Traditional construction safety management, reliant on manual inspections and heuristic judgments, increasingly fails to address the dynamic, multi-dimensional risks of modern projects, perpetuating fragmented safety governance and reactive hazard mitigation. This study proposes an integrated building information modeling (BIM)–AI platform to unify safety supervision across the project lifecycle, synthesizing spatial-temporal data from BIM with AI-driven probabilistic models and IoT-enabled real-time monitoring for sustainable construction safety management. Employing a Design Science Research methodology, the platform’s phase-agnostic architecture bridges technical–organizational divides, while the Multilayer Neural Risk Coupling Assessment framework quantifies interdependencies among structural, environmental, and human risk factors. Prototype testing in real-world projects demonstrates improved risk detection accuracy, reduced reliance on manual processes, and enhanced cross-departmental collaboration. The system transitions safety regimes from compliance-based protocols to proactive, data-empowered governance. This approach offers scalability across diverse projects. The BIM-AI intelligent fusion platform proposed in this study builds an intelligent construction paradigm with synergistic development of safety governance and sustainability through whole lifecycle risk coupling analysis and real-time dynamic monitoring, which realizes a proactive safety supervision system while significantly reducing construction waste and accident prevention mechanisms.

The rapid evolution of information technology has positioned digital intelligence as a transformative force across socio-economic domains, necessitating rigorous scholarly examination of its applications and implications. This paper systematically explores the digital intelligence empowerment in Building Lifecycle Management (BLM) under the framework of Construction 4.0. Employing a mixed-methods approach, the research combines a systematic literature review with bibliometric visualization analysis using CiteSpace to map the intellectual landscape, identify key technological drivers (for example, Building Information Modeling, Internet of Things, artificial intelligence, and blockchain), and elucidate integration mechanisms across planning, design, construction, and operational phases. A comparative case study of BLM adoption further demonstrates the transformative impacts of digital intelligence on project efficiency, sustainability, and safety. The research highlights the role of digital intelligence in addressing industry challenges, including resource waste (global construction generates 30% of total waste), safety risks, and stagnant productivity, while fostering innovation and sustainable development. This study advances a holistic model for digital transformation in BLM, offering actionable insights for stakeholders to bridge the academia–industry divide and prioritize strategic investments in interoperable systems, workforce upskilling, and governance frameworks.

The capacity to identify risks during the pre-contract phase is crucial for effective contract management. Among the various factors that can influence this ability, emotions play a significant role in determining the risk recognition capabilities of individuals. This study aims to investigate the complex interplay between emotional states, personality traits, learning styles, and risk recognition during contract review with physiological and psychological responses. Firstly, a theoretical framework that delineates the relationship among the demographic and emotional factors and risk recognition performance during contract review is proposed. Secondly, an experiment is conducted to record the physiological and psychological responses. Finally, a hierarchical regression model is employed. The results indicated that emotional valence and arousal significantly influence risk recognition performance (p < 0.001), while individual factors such as personality traits and risk propensity indirectly affect risk recognition performance through the mediating role of emotions (p < 0.01). The research contributes to the existing literature by elucidating the indirect pathways through which individual characteristics influence risk perception. These insights can inform sustainable risk management strategies, helping organizations and individuals make more informed and effective decisions in contract management.

Accurately predicting the degree of hydration in cement systems blended with slag and fly ash is difficult when experimental data are limited. Empirical kinetic models typically need recalibration for each specific blend, while purely data-driven approaches often fail to maintain physical consistency. In this work, we present a physics-informed neural network for predicting isothermal hydration by incorporating the Avrami–Erofeev–Arrhenius ordinary differential equation into the training loss and using a composition-focused sub-network to relate blend proportions to blend-specific kinetic parameters. The model was trained and tested on 29,379 calorimetry data points from 77 multi-component cement systems gathered from two open-access datasets. For previously unseen test systems, it reached an $R^2$ of 0.9864 and a symmetric mean absolute percentage error (sMAPE) of 25.3%. Its overall sMAPE was lower than that of a neural network with the same architecture but without physics-based constraints (34.9%); stage-resolved analysis showed that the largest difference occurred during early hydration. Across eight random seeds, the physics constraint did not confer a data-efficiency or training-stability advantage over the same-architecture network. The distinguishing features of the PINN are instead its explicit ODE and monotonicity regularization and its composition-conditioned effective parameters. Although random forest regression produced lower pointwise error, it failed to maintain physically consistent hydration-rate behavior. Overall, the framework provides a composition-conditioned surrogate that incorporates kinetic regularization within the composition range investigated.