Data-driven hierarchical causal modeling of risk propagation in bridge operations
Evidence from 132 accidents in China
Bibliographic Data
| ID | 22072036 |
|---|---|
| Authors | Peng Peng (0000-0001-5726-0872, Hefei University of Technology), Zuocai Wang, Zuo‐Cai Wang (0000-0001-8792-8108, Anhui Jianzhu University, corresponding author), Peng Cui (0000-0002-4566-6223, Nanjing Forestry University, corresponding author), Xiaokang Hu (0000-0002-8298-700X, Hefei University of Technology), Junfeng Yao (0000-0002-2330-7406, Hefei University of Technology), Sainan Lyu (0000-0001-6811-5101, Anhui Jianzhu University) |
| Year | 2025 |
| Volume | 13 |
| Pages | 1686346-1686346 |
| Publication date | 2025-09-26 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2025.1686346 |
| PMID | 41080884 |
| OpenAlex | W4414552841 |
| Language | EN |
| References cited | 68 |
Aging bridges worldwide face growing safety challenges due to extended service life and environmental stressors. However, most existing studies lack a systemic perspective and mainly rely on fragmented, expert-driven assessments. Such approaches fail to capture the interplay of risk factors. This gap in understanding the interactions and propagation of risks limits the development of effective safety strategies for bridge operation. To address this gap, this study aims to identify and structure key risk factors affecting bridge safety in operational contexts by adopting a data-driven hierarchical model. Utilizing 132 officially documented accident reports from national safety databases in China (2007–2024), text mining techniques are applied to extract lexical risk items, which are subsequently refined through expert workshops and association rule mining to capture factor relationships. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, integrated with Adversarial Interpretive Structural Modeling (AISM), is applied to construct a multi-level causal hierarchy of safety risks. The findings reveal 19 distinct risk factors, structured into seven levels with 20 transmission pathways. Notably, insufficient informatization management and unqualified managerial competence are identified as foundational factors, while overweight vehicle passage, inadequate inspection and maintenance, and geological and meteorological hazards emerge as direct triggers of safety incidents. The constructed hierarchy demonstrates a clear propagation chain from latent management deficiencies to observable surface-level hazards. Theoretically, the study advances the understanding of risk interaction mechanisms by integrating quantitative data analysis with expert interpretation. Practically, it provides infrastructure safety managers with a structured roadmap for targeted interventions, emphasizing the importance of enhancing digital management systems, traffic load regulation, and emergency preparedness in bridge operation contexts
Bayesian network · Bridge maintenance · Construction site safety · Hierarchy · Poison control · Risk assessment · Risk management · Infrastructure Maintenance and Monitoring · Occupational Health and Safety Research · Risk and Safety Analysis
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| Citation velocity | historical |
|---|---|
| Highly cited | No |