing, and secure data protocols, DTs are poised to revolutionize infrastructure management, contributing to smarter, safer, and more efficiently built environments.
Quotation impact factors for general contractors: a viewpoint from private sectors in Taiwan
The quotes provided by private sector entities to the project’s general contractor vary based on actual considerations. This research aims to identify the factors that influence these quotes for general contractors. Through an extensive review of literature and interviews with experts, four main aspects were identified, encompassing a total of 38 impact factors related to quoting patterns. A survey was conducted among construction professionals, resulting in 123 valid responses out of 146 distributed surveys. The survey’s validity was confirmed with a Cronbach’s Alpha value exceeding 0.7. Subsequently, a factor analysis was performed, confirming 38 impact factors and their respective weights. The results from the Principal Component Analysis (PCA) offer valuable managerial insights, highlighting the most impactful factors: external demand, corporate adjustment, and financial condition. These findings provide practical guidance for practitioners. These insights encompass areas such as information sharing, project limitations, and subcontractor capabilities. Contrary to stereotypes presented in prior research, factors such as reputation and social connections were found to not significantly impact quotation outcomes.
Evaluation model of construction quality data uncertainty: a study based on industry park project
This study proposes an evaluation and adjustment model for construction quality data uncertainty based on the research project of the Sichuan Provincial Building Industry Park. The core objective is to address the variability in concrete strength data caused by multiple factors during the construction process through a data-driven approach, which enables the accurate prediction of concrete strength. By analyzing construction quality data across different strength grades and curing ages, this study establishes a dynamic model centered on “data fitting” and “data prediction”. The model defines a reliability range for construction quality specific to the project, which facilitates the cleansing of outlier data and the adjustment of biased data. The results demonstrate that the model significantly enhances the reliability of the construction quality data. Further analysis of data from the same construction site revealed that it is highly applicable, and the date adjustment become more concentrated and stable. The fitting coefficient of this model approaches 1, which significantly improving the representativeness and reliability of the strength data. The findings provide a scientific basis for the dynamic assessment and optimization of construction quality, and robust support for quality control and prediction during the construction process.
Barriers and facilitators of promoting work-related injury insurance in the construction industry: lessons from China
Work-related injury insurance (WRII) is essential for protecting workers’ rights and enhancing workplace safety, yet its promotion in construction faces challenges. This study investigates WRII adoption barriers and facilitators in China’s construction sector using a mixed-methods approach. Semi-structured interviews with 14 industry experts were conducted, followed by thematic analysis to identify key barriers. An analytic hierarchy process was then applied to prioritize these barriers. The findings reveal six main barriers: (1) cumbersome claim process, (2) incomplete law and regulation system, (3) weak right-protection awareness of construction workers, (4) weak legal consciousness of contractors, (5) unreasonable compensation treatment and fund management, and (6) difficulties in employment relationship identification. To address these challenges, the study proposes five targeted measures: (1) simplifying the claim process, (2) promulgating authoritative and detailed laws and regulations, (3) strengthening supervision and law enforcement, (4) strengthening advocacy, and (5) improving insurance compensation and fund management. These findings offer practical guidance for policymakers to enhance WRII effectiveness in China’s construction industry and provide insights applicable to other developing countries. Despite limitations related to expert judgment and sector focus, the study offers valuable recommendations for future research on cross-industry comparisons and digital solutions for WRII.