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.
Development of based on BIM data extraction and automation evaluation process for barrier-free certification
Population ageing and increasing diversity in building users have intensified the need for Barrier-Free (BF) certification. However, the growing complexity of buildings has made conventional two-dimensional drawing-based certification workflows inefficient due to limited spatial information and repetitive manual interpretation. This study proposes a BIM-based automated evaluation process for BF certification by structuring certification criteria into rule-based BIM data extraction and object classification logic. BF certification criteria were classified according to user characteristics, facility types, object conditions, spatial relations, and required attributes. Based on this framework, seven BIM-based evaluation methods were developed, including space search, object search, placement verification, presence checking, and attribute extraction. The proposed process was applied to a multi-story BIM model containing approximately 18,000 objects. As a result, 253 door-related objects associated with 68 spatial entities were evaluated within approximately 60 seconds, whereas equivalent manual review required approximately 7,590 seconds. These results confirm that BIM-based automation significantly improves efficiency and reduces omission risks in certification tasks, demonstrating its applicability to broader BIM-based compliance assessment.
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
Multidisciplinary approach to the structural, hydraulic, and mechanical design of a steel decay vessel for the SHiP experiment at CERN
In March 2024, the Search for Hidden Particles (SHiP) experiment and its associated Beam Dump Facility (BDF) were selected for future physics exploitation using the Super Proton Synchrotron (SPS) at CERN. SHiP is a general-purpose, intensity-frontier experiment designed to search for dark matter-related physics. Proposed approximately a decade ago, the SHiP experiment has passed through multiple design stages and optimizations. This study focuses on the structural design of primary subcomponent of SHiP, i.e., “the decay volume”, where the experiment seeks decay signatures of new particles. Initial concepts included a reinforced-concrete structure with hollow rectangular cross-sections exceeding 10 m × 5 m, evolving to a 50-m-long conical steel vessel under vacuum, and more recently to a metal-framed helium balloon, which is a textile helium seal supported by steel frames. We present the structural and hydraulic configurations now considered the final reference solutions for decay vessel of the SHiP to highlight the unique engineering challenges of minimizing structural mass, thereby reducing particle interactions and enhancing physics performance. To provide context, the paper first summarizes the collaboration status of SHiP and subsequently reviews the sequence of decay vessel structural options explored since the inception of the project. This historical overview depicts how the decade-long, multidisciplinary effort shaped current design choices. Furthermore, it illustrates how a novel, multidisciplinary, integrated approach guided the design evolution of the decay vessel. In the context of large-scale international collaborations such as SHiP, this paper demonstrates that a coordinated strategy integrating structural, hydraulic, and detector requirements has seldom been applied in similar large experimental facilities. As demonstrated in this study, such an approach proved essential for achieving a configuration that satisfies engineering constraints while preserving the experiment’s physics performance goals. The principal outcome of this effort is the proposed final design strategy, which coalesces engineering and physics goals into a unified framework. This methodology ensured that all subsystem interactions were considered concurrently to prevent costly redesigns during project execution. The final design strategy separates the mechanical system of the decay vessel into two key components: (i) a structural steel framework and (ii) non-structural aluminum elements, both integrated with a complex hydraulic system to circulate a specific detection liquid. This division maintains functional and structural efficiency and offers flexibility to adapt to seismic-condition variations throughout the project.
Emotionally intelligent construction safety monitoring via integration of machine learning and expert-informed electroencephalogram-related features
This article proposes an emotionally intelligent construction safety monitoring model that integrates machine learning and expert-informed Electroencephalogram (EEG)-related features as brain wave measurement device. The proposed safety prediction model is trained with risky behaviours, related emotional states, and expert-informed EEG-related features, incorporating brain waves and corresponding cortex locations. The architecture of the proposed emotionally intelligent safety monitoring system is detailed, utilizing real-world construction datasets and questionnaires. The value of the proposed emotionally informed ML framework lies in introducing EEG-related emotional constructs into construction safety assessment through an exploratory, expert-informed representation of emotional intelligence. The addition of a new dimension to the construction safety prediction models and consideration of the versatility of factoring in emotional states makes implications of this study beyond safety to other fields of construction management areas.
Local protectionism in the Chinese construction industry: a data analysis of construction contractors’ credit scores
The Chinese government has recently introduced a credit scoring system to gauge the trustworthiness of construction contractors and urged public project owners to consider these scores during the tendering phase. Meanwhile, however, local protectionism, often seen as preferential policies and practices benefiting local firms over outside competitors, is a major concern in the construction industries of developing countries, particularly in China. Little is known about the presence and extent of local protectionism within this credit-scoring process. To find out, this study analyzes the scores of 15,301 construction firms across five cities in China’s Jiangsu Province. The findings reveal two key points: first, all five cities exhibit local protectionism, evidenced by significantly higher credit scores for local firms compared to non-local ones; and second, the size of a company has a significant moderating effect on this bias, with larger firms experiencing wider score disparities favoring local businesses. The study contributes to the knowledge of local protectionism and potential policy changes in the construction sector by providing empirical data analysis on China’s emerging credit evaluation system.
Blockchain for fire-risk reduction in long-scale tunnel construction
Tunnel construction fire risk reduction is an integral component for tunnel construction safety. Recent fire cases during tunnel construction illustrate how existing management and prevention measures may fail in a fundamental and critical guarantee in the accuracy and authenticity of on-site information, thereby limiting the effectiveness of dynamic risk assessment management. To address this issue of information assurance, a fire risk reduction framework that integrates the Fault Tree analysis method with blockchain technology has been proposed. This framework develops some smart contracts and leverages digital signature to maintain data authenticity and ensure data immutability, contributing toward tunnel fire risk reduction by revealing responsible parties and maintaining information-source integrity. Based on this framework, a tunnel fire risk reduction platform using blockchain technology has been developed and applied in a practical engineering case study. The results indicate that this platform promotes open sharing of risk information, facilitates accident liability tracing, and supports dynamic risk assessment, ultimately improving the efficiency of fire risk reduction in tunnel construction.
A Novel Fuzzy Delphi DEMATEL-RANCOM approach to assess root causes of construction accidents
The construction industry remains one of the most hazardous sectors worldwide, consistently reporting high accident rates. This highlights the urgent need to systematically identify and address the root causes of such incidents. Although previous studies have explored causal relationships among accident factors, existing approaches are often complex and insufficient in prioritizing the root causes. To overcome these limitations, this study proposes an integrated framework combining the Fuzzy Delphi Method (FDM), Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL), and the Ranking Comparison Method (RANCOM). Within this framework, FDM is enhanced with the Convert Fuzzy Data into Crisp Scores technique and the Pareto Principle to generate accurate crisp values and reduce experts’ cognitive load by producing a concise list of significant causes. FDEMATEL quantifies interrelationships among these causes to derive the ranking vector, while RANCOM determines their relative importance. Notably, this study is the first to quantify criteria importance based on net influence values using the proposed FDEMATEL-RANCOM integration. A case study validates the framework and identifies six key causes: lack of safety policies, poor safety culture, inadequate supervision, ineffective enforcement, insufficient equipment expertise, and improper use of protective equipment. This study provides insights into improving occupational safety within the construction industry.
Rice-by products potential as precursors in geopolymer concrete: a review and its beneficial use of studies
Rice serves as a primary staple in many Asian nations, generating significant straw residue during cultivation. Improper disposal or burning of this residue not only contributes to environmental hazards, such as greenhouse gas emissions, but also leads to the loss of a valuable agricultural by-product for farmers. Harnessing rice straw for construction purposes presents an opportunity to advance sustainable practices and provide a clean energy solution to address India’s increasing energy demands. Nevertheless, ensuring the sustainability of rice straw ash in geopolymer production requires thoughtful evaluation within the current agricultural landscape of India. Although rice straw is carbon-neutral, concerns about its environmental impact throughout the entire process, from cultivation to transportation, have been raised. This review evaluates methods and materials for partially replacing rice straw in construction for environmental sustainability. It also discusses mathematical models for predicting mechanical properties and examines the potential of rice straw ash and other materials as replacements in construction.
Understanding how humble leaders foster members’ adaptivity in construction project management
In the current volatile, uncertain, complex, and ambiguous (VUCA) era, construction project leaders play an increasingly pivotal role in driving project success. However, humble leadership, which offers a promising approach for construction project management, remains insufficiently examined through empirical research. Taking social identity theory as the overarching theoretical framework, this study examines how humble project leaders foster members’ adaptive behavior, that is, job crafting. Using hierarchical regression and bootstrapping on 239 time-lagged samples from construction projects, the results show that humble leadership directly promotes job crafting, with project identification mediating this effect. Moreover, power distance orientation weakens the indirect influence of humble leadership through project identification, while showing a U-shaped moderation on the direct relationship. Therefore, this study highlights that humble leadership promotes job crafting by fostering project identification, but its effectiveness depends on individual power distance orientation. These findings contribute to construction management theoretically and practically by highlighting humble leadership as an effective approach for construction managers to enhance members’ adaptivity and improve project outcomes in VUCA environments.
Nuclear waste management strategies, advancements and its impact on infrastructure
Nuclear waste management has become a critical issue due to growing concerns about the long-term safety and sustainability of radioactive waste disposal and its impact on infrastructure. Insufficient knowledge and regulations surrounding nuclear waste management are significant challenges to sustainable development. This review provides an in-depth categorisation of nuclear waste, with particular emphasis on high-level waste (HLW), which requires extensive pre-disposal treatments, and explores its impact on infrastructure and structures. Recent innovations, such as thermal treatment, partitioning and transmutation, are highlighted for their effectiveness in reducing HLW volume and radiotoxicity. The review also examines the detrimental effects of radiation on containment materials, especially concrete and steel. Notably, containment infrastructure is vulnerable to degradation, with studies showing a loss of up to 50% in compressive strength in concrete exposed to extreme neutron irradiation and reductions of up to 70% in Young’s modulus. Advanced materials, such as fibre-reinforced concrete and oxide-dispersion-strengthened steels, offer promising solutions by mitigating these effects and improving durability by 10–30%. Furthermore, the review identifies significant strides in international collaboration on nuclear waste management, which have led to advancements in minimising hazards and improving waste processing. These efforts are further supported by the integration of digital tools and material innovations, ensuring continued progress in managing nuclear waste safely and sustainably.
Identifying critical management activities for off-site construction supply chain coordination: an importance-performance analysis approach
Off-site construction (OSC) offers a transformative path toward faster, cleaner, and more consistent project delivery. Its success, however, depends on strong OSC supply chain coordination (OSC-SCC) across phases. Persistent fragmentation, unclear responsibilities, and reactive risk management continue to disrupt performance and constrain OSC’s scalability. Prior research identifies critical risks, but often treats them in isolation, without a phase-based perspective on how coordination failures unfold or which management actions fall short. This study addresses these gaps by developing a structured, risk-responsive framework for OSC-SCC that (1) identifies critical risk categories and corresponding management activities across project phases; (2) evaluates their perceived importance and actual performance; and (3) clusters these activities to reveal cross-phase coordination gaps and inform targeted improvements. The analysis is grounded in importance-performance analysis, supported by expert surveys. Results reveal persistent underperformance in supplier incentivization, logistics traceability, responsiveness to demand changes, and process standardization. If left unaddressed, these issues cascade across phases, undermining efficiency and amplifying risks. The study contributes a practical tool that helps stakeholders align risk management with OSC’s interdependent workflows. By translating risk insights into actionable, phase-aware strategies, the framework bridges theory and practice, supporting more resilient and scalable adoption of industrial construction.
When capital meets control: governance dilemmas of financial investors in PPP projects
While financial investors typically provide substantial share of capital in Public Private Partnership (PPP) Projects, they frequently lack meaningful operational control, leaving them vulnerable to opportunism from both government entities and contractors (i.e., industrial investors). This study investigates these unique governance challenges through a dual trajectory approach: first, by developing a comprehensive PPP governance theoretical model specifically tailored to financial investors’ needs, and second, by empirically validating this model through an in-depth case analysis of a financial investor’s innovative governance practices. The research makes three significant contributions to the field: First, it reconceptualizes financial investors as distinct stakeholders with specialized governance requirements in PPP ecosystems. Second, it advances a holistic theoretical model comprising 19 critical governance elements across institutional, organizational, contractual, and managerial dimensions. Third, it derives four practical, evidence-based strategies for maintaining control while preserving partnership viability: 1) implementing contractor profit-sharing mechanisms to align incentives, 2) developing integrated risk-transfer structures, 3) establishing joint government-contractor oversight systems, and 4) creating debt service reserve funds from construction margins to mitigate government payment delays. These findings offer both theoretical insights and actionable solutions for improving financial investor protection in PPP arrangements.
Hypothesis-validated construction accident analysis and framework for potential multi-sensor fusion with 360° camera and LiDAR
Construction sites are among the most hazardous work environments, with frequent accidents such as falls, entrapments, and collisions. Traditional single-sensor detection systems suffer from occlusions, poor lighting, and limited depth perception, reducing reliability in complex conditions. This study addresses these limitations by proposing a realtime multi-sensor fusion framework integrating a 360° camera and LiDAR. A three-year construction accident dataset was analyzed, and Chi-squared tests and ANOVA (p < 0.05) confirmed the statistically significant superiority of the fusion approach over single-sensor systems. Deep learning techniques were applied to enhance real-time detection and prevention capabilities. The results demonstrate that sensor fusion substantially improves detection accuracy, especially in high-risk scenarios such as falls and collisions. This study provides a comprehensive statistical analysis based on national incident records and proposes an AI-driven monitoring framework. The findings offer strong empirical support for multi-sensor fusion as a foundation for next-generation construction site safety systems.
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.
Advancing the circular economy: a BIM and lean construction dynamic framework for pre-construction phases
The execution of construction technology projects generates substantial construction waste, both site-based and non-site-based, posing significant environmental and economic challenges worldwide. This paper explores a proactive approach to minimizing construction waste by controlling its causes during the pre-construction phase by integrating Building Information Modeling (BIM) and Lean Construction principles. Key pre-construction activities such as cost estimation, scheduling, constructability reviews, value engineering, procurement, and contracting are analyzed for their potential to reduce waste. Two scenarios - Existing Waste Management Strategies and Waste-Effective Site Management Practices are assessed using quantitative metrics and system dynamics modeling. The proposed technique employs eight causal loop diagrams, reflecting expert insights into waste reduction strategies, and is implemented using AnyLogic software with a stock-flow system dynamics model. Validation is achieved through a real-world case study, demonstrating the technique’s applicability and effectiveness in minimizing construction waste during the pre-construction phase. The findings highlight the dynamic buildup of critical variables influencing waste generation and provide a framework for evaluating and implementing optimal waste reduction strategies. This research underscores the potential of BIM and Lean Construction to drive sustainable waste management practices in construction technology projects, contributing to enhanced efficiency and environmental stewardship.
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.
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.
Construction waste governance in China: policy analysis and roadmap for circular economy transition
Despite the proliferation of circular economy (CE) policies targeting construction and demolition waste – the largest urban solid waste stream in China – a critical policy-practice gap persists between regulatory frameworks and on-site recycling practices. Existing literature often lacks a systematic diagnosis of the institutional and behavioral barriers hindering this sector’s transition to circularity. Drawing on policy design theory, this study formulates an integrated analytical framework that synthesizes policy instrument analysis with stakeholder-centered perspectives to address this research gap. By integrating a mixed-methods content analysis of 925 policy documents with qualitative insights from 23 expert interviews, this study identifies six systemic deficiencies in China’s CE transition in the construction sector: institutional fragmentation, policy instrument imbalance, limited upstream stakeholder engagement, regional disparities, insufficient standardization, and inadequate economic incentives. To transcend these barriers, the study then proposes a novel dynamic transformation roadmap. Unlike static solutions, this framework articulates a phased evolution (short-, medium-, and long-term) that guides the transition from rigid administrative control to a market-driven, lifecycle-integrated ecosystem.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Investigating key factors in the digitalization of value engineering in the Malaysian construction industry
Value Engineering (VE) is widely recognized for enhancing value and minimizing unnecessary costs in construction projects. As the industry moves toward digital transformation, integrating digital tools into the VE process offers new opportunities to improve efficiency, collaboration, and decision-making. This study aims to identify and evaluate the critical factors influencing the digitalization of VE in the Malaysian construction industry and to propose a validated conceptual framework tailored to its specific context. A quantitative approach was employed, using data from 199 Malaysian construction professionals and analyzed through Structural Equation Modelling (SEM). The confirmatory factor analysis revealed high factor loadings (≥ 0.8) for key enablers such as real-time information access, data validation, digital workflow integration, operational scalability, regulatory compliance, decision-support systems, and long-term implementation support, including training and system maintenance. In contrast, lower loadings (< 0.7) for constructs like alternative evaluation and return on investment suggest a declining emphasis on traditional costcentric perspectives. The findings demonstrate that digitalizing VE can lead to enhanced project value, cost efficiency, and process automation within the Malaysian construction sector. The study introduces a comprehensive, phase-based conceptual framework that integrates digital technologies with conventional VE practices, offering actionable insights for project managers, engineers, and policymakers seeking to adopt and scale digital VE in Malaysia.