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 extension of the hybrid flux-splitting finite-volume (HFF) scheme to the shallow water equations with source terms is presented. Based on the monotonic upstream schemes for conservation laws (MUSCL) method, the scheme is second-order-accurate both in space and time. An accurate and efficient surface gradient method (SGM), in conjunction with the HFF scheme, is adopted for the discretization of source terms, including the bed slopes and friction slopes. The resulting scheme has several desirable properties: ease of implementation, satisfaction of entropy condition, sharp shock resolution and preservation of well-balancing. The HFF scheme with SGM is verified through the simulations of steady transcritical flow over a hump and steady flow over an irregular bed. Besides, the effects of the limiter functions, the grid sizes and the Manning roughness coefficients on the simulated results are investigated for the steady transcritical flow problems. Using the laboratory measurements, the scheme is also applied to the dam-break flows: with an adverse slope, with a triangular hump, and with a constriction. Furthermore, the HFF scheme is employed in the simulation of typhoon flood flow with natural-irregular river topography to demonstrate the practical engineering application. The results show good agreements compared with the exact solutions, the experimental data and the field measurements.

With the growth of artificial intelligence (AI) and Industry 4.0, construction management has entered a phase of rapid digital transformation. In order to effectively adopt digital applications of construction management, this paper aims to identify the specific applications of AI in construction management from the perspective of Construction 4.0, especially when applying technologies from Industry 4.0. A scoping review methodology was used to explore the limited literature in this research area. 60 articles were selected to analyze the state of the art of AI applications in construction management, especially for schedule management, cost management, quality management, and health and safety management. This review shows that AI has mainly been used in the preliminary design and construction phases of the above management areas, and proposes a research framework to highlight the contemporary development and needs for AI integration in construction management. The main contributions of this paper are its practical exploration of AI applications in construction management, its human-centered approach to AI adoption, and the introduction of a novel research framework to guide industry practitioners in AI integration.