Swivel bridge construction (SBC) technology has significant advantages in building bridges that span existing railway lines (ERLs), but it also entails complex risks from ‘skylight’ windows and railway boundaries. A notable challenge is the relationships and interdependencies among these risks, which collectively increase safety hazards through mutual influence. Prior research has typically focused on mitigating the risks inherent in particular tasks or operations, with less emphasis on the risks from interdependencies. A novel framework was developed to explore this research gap by integrating fuzzy logic, interpretive structural modeling (ISM) and the cross-impact matrix multiplication applied to classification (MICMAC) approach (Fuzzy-ISM-MICMAC) to investigate the relationships and interdependencies among the risks of SBC spanning ERLs and the critical points of risk control. Furthermore, the data collected from a literature review, a case analysis and expert interviews resulted in 28 risk factors. Then, the application of ISM distilled complex risk relationships into a clear, multilevel hierarchy, accurately illustrating the complex interrelationships among risk factors. Combined with MICMAC analysis, the research findings indicate that preliminary works such as the selection of construction technology, scheme design, construction rehearsal and the provision of safety facilities are essential for preventing risks in SBC spanning ERLs. We applied these findings to the double T-structure swivel construction of the Xiaojizhuang Bridge, where its practicality and efficiency were thoroughly tested and validated. This research’s critical contribution is identifying, clarifying and visualizing the interrelationships of the complex risk factors of SBC spanning ERLs and providing specific solutions for safety management in similar bridge construction projects. The research results and risk control recommendations offer valuable insights for managing other swivel bridge construction risks.

The rapidly evolving domain of artificial intelligence (AI) is significantly influencing the green building (GB) sector, acting as a catalyst for green building technology innovation (GBTI). Notably, unlike AI applications in green buildings (AI-in-GB), AI-driven GBTI positions AI as the central force, promoting and leading novel technological breakthroughs. Although research has been conducted in AI-in-GB, there remains a lack of in-depth analysis on AI-driven GBTI advancements. To address this gap, this study comprehensively reviews the existing research in AI-driven GBTI, systematically organizing and analyzing the knowledge structure, thematic evolution, research paradigms, and potential future research directions. This study conducts bibliometric analyses on 151 research publications sourced from Scopus using VOSviewer and CiteSpace, capturing the temporal characteristics, research hotspots, and frontiers of research in this area. Additionally, based on dynamic topic modeling, this study analyzes 86 representative articles, identifying three key research themes and their evolution trends, systematically elucidating the knowledge framework within the field. Through further discussion, this study reveals four core research paradigms and proposes three potential future research directions, providing theoretical support and guidance for its continued development. This study is the first to focus on AI-driven GBTI, contributing to a comprehensive understanding and expanding the knowledge domain of GBTI.

There is a high level of construction safety risk shared among construction workers in tunneling projects due to collaboration on the narrow and semi-enclosed construction site. However, no one has reported on this. Therefore, this paper proposes a new network model to explore risk-sharing features among construction workers based on a two-mode network. That model represents a new personnel safety management tool to provide suitable risk mitigation for tunneling projects. First, the work breakdown structure (WBS)–risk breakdown structure (RBS) method was employed to identify construction activities, risk resources, and construction safety risk factors (CSRFs). Subsequently, the two-mode WBS–RBS matrix was further established. The construction workers’ sets were determined based on the organization breakdown structure (OBS)–WBS method and a two-mode OBS–WBS matrix was established. By applying the construction activities in the WBS tree carrying the CSRFs as the link, a two-mode OBS–RBS network was established by converting the two-mode WBS–RBS and OBS–WBS matrices. Hence, taking CSRFs allocated by several construction workers as a basis for network generation, the construction workers’ risk-sharing network was further established. Centrality analysis identified the network characteristics and determined the most important construction workers in risk network. For example, this model was employed to explore the whole network characteristics of the Shangwu Tunnel and identify the workers in key positions in the risk-sharing network. Expert interviews demonstrated the model’s rationality and practicality. The results show that each construction worker’s safety risk-sharing degree in the Shangwu tunnel differed and reached varying levels. However, the staff from the engineering management department were in the key position of the risk-sharing network. Collectively, this model can help construction workers understand their risk-sharing degree to improve their safety awareness and adjust their attitude toward safety accordingly. Moreover, this strategy provides project managers with the necessary information to more effectively allocate safety resources and to be cognizant of the safety quality of each construction worker according to the different risk-sharing degrees.

Purpose: A persistent gap exists between abundant safety documentation and its effective conversion into actionable knowledge in bridge construction safety. While knowledge modeling offers a promising solution, unresolved issues remain regarding knowledge granularity and the boundaries of model advantage. This study develops a Bridge Engineering Construction Safety Knowledge Model (BECS-KM) to narrow this gap and to investigate these challenges. Design/methodology/approach: BECS-KM is built using ontology and knowledge graph techniques grounded in codes and standards. A multilayered representation structure is incorporated to balance knowledge granularity, and a comparative experiment is conducted to evaluate the strengths boundaries of BECS-KM versus ChatGPT-5. Findings: In terms of knowledge modeling, the multilayered structure effectively balances knowledge granularity within BECS-KM. Regarding knowledge application, the comparative experiment demonstrates the superiority of BECS-KM (F1 = 0.97) over ChatGPT-5 (F1 = 0.83) of domain knowledge queries. Originality/value: This study makes three contributions: (1) transforms fragmented and heterogeneous safety documentation into a structured and operational knowledge resource, (2) provides a multilayered representation structure to balance knowledge granularity and (3) empirically delineates the performance boundary of domain knowledge models against general LLMs. Together, these contributions establish BECS-KM as a robust foundation for future intelligent safety knowledge services.