Construction sectors in developing countries continue to experience disproportionately high fatality rates, largely due to reactive safety practices and the limited adoption of digital safety technologies. While Building Information Modeling (BIM) and the Internet of Things (IoT) offer significant potential for proactive safety management, their integrated application remains underexplored in resource-constrained contexts. This study examines stakeholder readiness to adopt IoT-enhanced BIM-based safety monitoring systems in large-scale infrastructure projects in Pakistan, including China–Pakistan Economic Corridor (CPEC) initiatives and the Barakahu Bypass project. An integrated Technology Acceptance Model–Technology–Organization–Environment (TAM–TOE) readiness framework is employed, wherein TAM-derived cognitive-motivational factors (Technology Awareness and Perceived Benefits) and TOE-derived contextual factors (Organizational Readiness and Perceived Barriers) are examined as joint predictors of Behavioral Intention (BI). Data were collected from 107 purposively sampled construction professionals using a structured questionnaire. The results indicate high attitudinal readiness (BI mean = 4.7; perceived benefits mean = 4.6) alongside moderate organizational readiness (mean = 3.4). Regression analysis reveals that perceived benefits (β = 0.42, p < 0.001) and technology awareness (β = 0.29, p = 0.003) are the strongest positive predictors of adoption intention. In contrast, perceived barriers exert a significant negative effect (β = −0.22, p = 0.022). The model explains 61.2% of the variance in behavioral intention. This study advances the literature by providing empirical evidence on stakeholder readiness for BIM–IoT safety adoption within construction management processes, estimated through a multiple regression model. It offers practical implications for policymakers and industry stakeholders seeking to accelerate data-driven decision-making and digital safety transformation in developing economies.

Developing-country construction sectors continue to record disproportionately high occupational accident rates, partly attributable to the slow adoption of digital safety technologies, including Building Information Modeling (BIM) and Internet of Things (IoT) systems. While prior empirical research has established the population-level factors that explain stakeholder adoption intention through survey-based frameworks, the ability to classify individual stakeholder readiness for targeted, pre-deployment intervention remains methodologically unaddressed. This study fills that gap by applying three supervised machine learning classifiers (Random Forest [RF], XGBoost (XGB), and Support Vector Machine (SVM)) to a dataset of 107 construction professionals purposively sampled from large-scale infrastructure projects in Pakistan, including China−Pakistan Economic Corridor (CPEC) packages and the Barakahu Bypass project. Five construct-level features derived from an integrated Technology Acceptance Model and Technology−Organization−Environment (TAM-TOE) survey instrument were used to classify stakeholders into High, Moderate, and Low readiness tiers. XGBoost achieved the best classification performance (accuracy = 93%, macro F1 = 0.93), followed by RF (91%, F1 = 0.91) and SVM (87%, F1 = 0.87). The convergent performance across three structurally different algorithm families indicates that the readiness signal reflects a consistent attitudinal pattern rather than an artifact of any single modeling assumption. Feature importance analysis consistently identified Perceived Benefits (32%) and Technology Awareness (25%) as the dominant predictive features, followed by Organizational Readiness (20%), Perceived Barriers (15%), and Respondent Profile (8%). Attitudinal readiness mapping classified 62% of stakeholders as High readiness, 28% as Moderate, and 10% as Low, providing an exploratory attitudinal segmentation framework to assist construction managers in prioritizing capacity-building investments, subject to longitudinal behavioral validation. The study also finds that awareness of digital technology consistently outpaces Organizational Readiness for implementation, a pattern consistent with findings from analogous developing-country construction contexts.

Currently, China’s construction industry is evolving towards green, low-carbon, and intelligent development, with prefabricated construction playing a crucial role in this transformation. However, existing risk research on prefabricated residential projects remains fragmented, focusing on isolated risk factors or specific construction stages, and lacking systematic analysis of risk interactions. To address this gap, this study introduces vulnerability theory and develops a structural equation model (SEM) to quantitatively analyze risk factors. Through literature review, case analysis, and expert surveys, 28 vulnerability risk factors were identified and categorized into exposure, sensitivity, and adaptability. A SEM was constructed and validated using 365 valid survey responses. The results reveal that resource deficiency, human factors, and lack of management capacity exert the strongest overall impact on project risk, with path analysis further demonstrating how these vulnerability dimensions propagate risk through interconnected pathways. This study makes two key contributions: First, it extends vulnerability theory to prefabricated residential construction through a three-dimensional analytical framework; second, it demonstrates the integration of vulnerability theory with SEM, offering a replicable quantitative approach. The findings provide project managers with a diagnostic tool for identifying systemic weaknesses and prioritizing interventions.

Rapid population growth is increasing housing demand and accelerating the expansion of the built environment in Egypt. However, practical and sustainable residential building decarbonization remains constrained by limited supplies of supplementary cementitious materials, limited structural timber resources, code restrictions on cement reduction, and cost sensitivity. This study evaluates two Egyptian multi-unit residential case studies—one affordable housing project and one middle-class housing project—to assess whether wall-system substitution can reduce both embodied and operational carbon under local material, code, and cost constraints. An integrated BIM-based digital twin workflow was used to link quantity takeoff, finite-element structural assessment, and whole-building energy simulation. An architectural BIM model was used for material quantification, wall-system definition, and energy-model inputs. A structural model was used to assess the effects of reducing wall density on reinforcement and concrete demand under gravity and seismic load combinations. Operational performance was assessed through cooling-focused energy simulations under hot-arid climatic conditions representative of Egypt’s new desert cities. Alternative wall systems were then evaluated through scenario- based material substitution and revised structural and energy assessments. The results show that reinforcement, concrete, and wall- core materials account for about 80% of total embodied carbon, while cooling accounts for about 72% of operational emissions. Non-structural cement uses, mainly mortars and finishes, account for 36% of total cement demand, ranging from 161 to 229 tons per building across the two case studies. Replacing conventional partition walls with lightweight, energy-efficient alternatives reduced embodied carbon by up to 35.2%, operational carbon by about 15.7% to 16.5%, and total life-cycle carbon by about 17.4% to 17.5% over a 60- year service life. The average savings per building corresponded to avoiding about 30 tons of steel, 165 m³ of ready-mix concrete, and 191 m³ of mortar, with net cost savings of about 3.15 million EGP per building. These results identify a practical pathway toward more sustainable, lower-carbon Egyptian residential buildings without increasing project cost.