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....

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Detalles Bibliográficos
Autores: Díaz Secades, Luis Alfonso, Sánchez González, Aitana
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
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spelling A data-driven system-theoretic Bayesian network framework for probabilistic safety assessment of passenger vesselsDíaz Secades, Luis AlfonsoSánchez González, AitanaBayesian networkSystem-Theoretic process analysisPassenger ship safetyAccident causationData-driven modelingHuman and organizational factorsPassenger 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.Elsevier20262026-02-02journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articlehttps://hdl.handle.net/10651/84004https://dx.doi.org/10.1016/j.ress.2026.112350reponame:RUO. Repositorio Institucional de la Universidad de Oviedoinstname:Universidad de Oviedo (UNIOVI)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:dnet:ruo_________::3ee1fda1df27643c8f66d9d8f23352b82026-06-07T06:38:51Z
dc.title.none.fl_str_mv A data-driven system-theoretic Bayesian network framework for probabilistic safety assessment of passenger vessels
title A data-driven system-theoretic Bayesian network framework for probabilistic safety assessment of passenger vessels
spellingShingle A data-driven system-theoretic Bayesian network framework for probabilistic safety assessment of passenger vessels
Díaz Secades, Luis Alfonso
Bayesian network
System-Theoretic process analysis
Passenger ship safety
Accident causation
Data-driven modeling
Human and organizational factors
title_short A data-driven system-theoretic Bayesian network framework for probabilistic safety assessment of passenger vessels
title_full A data-driven system-theoretic Bayesian network framework for probabilistic safety assessment of passenger vessels
title_fullStr A data-driven system-theoretic Bayesian network framework for probabilistic safety assessment of passenger vessels
title_full_unstemmed A data-driven system-theoretic Bayesian network framework for probabilistic safety assessment of passenger vessels
title_sort A data-driven system-theoretic Bayesian network framework for probabilistic safety assessment of passenger vessels
dc.creator.none.fl_str_mv Díaz Secades, Luis Alfonso
Sánchez González, Aitana
author Díaz Secades, Luis Alfonso
author_facet Díaz Secades, Luis Alfonso
Sánchez González, Aitana
author_role author
author2 Sánchez González, Aitana
author2_role author
dc.subject.none.fl_str_mv Bayesian network
System-Theoretic process analysis
Passenger ship safety
Accident causation
Data-driven modeling
Human and organizational factors
topic Bayesian network
System-Theoretic process analysis
Passenger ship safety
Accident causation
Data-driven modeling
Human and organizational factors
description 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.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-02-02
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10651/84004
https://dx.doi.org/10.1016/j.ress.2026.112350
url https://hdl.handle.net/10651/84004
https://dx.doi.org/10.1016/j.ress.2026.112350
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUO. Repositorio Institucional de la Universidad de Oviedo
instname:Universidad de Oviedo (UNIOVI)
instname_str Universidad de Oviedo (UNIOVI)
reponame_str RUO. Repositorio Institucional de la Universidad de Oviedo
collection RUO. Repositorio Institucional de la Universidad de Oviedo
repository.name.fl_str_mv
repository.mail.fl_str_mv
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