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.
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.
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 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.
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 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.
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.
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.
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.
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.
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.
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.
Managing cities has become more complex recently with rapid urbanization triggering serious concerns in areas including quality of life, environment, safety, health and access to reliable infrastructure and high-quality services. Smart infrastructure (SI) development helps to address such concerns and meet societal aspirations. However, increasing development costs and risks in developing SI, have prompted a wider adoption of public-private partnerships (PPPs). Thus, this research study aims to determine and assess the major barriers to implementing PPPs for SI developments in Sri Lanka (SL), a developing country in South Asia, and to provide effective strategies for mitigating those barriers. Ten expert-interviews and an empirical questionnaire survey followed a systematic and extensive literature review to achieve this aim. Lack of expertise and certain public-related barriers such as citizens’ reluctance to accept private sector participation and unawareness on the perceived advantages from PPP were identified as major barriers in SL. Among the mitigation strategies, creating awareness and capacity-building of stakeholders was discerned as a significant strategy. Quantifying each barrier’s influence, interdependence and providing effective strategies for overcoming the barriers are described herein, leading to the findings that construction industry practitioners and policymakers could adopt to develop SI in SL more smoothly and efficiently.
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.
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.
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.
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.
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.
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.
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.
