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.
Ontology development using language model-based Named Entity Recognition for integrated construction information
Named Entity Recognition (NER) is crucial for building knowledge bases and facilitating semantic search in the construction industry. While conventional NER models can identify general entities such as spatial and organizational information, extracting domain-specific entities, like materials and dimensions from construction-related texts –particularly in Bill of Quantities (BoQ) and Building Information Modeling (BIM) parameters – remains challenging extensive manual annotation. Key entity categories were defined, and datasets from four BoQ and two BIM sources were annotated to establish ground truth labels. A semi-automated labelling process was introduced to streamline annotation and improve training efficiency. Experimental results demonstrate that the proposed framework reduces annotation time by nearly threefold compared to manual processes. This study developed a BERT-based NER model achieving F1 scores ranging from 0.81 to 0.97, with higher performance for well-defined construction parameters (name, material, size, thickness, diameter, length, type: 0.95–0.97) compared to miscellaneous text entities (0.81). Despite extensive research in construction NLP, existing approaches fail to address the integration challenges between heterogeneous BIM-BoQ data formats and lack domain-specific entity recognition capabilities. The extracted entities are aligned with standardized formats using semantic text similarity techniques. This ontology-based integration enhances data consistency, interoperability, and retrieval accuracy, improving semantic alignment while minimizing discrepancies from heterogeneous terminology.
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
Integrating Monte Carlo simulation and Digital Twin technology for advanced risk management in fast-track infrastructure projects
Fast-tracked infrastructure projects are prone to unforeseen factors that can disrupt commissioning schedules, due to quicker timelines, concurrent work, and changing site conditions. Existing risk analysis techniques have very limited flexibility to keep pace with real-time changes and do not sufficiently address the dynamic nature of interdependent activities within the project. The study aims to bridge this gap by formulating an integrated risk management mechanism using Monte Carlo Simulation (MCS) and a Digital Twin (DT), along with a tailored optimization module. The framework describes the risks associated with multiple work package overlaps while calibrating performance forecasts via DT’s feedback loop. MCS is used to simulate uncertainty and monitor cost-time impacts, while the optimization module helps to find the least detrimental overlap arrangement. Continuous field data sync with the simulation model enables proactive, data-driven decisions that improve situational awareness in the DT environment. Real project conditions show this MCS/DT approach improves prediction accuracy, reduces risk, and aids change during execution. The proposed framework serves as a pragmatic, adaptive risk management tool for fast-track projects while enhancing the integrity and resilience of the parent project as a whole.
ESG, open innovation, and firm performance in construction firms: exploring the mediating role of digital transformation
The construction industry faces growing pressure to reconcile financial performance with Environmental, Social, and Governance (ESG) commitments and open innovation strategies. Although ESG and open innovation are recognized as drivers of long-term corporate value, their impact mechanisms on firm performance, particularly the mediating role of digital transformation and the moderating role of financing constraints, remain insufficiently understood. This study addresses this gap by empirically analyzing Chinese listed construction firms from 2013 to 2021. Using multiple regression, bootstrap mediation, and moderation analysis, we examine how ESG performance and open innovation affect firm performance, and how digital transformation and financing constraints shape these relationships. The results reveal that both ESG performance and open innovation negatively affect firm performance in the short term, while open innovation improves ESG performance. Digital transformation partially mediates the relationships between ESG performance, open innovation, and firm performance. Financing constraints strengthen the positive effect of ESG on digital transformation and weaken the negative effect of open innovation on firm performance. Notably, all these relationships are significant only in state-owned enterprises, not in nonstate-owned enterprises. These findings illuminate the complex interplay among ESG, open innovation, digital transformation, and financing constraints, providing actionable implications for construction enterprise.
Automated progress monitoring in pipeline construction: a systematic review
Automated progress monitoring for pipeline construction is an evolving research domain among researchers which can provide effective visualisation and control of related projects. Although automation has been widely reviewed in building construction, a focused review on pipeline construction is lacking despite unique challenges and a growing need for automated monitoring. Hence, a systematic review of available methodologies and technologies was necessary to assess the achievement level of automation attained in progress monitoring practice in pipeline construction. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement, relevant studies in the area were identified by using five databases: Scopus, Web of Science, ScienceDirect, American Society of Civil Engineers, and Emerald. Keyword analysis was performed by developing a keyword network, and descriptive statistics were provided. The review examines a wide range of technologies and methods for automated progress monitoring, with a focus on data acquisition tools, monitoring techniques, and their integration within unnecessary article pipeline construction scenarios. A technological process overview was developed to outline the complete monitoring workflow, while a conceptual visual representation illustrated the potential impact of tools selection and integration strategies on successful project completion, and its broader impact on sustainability and economy.
Selection of optimal construction service provider: a combined decision framework with hyperbolic fuzzy data
This study introduces an integrated decision-making framework for Construction Service Provider (CSP) selection, addressing limitations in traditional methods that prioritize cost over quality and holistic evaluation. The framework integrates “Hyperbolic Fuzzy Sets (HYFS)” to capture uncertainties in expert opinions, a variance method to weigh expert reliability, the “Logarithmic Percentage Change-driven Objective Weighting (LOPCOW)” method to determine criterion weights, and the “Weighted Aggregated Sum Product Assessment (WASPAS)” algorithm to rank CSPs. A case study involving five construction companies and fifteen criteria was conducted to validate the proposed framework. The framework proposed in the study is able to effectively rank the CSPs, demonstrating practical utility in selecting an optimal CSP considering both qualitative and quantitative factors. Sensitivity analysis showed the framework is robust to changes in criterion weights. It is to be noted that, “Experience”, “Quality of work”, and “Technology and Innovation” emerged as the top three categories of criterions influencing the selection process. The study contributes to the literature by introducing usage of HYFS to CSP selection problem, explicit computation of expert weights based on variance, LOPCOW for criterion weights, and an integrated HYFS-Variance-LOPCOW-WASPAS framework. The study offers a practical tool for stakeholders to move beyond cost-centric bidding, promoting fairness, efficiency, and accountability in project selection and various other decision-making contexts.
Integrating AI-enhanced and conventional market selection approaches for the construction industry: insights from Indian firms
International market selection (IMS) plays a pivotal role in shaping construction companies’ global strategies. However, conventional IMS models generally fail to capture the distinctive challenges in the construction sector, such as the regulatory volatility, supply chain vulnerabilities, and site-specific operational constraints. To address these deficiencies, this study introduces a data-driven IMS framework specifically designed for the construction industry that incorporates financial, institutional, and industry-specific variables. The research adopts a two-phase analytical process: First, key IMS factors are identified and assessed through four methodological approaches, namely, logistic regression, partial least squares structural equation modeling, adaptive neuro-fuzzy inference systems (ANFIS), and the fuzzy ordinal priority approach. Second, the model is validated using 5,656 international market entry records for Indian construction firms from 2016 to 2023. The results demonstrate that the proposed artificial intelligence-enhanced framework significantly outperforms conventional models. Specifically, the ANFIS model achieved a prediction accuracy of 94.523% with an AUC-ROC of 0.874. This quantitative enhancement confirms that the integrated approach effectively captures nonlinear interactions and complex market constraints that conventional models generally neglect. Internal variables such as international experience and engineering productivity contribute positively to predictive accuracy. Meanwhile, external variables, particularly geographic distance and national risk, are demonstrated to be more difficult to model. Overall, the proposed framework provides a reliable foundation for strategic decision-making in global construction. It provides actionable insights for firms aiming to expand sustainably in uncertain international environments.
Innovation in construction: assessing the role of transformational leadership and knowledge governance
The dynamic nature of today’s world presents numerous challenges, and construction companies are not exempt from these difficulties. This research aimed to investigate the effects of transformational leadership (TL) on both product innovation (PDI) and process innovation (PCI), as well as to determine the mediating role of knowledge governance (KG) and knowledge sharing (KS), along with the moderating impact of innovation climate (IC). A cross-sectional design and structural equation modeling (SEM-AMOS) were employed to analyze data collected from 185 participants from various construction companies in Spain. The findings demonstrate that knowledge sharing and knowledge governance play a significant role in connecting TL with innovation capabilities. Additionally, it was found that the effects of TL and KS on different aspects of innovation capabilities vary and depend on the company’s innovation climate. This study provides valuable insights into the relationship between TL, KG, and innovation capability, highlighting the importance of fostering knowledge sharing, enhancing knowledge governance, and promoting a positive innovation climate to drive organizational innovation.
Scheduling the production of prefabrication construction supply chains considering variable delivery times
The application of prefabrication and modularization in the construction industry has grown significantly recently. The efficiency of prefabrication supply chains yields substantial advantages for construction projects. A challenge is the variability in delivery times, which negatively impacts the economy and reliability of prefabrication supply chains. Most construction prefabrication suppliers have difficulty adjusting their schedules in response to delivery time changes in a timely manner. Despite this critical challenge, limited research has addressed proactive and robust production scheduling to mitigate these uncertainties. This study investigates a method for proactive and robust production scheduling for construction prefabrication suppliers, particularly those with multiple fabrication shops, responding to changes in delivery times. This paper introduces a multi-objective, two-stage stochastic programming model that facilitates the production planning with multiple fabrication shops, considering variable delivery times. Computational results from an experimental study demonstrate that the proposed optimization model achieves a 14.6% cost reduction compared to the traditional EDD method. Computational results also show that the expected cost of the stochastic programming model achieves a cost reduction of 0.23% compared to a deterministic model. These findings suggest the model’s capability to generate robust and flexible schedules that effectively balance cost minimization with time reduction.
Post-pandemic building resilience: Building Information Modeling and green building integration
This research explores how Building Information Modeling (BIM), green building design, and post-pandemic resilience contribute to sustainable, healthy buildings. Although studies have explored BIM’s impact on green buildings, its role after COVID-19 remains largely unexplored. This study fills this gap through a “Green BIM post-pandemic square” taxonomy, integrating findings from a 1999–2024 literature review and BIM software evaluation. The taxonomy delves into project phases, green attributes, BIM features, and post-pandemic considerations to uncover links between BIM, green buildings, and post-pandemic cycles. It reveals how BIM enhances green design, construction, operation, and retrofitting. Additionally, BIM scrutinizes green building parameters such as energy efficiency and supply chain management. The study underscores BIM’s potential in sustainability, occupant health, and supply chain resilience through Green Building Evaluations (GBE). Ultimately, it synthesizes data to guide building researchers and practitioners, identifying gaps and suggesting future initiatives. Relevant journals and software are incorporated to provide comprehensive insights into the subject.
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.
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.