A data-driven system-theoretic Bayesian network framework for probabilistic safety assessment of passenger vessels
Passenger vessel operations present a high-consequence environment where a paradox has emerged: incident frequency is decreasing, yet catastrophic severity is not. This trend exposes the inadequacy of existing risk models, which are typically localized, and reliant on subjective expert elicitation....
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad de Oviedo (UNIOVI) |
| Repositorio: | RUO. Repositorio Institucional de la Universidad de Oviedo |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:ruo_________::3ee1fda1df27643c8f66d9d8f23352b8 |
| Acceso en línea: | https://hdl.handle.net/10651/84004 https://dx.doi.org/10.1016/j.ress.2026.112350 |
| Access Level: | acceso abierto |
| Palabra clave: | Bayesian network System-Theoretic process analysis Passenger ship safety Accident causation Data-driven modeling Human and organizational factors |
| Sumario: | Passenger vessel operations present a high-consequence environment where a paradox has emerged: incident frequency is decreasing, yet catastrophic severity is not. This trend exposes the inadequacy of existing risk models, which are typically localized, and reliant on subjective expert elicitation. This study develops a robust, data-driven risk assessment framework by synergizing System-Theoretic Process Analysis (STPA) with a Bayesian Network (BN), grounded in a novel database of 235 official European accident reports. STPA defines the BN’s causal topology, ensuring theoretical coherence and mitigating the epistemic uncertainty and bias of conventional expert-led modeling. Sensitivity analysis reveals the probabilistic primacy of latent systemic precursors, identifying Structural Failure and Defective Maintenance as dominant risk control points. The analysis moves beyond simplistic attributions of “human error”, revealing how operational failures like COLREGs infringements are symptoms of distinct causal pathways dependent on vessel type and operational conditions. The resulting model is a quantitative instrument that identifies the most probable pathways to catastrophe, offering an objective foundation for transitioning from reactive compliance to proactive, data-driven safety governance. |
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