Hazard-adjacent sites such as seawalls, bridge edges, quaysides, and fenced industrial perimeters routinely require metric positioning without in-zone fiducials, survey control, or repeated traversal, yet still demand decision-ready uncertainty on a site-meaningful datum. We present a datum-constrained forward intersection (DFI) pipeline that completes metric anchoring in a reachable safe subregion and transfers the uncertainty of scale, pose, and datum plane across the boundary to a targetless zone. In the safe-zone calibration stage, standard ChArUco imagery drives a prior-constrained bundle adjustment that jointly estimates multi-camera extrinsics and the site datum plane under weak but auditable tape-measure priors (inter-camera baseline and optical-center heights). In the target-zone measurement stage, undistorted pixel rays intersect the datum in closed form and are fused by incidence weighting; first-order propagation yields a 95% confidence region on the datum plane for each reported coordinate and distance. To tolerate consumer-grade instabilities (modest refocus/zoom and mixed resolutions), the pipeline supports BA-anchored refinement and a minimalist one-parameter in-situ metric correction (DFI–MScale) based on a few taped distances. In the test pair, DFI–MScale attains a median/p90 distance error of 62/106 mm, reducing p90 by 88.5% versus ORB, 87.6% versus LoFTR, and 78.4% versus RoMa under identical intrinsics and target points.

The application of sustainable manufactured sand (MS) in 3D printed concrete is currently severely restricted by the “pumpability-buildability” conflict, primarily characterized by the high internal friction and angularity of MS particles. To overcome this bottleneck, this study proposes a novel process-oriented strategy: a secondary mixing protocol featuring a static resting period and delayed superplasticizer addition. This approach is explicitly designed to induce a “rheological rejuvenation” effect in stiff MS mixtures. Quantitative results demonstrate that this protocol reduces the static yield stress of high-volume MS ink (T-3) by approximately 39% (from 1,516 Pa to 924 Pa) and significantly diminishes thixotropic hysteresis, thereby successfully reopening the effective printing window to approximately 65 min. Although increasing MS content accelerates the structural build-up rate, the optimized 3D printed manufactured sand concrete (T-3) achieves a 28-day compressive strength of 61 MPa, representing a 15% enhancement over the river sand reference group (T-0). These findings confirm that the secondary mixing protocol is a robust solution capable of unlocking the potential of high-volume MS in sustainable digital construction.

Seawater-sea sand concrete has emerged as an innovative construction material due to its effective utilization of marine resources. At the same time, geopolymer concrete has shown rapid hardening capabilities, low energy consumption, and eco-friendly properties, making it highly promising for structural reinforcement. This study evaluated the effects of alkali modulus, alkali concentration, and fly ash (FA) content on the performance of geopolymer FA-slag seawater-sea sand mortar (SWSSGM). Performance characteristics such as consistency, flowability, compressive strength, and flexural strength were tested. The findings indicate that FA enhances workability, whereas slag powder has the opposite effect. Increasing alkali concentration reduces consistency but increases slump. Optimal compressive and flexural strength is achieved when the alkali modulus is 1.2, alkali content is 15%, and FA content is 30%. Generally, higher alkali concentrations promote strength development, while increased FA content raises porosity and reduces strength. However, strength gains over time are more pronounced, and the effects of alkali modulus require further investigation. Multiple linear regression analysis of the three factors affecting SWSSGM properties demonstrates that a quadratic polynomial regression model yields high coefficients of determination (R² ≥ 0.92), enabling accurate predictions of SWSSGM performance parameters. From a lifecycle (cradle-to-gate) perspective, SWSSGM outperforms traditional cement-based materials in terms of carbon emissions, energy consumption, and resource efficiency, showcasing its significant low-carbon, energy-saving, and environmentally friendly attributes. Through systematic optimization of SWSSGM’s design parameters, performance prediction modeling, and preliminary environmental assessment, this study provides guidance for its sustainable application in coastal engineering projects.

The increasing digitalization of the construction industry is reshaping how professionals are trained and educated. As a key component of construction education, traditional site visits provide students with valuable first-hand exposure to real-world construction environments. However, despite their educational value, such visits present multiple challenges, including scheduling difficulties, limited site capacity, health and safety restrictions, induction requirements, and high logistical costs. In recent years, Virtual Reality (VR) has offered new opportunities to deliver remote, realistic, and interactive site experiences that replicate the authenticity of on-site learning without requiring physical presence. Previous studies have primarily focused on fully computer-generated VR environments developed using platforms such as Unity or Unreal Engine. While these models offer flexibility and interactivity, they often lack the contextual realism of actual construction sites. Addressing this gap, the present study designed and developed a VR immersive construction site experience tailored to the Australian construction industry, built from 111 high-resolution 360° panoramas captured at a real construction site. The system architecture was developed using an incremental development approach to manage complexity, refine functions, and ensure continuous feedback. Drawing on insights from a systematic literature review, the system architecture specifically targeted barriers to VR adoption in construction education. The resulting prototype was evaluated by 31 students, revealing positive perceptions across all key constructs, with mean scores of 4.31 for usefulness, 4.30 for ease of use, and 4.28 for learning impact (five-point Likert scale). This study represents a pioneer exploration in establishing a scalable and educationally aligned framework for VR-based construction learning. Future development stages will focus on expanding the system to cover diverse project types, integrating adaptive learning features, and linking it with learning management systems to enhance engagement and learning analytics.

Integrating Carbon Capture, Utilization, and Storage technology into concrete additive manufacturing represents an innovative pathway for achieving carbon emission reduction in the construction industry, while simultaneously offering effective solutions to critical challenges such as insufficient early-age strength and weak interlayer bonding in printed components. Bibliometric analysis reveals that this interdisciplinary field is experiencing a period of exponential growth, with research hotspots rapidly shifting from fundamental 3D printing process exploration to the synergistic optimization of material properties utilizing accelerated carbonation and carbon dioxide sequestration technologies. This review systematically elucidates the mechanisms of carbon mineralization curing, with a particular focus on analyzing three key process pathways: fresh state carbon mineralization, synchronous carbon sequestration during printing, and post-printing carbonation curing. Furthermore, the synergistic enhancement mechanisms of material strategies, including supplementary cementitious materials and carbonated recycled aggregates, are discussed. On this basis, the review identifies core challenges regarding curing uniformity, narrow process windows, and the absence of long-term durability research, and subsequently outlines future development directions such as intelligent closed-loop control and integrated structure-material-process design.

Against the backdrop of global climate anomalies and rapid urbanization, rainfall-induced urban flooding has become increasingly frequent. Wuxi, a city in southern Jiangsu Province, suffers annual casualties and economic losses due to flooding, leading to urban chaos. This study proposes a quantitative evaluation of the level of urban flood resilience using a system of resilience evaluation indicators. An evaluation indicator system for urban flood resilience encompassing infrastructure, environment, society, and economy was constructed with 26 indicators. A hybrid multi-criteria decision-making method integrating the Analytic Hierarchy Process, entropy weight method, and GIS technology was developed to assess flood resilience in Wuxi systematically. Additionally, emergy analysis was applied to evaluate urban sustainability. The results indicate that five administrative districts centered in Liangxi District and parts of Yixing City near water bodies exhibit low resilience due to gentle slopes, proximity to active river channels, and high population density. In contrast, most areas of Jiangyin City demonstrate higher resilience owing to elevated terrain, fewer social vulnerabilities, and advanced economic development. The model’s consistency was validated using the Area Under the Curve (AUC) method, yielding an AUC of 0.843. The Emergy Sustainability Index for Wuxi was 0.08 < 1, indicating an unsustainable state. This study provides actionable recommendations for enhancing urban flood management and regional resilience.

Solid waste generation from industrial production and construction activities has increased sharply in recent years. Improper disposal and abandonment of solid waste have led to significant resource loss and pose serious risks to soil, water, and air quality. As a result, incorporating solid waste into concrete production presents a promising strategy to reduce environmental pressure and promote sustainable development. This review investigates the feasibility and performance of various solid waste materials, such as fly ash, limestone powder, stone sludge, silica fume, waste glass, rubber particles, and recycled concrete sand, that are incorporated into 3D printed concrete (3DPC). These materials demonstrate the potential to enhance rheological behavior, mechanical strength, interfacial bonding, and dimensional stability of printable composites. Additionally, their incorporation reduces carbon emissions, diverts waste from landfills, and promotes resource circularity. The benefits and limitations of solid waste are critically evaluated, including printability challenges arising from particle morphology and water absorption. The review emphasizes the need for standardized cost-effectiveness analysis frameworks to quantify economic and environmental balance. Finally, this study highlights the importance of optimizing mix design, surface treatment techniques, and large-scale production strategies to enable widespread adoption of waste-based 3DPC in sustainable construction.

It has been extensively shown that Building Information Modelling (BIM) improves project performance, which is impacted by various critical performance factors of BIM project (CPFs-BP). However, it has become a difficult problem in theory and practice to determine the most important CPFs-BP impacting BIM project performance (BPP) at various life-cycle stages. In light of this information gap, the goal of this study is to identify the most important CPFs-BP and identify the stages at which BIM can be applied to enhance BPP through specific CPFs-BP. Using a review of the literature, this work first discovers and characterizes 17 CPFs-BP. Then, by constructing a two-dimensional, two-mode network of heterogeneous nodes, this study demonstrates the top seven CPFs-BP, the relationship between CPFs-BP and life-cycle stages, and the relative significance of various life-cycle stages in BIM projects using descriptive methods and complex network theory. BPP prediction models were then constructed and validated utilizing fitness parameters using MATLAB and data obtained from expert interviews and surveys. Lastly, five specific strategies are put forth to enhance BPP: increasing BIM accuracy during the construction phase, enhancing stakeholders’ capacity to coordinate the design of construction documents, enhancing BIM accuracy during the completion phase, fostering cooperation between conceptual design organizations, and utilizing the BIM system environment during the construction phase. Important BIM project stakeholders can apply the study’s conclusions. Finally, five specific approaches to improving BPP are proposed: improving BIM accuracy at the construction stage, stakeholders’ ability to organize construction document design, BIM accuracy at the completion stage, collaboration between conceptual design organizations, and the BIM system environment during the construction stage. The study is useful for key stakeholders in BIM projects who can put its findings into practice and help evaluate BPP throughout the project’s life cycle.

Several EU policy and legislative initiatives aim to tackle the problem of major resource use and waste within the built environment, both energy and materials, including plans to decarbonise the sector and implement circularity principles. The use of modular systems in deep energy retrofit is argued to present an accelerated decarbonisation pathway with potential to implement advanced circularity. However, modular retrofit and the integration of circularity in the built environment and its assessment is an emerging area of research with the literature identifying need for comprehensive assessment methods demonstrated in practical case studies. This paper examines the development of a novel holistic design for disassembly assessment framework and method piloted in the prototype design of modular wall over-cladding systems in the deep energy retrofit of social housing in Ireland under the EU Drive 0 project. The findings indicate significant differences in design for disassembly performance between the modular circular solution and a conventional solution, with differences in performances at all construction levels. The method provides a holistic and detailed assessment but is critiqued in terms of complexity and user subjectivity relating to the assessment stage, data availability and professional knowledge and/or experience, with a revised adapted method proposed. The research contributes to knowledge in highlighting the potential and complexity of achieving advanced design for disassembly in practice, demonstrating that there are significant differences in the performances across construction types and hierarchy and that assessment methods need to be robust, holistic, sufficiently detailed but not overly complex for application in practice.

3D-printed concrete (3DPC) represents a rapidly developing technology with numerous applications in construction. The properties of 3DPC under both static and dynamic loads can be enhanced by incorporating reinforcement. This study presents a novel reinforcement technique utilizing U-shaped steel wire mesh (U-shape SWM). This technique involves integrating both horizontal and vertical reinforcements during the concrete printing process. Additionally, a new fabrication process was developed using a dual-arm robotic system, with each arm equipped with shaping and welding tools, to autonomously create U-shape SWM structures. An experimental investigation was conducted on specimens reinforced with either flat or U-shape SWM reinforcements under flexural loading. Compared to the specimens reinforced with a single layer of 6-mm flat SWM under a shear-span ratio of 2, those incorporating one layer of U-shape SWM combined with two layers of 6-mm flat mesh exhibited a 200% increase in failure load and a 26.09% increase in cracking load, while the ultimate displacement decreased by approximately 6%. Under a reduced shear-span ratio of 0.5, this reinforcement configuration reached a failure load of 43.6 kN, a cracking load of 16.1 kN, and an ultimate displacement of 11.1 mm. Furthermore, an analytical model based on the strut-tie model was developed to predict the load-bearing capacity of the reinforced specimens. The model predictions showed good correspondence with the experimental results.

Indoor geometric quality inspection plays a crucial role in construction quality control. Light detection and ranging (LiDAR) can obtain point cloud data of the indoor environments in full range, which then can be utilized to efficiently extract geometric features of building elements for inspection. However, existing data collection of indoor environments using LiDAR is still manually planned and implemented, which is quite time-consuming as the scanning space is usually unknown and the movability of equipment is limited. Therefore, this paper proposes an automated approach for indoor geometric quality inspection data collection based on Building Information Model (BIM) and quadruped robot equipped with LiDAR. First, BIM with accurate geometric and semantic information of building is integrated with heuristic algorithm to automate scan planning, including scan stations and order. Second, Simultaneous Localization and Mapping (SLAM) integrates with BIM is equipped into a quadruped robot to achieve automated data collection capability. Finally, an intelligent execution module for scanning point cloud data in indoor environments is introduced. The proposed approach is validated in real indoor environments. Compared with traditional data collection methods, the experiment results show that the proposed approach can save 42% of scanning time, and the scanned point cloud data has better quality and enough density, which significantly improves the efficiency of inspection data collection for indoor geometric quality management. By unifying BIM-driven planning, SLAM-based localization, and quadruped robot mobility into a single framework, this study introduces a novel approach for large-scale automated indoor inspection, with attention to connected areas to ensure continuity across multiple spaces.

The growing complexity of construction projects demands decision processes that can integrate diverse professional perspectives across design, cost control, construction, and acceptance. Traditional sequential management approaches often leave conflicts unresolved until late stages, causing delays, cost overruns, and rework. To address this issue, this study develops a cross-stage workflow for construction projects that embeds artificial intelligence into collaborative decision-making. The workflow employs digital agents representing different roles, which engage in structured debates supported by evidence from building information modeling models, engineering drawings, cost records, and technical standards. Through iterative exchanges, alternative solutions are proposed, challenged, and refined, with the process producing decisions that are transparent, traceable, and adaptable to changing project conditions. Results from four representative scenarios confirm significant improvements over conventional practice: unresolved design clashes were reduced by more than 40%, cost forecast deviations narrowed from around 12% to below 5%, schedule variance under disruption decreased by about 18%, and first-pass acceptance rates increased from 74% to 88% with fewer reworks. A longer-term case study further demonstrated reductions in cost variance and project duration, together with higher stakeholder satisfaction. By coupling AI-enabled debate mechanisms with established digital construction environments, the workflow turns disciplinary conflicts into structured reasoning that enhances decision quality. The findings highlight how intelligent and collaborative approaches can deliver measurable gains in cost, time, and quality, while offering a practical pathway for advancing smart construction practices.

Effective scheduling in construction projects increasingly depends on allocating scarce multi-skilled labour under strict precedence and capacity constraints. Reinforcement learning (RL) techniques have presented outstanding performances in addressing Resource Constrained Project Scheduling Problem (RCPSP) instances. However, studies are lacking that utilise RL algorithms in solving extended RCPSP like the Multi-Skilled RCPSP (MSRCPSP). MSRCPSP is a nondeterministic polynomial time (NP)-hard problem that enables activity start times and allocation of multi-skilled resources to be determined simultaneously. Unlike the classical RCPSP, each activity specifies explicit skill requirements rather than anonymous capacity units. This study formulates scheduling as a Markov decision process and compares five optimisation agents, including the genetic algorithm, particle swarm optimisation, black-winged kite algorithm, deep Q-Network (DQN), and proximal policy optimisation (PPO), on benchmark instances from the MSLIB library. The results showed that solutions produced by PPO and DQN concentrate near the reference optima. PPO, in particular, attains the largest number of optimal or near-optimal schedules and the shortest makespans. In several instances, the deep reinforcement learning (DRL) agents also outperform the benchmark solutions, plausibly because CPLEX, constrained by a 60-second time limit, returns suboptimal solutions. Overall, the comparative results provide a practical benchmark of widely used heuristics and metaheuristics against DRL baselines, offering guidance for future algorithm selection and hybridisation for MSRCPSP.

Buildings account for a substantial proportion of urban energy demand, making it essential to understand the interrelationships between the built environment, urban heat island (UHI) effects, and energy demand. This study investigates the impacts of UHI on urban building energy demand in Mumbai, India, using a multi-scale framework. First, UHI intensity is assessed by generating and analyzing Land Surface Temperature maps and calculating the Urban Thermal Comfort and Vulnerability Index. This assessment identifies UHI hotspots and regions with potential thermal discomfort. Subsequently, energy demand modelling is conducted across two locations with distinct thermal comfort conditions, spanning from the individual building to the urban scale. Detailed building information and site-specific climate data from the two contrasting locations are collected to support urban building energy modelling (UBEM). Results reveal that UHI increases cooling energy demand by 7.31% at the individual building level; however, its impact on urban-scale energy demand is significantly mitigated by building geometry and surrounding structures. Specifically, variations in building height and inter-building shading outweigh the influence of UHI, leading to a substantial 15.9% reduction in the mean cooling energy demand intensity. These findings highlight the critical role of urban morphology and density in shaping cooling energy demand under UHI conditions. By extending beyond individual building-level assessments to UBEM, the study contributes new evidence regarding UHI-energy interactions in a year-round warm tropical context such as Mumbai. Furthermore, the results position urban-scale cooling energy demand assessments as a transferable framework to integrate UHI research with energy policy, ultimately supporting climate-responsive planning.

The construction industry is at a critical juncture, facing both unprecedented opportunities and challenges driven by emerging technologies like BIMS-GPT, which combines Building Information Models (BIMs) with Generative Pre-trained Transformer (GPT) language models. This study investigates the drivers and barriers to the adoption of BIMS-GPT in the construction industry through empirical research using questionnaires and expert interviews. The results indicate that the most significant drivers are BIMS-GPT’s ability to automate tasks, enhance decision-making, improve safety, and optimize processes across the entire building lifecycle. The main barriers include high development and training costs, lack of legal frameworks, data security concerns, and resistance to change among employees. Furthermore, the study analyzes differences in perceptions among respondents based on their years of experience and departmental roles. Those with 6-10 years of experience show the highest interest in adopting BIMS-GPT, while management and technical departments prioritize different aspects of the technology. The findings provide valuable insights for construction companies looking to implement BIMS-GPT and establish a solid foundation for its promotion and implementation. By understanding stakeholders’ attitudes and addressing the identified drivers and barriers, the industry can fully leverage the potential of this transformative technology, paving the way for a smarter, more efficient, and sustainable future in construction.

Net Zero Energy Buildings (NZEBs) offer a transformative pathway for decarbonizing the built environment by integrating energy-efficient design, renewable energy systems, and smart grid interaction. This review positions NZEBs as critical enablers of low-carbon cities, highlighting their ability to balance annual energy demand through both passive strategies and active technologies. Evidence from the literature shows that advanced envelope materials can reduce heating and cooling loads by up to 18.2%, window retrofits lower thermal loads by 15.5%, and rooftop photovoltaic systems can supply up to 70% of household energy demand in certain regions. The review traces the evolution of NZEBs from early solar integration to contemporary climate-responsive designs aligned with global sustainability frameworks. It also identifies persistent challenges, including high upfront costs, climate-dependent performance variability, and retrofitting difficulties in dense urban contexts. Future directions are suggested in the areas of advanced materials (e.g., aerogels, phase-change composites), urban-scale microgrids for energy sharing, and policy harmonization to strengthen grid resilience. Successful deployment of NZEBs will additionally require interdisciplinary collaboration, standardized international codes, and financial incentives to overcome existing barriers.

Sustainability in the construction industry entails measures to reduce the environmental impact of construction projects, achieve economic viability, and ensure a livable environment before, during, and after construction. However, construction activities generate substantial pollution, including dust and noise, and given their large scale, long duration, and involvement of multiple public service departments, considerable information is produced throughout each project. Traditional approaches to pollution control have proven largely ineffective, whereas platform-based models have demonstrated efficiency and cost-effectiveness in many sectors. This study therefore explores the digital transformation of public service governance for construction pollution through a platform-based approach. Timed Petri nets are employed to model and analyze both traditional and platform governance modes, providing a theoretical foundation for the digital upgrading of the construction industry using public data (e.g., construction pollution data). Results indicate that the platform governance mode reduces governance time by over 40% and substantially enhances information exchange efficiency compared with the traditional mode. By contrasting time delays between the two approaches, the superior efficiency of the platform model is highlighted. The paper further summarizes key findings, offers recommendations for institutional support and future research, and validates the proposed governance model through an empirical case study.

This study integrates ChatGPT and complex network (CN) techniques into an accident analysis framework designed to reduce manual effort in accident causation analysis. The proposed framework supports construction stakeholders in extracting causal factors (CFs) from accident reports and identifying both critical CFs and key causal paths. A multistep research design was adopted to develop and validate this novel framework for analyzing crane-related construction accident reports using ChatGPT and CN techniques. First, ChatGPT was prompted to extract CFs from a database of crane-related accident reports. Second, evaluation metrics and an expert questionnaire survey were developed to assess ChatGPT’s performance in CF extraction. Finally, CN analysis was conducted to explore the relationships among CFs and to identify critical causal paths. A total of 95 crane-related accidents from Hong Kong (2011-2020) were analyzed using the proposed framework. The critical CFs identified included: “carelessness”, “operation error”, “crane unbalanced”, “machine failure”, “parts of a crane fall”, “object strike”, “worker fall”, “trapping”, “collapse of crane”, and “load drop”. The critical path identified was: “broken/failed rope” → “load drop” → “object strike”. The primary contribution of this study lies in developing an AI-driven framework that combines the contextual understanding of ChatGPT with the structural analysis capabilities of CN methods—offering a novel and scalable approach to accident causation analysis in the construction industry. Safety managers and practitioners can leverage this framework to improve the automation, consistency, and interpretability of construction accident reporting.

This study explores the adoption of Virtual Reality (VR) in the Nigerian construction industry, with a focus on its potential benefits and associated challenges. Purposive and snowball sampling techniques were employed to select 52 construction professionals from Benin, Edo State,an emerging urban center with extensive construction activity. Adopting a quantitative approach, the research utilized a five-point Likert scale survey to assess perceptions of the benefits and barriers to VR adoption. The survey was pretested for clarity and reliability, and data were collected via the Qualtrics platform. The findings indicate that the key benefits of VR include improved task-technology alignment, enhanced workplace safety through virtual training, and more effective remote collaboration. VR was also found to enrich user experience and learning engagement by simulating high-risk scenarios to aid hazard prevention. Nevertheless, the study identifies several critical barriers to adoption, such as uncertainty regarding learning outcomes, technical disruptions, and high implementation costs. Despite these limitations, VR holds considerable promise for transforming training and professional development in the construction sector. To maximize its impact, the study recommends the development of customized training modules, technological improvements to enhance system reliability, and government support to mitigate implementation costs. Overall, VR has the potential to significantly improve training effectiveness, safety standards, and operational efficiency in the Nigerian construction industry, provided that the identified barriers are adequately addressed.

Amid growing global attention to occupational health and safety, the construction industry faces critical challenges associated with engineered stone, which emits high concentrations of respirable crystalline silica during processing and has been linked to severe lung diseases. In response, Australia enacted a comprehensive nationwide ban on engineered stone in July 2024. Drawing on media framing theory, this study analyzes public discourse and sentiment surrounding the ban by examining 7,198 comments collected from Reddit and YouTube. Through Latent Dirichlet Allocation, three dominant themes emerged: health risks and safety concerns, economic impacts and industry transition, and regulatory implementation. Sentiment analysis revealed that 55.5% of the comments expressed negative sentiment, mainly centered on economic concerns, while 21.3% were positive, emphasizing health benefits, and 23.1% were neutral. Economic impact frames predominated among negative comments, whereas health risk frames were more common in positive ones. These findings suggest that future policy communications should more effectively integrate narratives around both health protection and economic transition. This study contributes to the methodological development of sentiment analysis and offers practical insights for policy formulation and implementation.

The architecture, engineering and construction (AEC) industry is currently encountering numerous challenges and actively pursuing industrial upgrades through digital transformation. Digital twin (DT) technology aligns well with the industry’s evolving needs due to its capabilities in data integration, intelligent decision-making, and effective management of project progress, efficiency, and quality. This study conducts a comprehensive literature review to analyze the strengths, weaknesses, opportunities, and threats (SWOT) associated with DT applications in the AEC sector. Additionally, field visits and semi-structured interviews were conducted on a selected construction project where DT was implemented, allowing for an in-depth examination of both its significant benefits and potential limitations. To further evaluate and prioritize the identified SWOT factors, a SWOT-AHP analysis was performed. The results indicate that the AEC industry has a strong interest in DT’s advantages, particularly its potential to enhance resource allocation and process efficiency. Furthermore, the analysis reveals that the strategic quadrilateral occupies the largest area in the fourth quadrant (0.8554), with the strategic center of gravity located at (0.2321, -0.04135), suggesting that Strengths-Threats (ST) strategies should be implemented to support the future development and adoption of DT. Based on this strategic framework, the study formulates actionable development strategies to facilitate the effective integration and widespread adoption of DT in the AEC industry.

In pursuit of proactive safety management in construction, accurate and real-time recognition of workers’ cognitive states is essential for crafting targeted interventions to minimize safety risks. Unlike conventional manual and qualitative methods, electroencephalogram (EEG) offers a reliable and objective solution for cognitive state recognition. Notably, efforts that integrate EEG with deep learning have exhibited significant advancements. Nevertheless, certain constraints, such as experimental costs and limited participants, have caused a scarcity of high-quality data, which has impeded performance in recognition tasks. Although transfer learning demonstrates its capability in addressing this challenge, the lack of relevant explorations into EEG-based cognitive state recognition remains a significant research gap. Therefore, customizing pre-trained deep learning models for these tasks would be beneficial. This study aims to develop pre-trained models to advance future studies in this domain through transfer learning, encompassing: 1) extracting extensive and accurately labeled EEG data from the DEAP dataset, 2) selecting appropriate network architectures and implementing pre-trained model development, and 3) collecting EEG data for mental fatigue recognition and evaluating model effectiveness. Rigorous evaluation suggests a substantial improvement in model performance, with test accuracy increasing from 63.19% to 92.29% by leveraging the pre-trained convolutional neural networks (CNN)—long short-term memory (LSTM) model. In conclusion, this study significantly contributes to enhancing safety management for construction workers through EEG by providing validated pre-trained models to address challenges of data scarcity. Future research may advance by exploring additional network architectures, increasing sample size, and considering more specific recognition tasks for model evaluation.

This study investigates the integration of Building Information Modeling (BIM) and the Internet of Things (IoT) in construction projects to enhance efficiency, safety, and lifecycle monitoring. Despite the significant potential of BIM and IoT to facilitate the transformation of the construction industry, existing research lacks a comprehensive framework that addresses its interoperability challenges, data security concerns, and real-world implementation barriers. To address this gap, a multiple case study approach was employed, incorporating semi-structured interviews, document analysis, and real-world project evaluations. The results find that standardized protocols, data encryption, and modular IoT devices are essential for effective BIM-IoT adoption across different construction phases. The proposed framework offers a structured approach for construction teams to effectively utilize BIM and IoT, facilitating smarter and more sustainable project management. The contribution of this paper resides in the detailed analysis of the BIM-IoT applications, clearly demonstrating its advantages, such as improving building productivity and safety, and its potential to align with China’s sustainable urban development goals. The study also provides a forward-looking prediction of the future development trend of BIM-IoT in China, offering valuable insights for construction project teams and decision makers. By demonstrating the structured framework and its practical application, this study provides guidance for the industry’s transition to smarter and safer construction practices.

The predominance of a linear economic model and the limited integration of circular strategies in the design and execution of building projects—particularly in the construction sectors of developing countries—have resulted in ongoing pressure on natural resources, high levels of waste generation, reduced productivity, and frequent time and cost overruns. Collectively, these issues contribute to unsustainable development, adversely impacting the social, economic, and environmental dimensions of sustainability. This study explores the perceptions of design professionals regarding the benefits, awareness, and implementation of Design for Deconstruction (DfD) within the Nigerian construction industry (NCI). Data were collected through a structured questionnaire distributed electronically to design experts in Nigeria’s South-South geopolitical zone using a snowball sampling technique. With a 40.10% response rate and a reliability index above 0.800, the data were analysed using Exploratory Factor Analysis (EFA) and Partial Least Squares Structural Equation Modelling (PLS-SEM). Findings reveal that awareness of DfD is moderate, but its adoption remains low. EFA identified five key categories of DfD benefits: (1) business benefits, (2) economic benefits, (3) environmental benefits, (4) green certification and technology integration, and (5) social benefits. PLS-SEM results show that all five categories have a positive and significant influence on the decision to adopt DfD within the NCI. This study contributes to the theoretical advancement and practical understanding of circular construction practices, particularly DfD, with implications for reducing construction waste, improving resource efficiency, and supporting the achievement of Sustainable Development Goals (SDGs) 3, 9, 11, 12, and 13.

Digital twin (DT) technology is revolutionizing the architecture, engineering, construction, and operation (AECO) industry, driven by the advancements of Construction 4.0 (C4.0). Despite this, adopting DT in the AECO sector remains limited due to various barriers. This study, driven by four research questions (RQ), aims to advance DT adoption in the AECO industry under C4.0 using an integrated approach. The degrees of influence and importance ranking of barriers are assessed through the decision-making trial and evaluation laboratory (DEMATEL). The hierarchy and causal relationships among barriers are revealed through interpretative structural modeling (ISM). Then, barriers are divided into four clusters employing the cross-impact matrix multiplication applied to classification (MICMAC) method. The paper reveals that “lack of government financial and policy support” is the most critical barrier, “lack of trust and long-term perspective in DT” is the most significant direct-influence barrier, and “immature 3D engine technology” is the most fundamental barrier. By exploring interrelationships and prioritizing barriers, the study provides insights to enhance adopting DT in the AECO industry in the context of C4.0.

Energy performance contracting (EPC) has been implemented as a turnkey solution to enhance the energy efficiency of building systems and fixtures. The two most common types of EPC-guaranteed savings contracts and shared savings contracts-are widely applied in the industry, with their selection primarily depending on stakeholders’ risk tolerance and the availability of external financing. EPC involves multiple key participants, including the government, third-party financiers, building owners, and energy service companies (ESCOs). The relationships among these stakeholders vary based on the contract type: in a guaranteed savings contract, the owner has a direct financial relationship with the third-party financier, whereas in a shared savings contract, the ESCO assumes this role. This paper provides a critical review of previous studies on EPC, categorizing them into four key areas: (1) challenges in EPC adoption, (2) critical success factors, (3) diffusion strategies from different stakeholder perspectives, and (4) a stakeholder relationship framework linking key success factors and strategies. By analyzing these aspects, this review aims to inform potential investors and contractors about essential considerations for EPC implementation while examining the stakeholder dynamics within EPC projects.

Prefabricated housing (PH) is widely supported and applied in China due to its high efficiency and low carbon emissions. However, the adoption of this sustainable construction method has been slow in rural areas, hindered by economic, policy and location factors. To promote the development of low-carbon construction in rural areas and address the obstacles, this study focuses on Jiangsu province as a case study, proposing effective strategies to modernize and advance sustainable rural housing. The primary barrier to the prefabricated concrete (PC) adoption in rural Jiangsu were identified through a literature review and expert interviews. A survey was then conducted to assess the impact of these barriers, yielding 228 valid responses from industry professionals. Factor analysis was conducted using SPSS 19.0 to calculate the multi-level weights for influencing factors across dimensions. Based on these findings, key obstacles were further analyzed through strengths, weaknesses, opportunities and threats analysis to develop targeted countermeasures and recommendations. The research results show that: (1) Among secondary indicators, Policy factors (weight = 0.219) and Location factors (weight = 0.215) have the most significant impact PC promotion in rural Jiangsu; (2) Among the tertiary indicators, the critical factors are “The standard system is incomplete” (weight = 0.059), “The technical system is not mature” (weight = 0.048), “Lack of specific departments for promoting and supervising at the grassroots level” (weight = 0.046); (3) Strategies such as enhancing policy alignment and market penetration, tailoring solutions to local demands, establishing a robust regulatory framework, and fostering community engagement and acceptance can effectively address the challenges of promoting PC in rural areas. This research provides actionable pathways for the advancing PH in Jiangsu and offers valuable references for other regions seeking to promote PH in China.