A comprehensive risk assessment framework for mooring risks at hydrocarbon berths using fuzzy rule-based Bayesian network and multi-attribute decision-making

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Özet

Mooring operations -especially at hydrocarbon berths- are critical components of the marine and offshore industry. They usually involve securing the berths of ships carrying valuable cargo and ensuring the safety of personnel, assets, and the environment. For this purpose, a comprehensive risk assessment framework for mooring operations at hydrocarbon berths is proposed in this study. This framework helps evaluate risks using a rule-based Bayesian network. In the assessment of mooring risks, four risk parameters of severity, occurrence, detection, and maintenance are considered to construct the BN structure of each mooring risk. These parameters are weighted with the aid of the fuzzy Best Worst Method. Hereafter, a fuzzy rule-based system is constructed by incorporating BN to determine a risk priority score. The framework also develops mitigation strategies to maintain effective risk management for safe and secure maritime transportation. Sensitivity analyzes and comparison studies were conducted to test the validity of the proposed comprehensive risk management framework. The study reveals that the most critical risk is associated with Technical Failures (Q1) in the cluster pertaining to the automation of Quick Release Hooks. This risk stems from technical malfunctions in automation systems, encompassing sensors and control mechanisms, potentially resulting in the unintended release of mooring lines. The second highest priority risk is linked to Human Error (M1) in the mooring risks cluster, attributed to human errors such as inadequate training, miscommunication, and procedural mistakes during mooring operations, posing risks of accidents and damage to ships and infrastructure. Conversely, the least significant risk, Redundancy (Q5), focuses on redundancy. This risk is associated with automation and underscores the importance of implementing redundancy mechanisms to ensure the safe continuation of mooring operations in the face of system failures. In conclusion, the proposed comprehensive risk assessment framework offers a systematic approach to evaluate and prioritize mooring risks at hydrocarbon berths. The study’s findings emphasize the critical importance of addressing technical malfunctions in the automation of Quick Release Hooks and human errors during mooring operations. By identifying the most significant risks and developing mitigation strategies, this framework contributes to enhancing the safety and security of maritime transportation, particularly in the context of hydrocarbon berths.

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https://rdlopen.rawdatalibrary.net/r/munzur/2115Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.1007/s00477-024-02809-wYayıncı resmi DOI adresi
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Tam Kayıt
AlanDeğerDil
dc.contributor.authorDemirel, Hakan
dc.contributor.authorBaşhan, Veysi
dc.contributor.authorYucesan, Melih
dc.contributor.authorGul, Muhammet
dc.date.accessioned2026-10-28T14:54:57.362476834Z
dc.date.available2026-10-28T14:54:57.362476834Z
dc.date.issued2024
dc.description.abstractMooring operations -especially at hydrocarbon berths- are critical components of the marine and offshore industry. They usually involve securing the berths of ships carrying valuable cargo and ensuring the safety of personnel, assets, and the environment. For this purpose, a comprehensive risk assessment framework for mooring operations at hydrocarbon berths is proposed in this study. This framework helps evaluate risks using a rule-based Bayesian network. In the assessment of mooring risks, four risk parameters of severity, occurrence, detection, and maintenance are considered to construct the BN structure of each mooring risk. These parameters are weighted with the aid of the fuzzy Best Worst Method. Hereafter, a fuzzy rule-based system is constructed by incorporating BN to determine a risk priority score. The framework also develops mitigation strategies to maintain effective risk management for safe and secure maritime transportation. Sensitivity analyzes and comparison studies were conducted to test the validity of the proposed comprehensive risk management framework. The study reveals that the most critical risk is associated with Technical Failures (Q1) in the cluster pertaining to the automation of Quick Release Hooks. This risk stems from technical malfunctions in automation systems, encompassing sensors and control mechanisms, potentially resulting in the unintended release of mooring lines. The second highest priority risk is linked to Human Error (M1) in the mooring risks cluster, attributed to human errors such as inadequate training, miscommunication, and procedural mistakes during mooring operations, posing risks of accidents and damage to ships and infrastructure. Conversely, the least significant risk, Redundancy (Q5), focuses on redundancy. This risk is associated with automation and underscores the importance of implementing redundancy mechanisms to ensure the safe continuation of mooring operations in the face of system failures. In conclusion, the proposed comprehensive risk assessment framework offers a systematic approach to evaluate and prioritize mooring risks at hydrocarbon berths. The study’s findings emphasize the critical importance of addressing technical malfunctions in the automation of Quick Release Hooks and human errors during mooring operations. By identifying the most significant risks and developing mitigation strategies, this framework contributes to enhancing the safety and security of maritime transportation, particularly in the context of hydrocarbon berths.
dc.description.sponsorshipIstanbul Teknik Üniversitesi, IT, (FHD-2024-45410)
dc.description.sponsorshipIstanbul Teknik Üniversitesi, IT
dc.identifier.doi10.1007/s00477-024-02809-w
dc.identifier.eissn1436-3259
dc.identifier.endpage4414
dc.identifier.issn1436-3240
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85203167395
dc.identifier.startpage4393
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/munzur/2115
dc.identifier.volume38
dc.identifier.wosWOS:001306201200002
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofStochastic Environmental Research and Risk Assessment
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBayesian network
dc.subjectFuzzy best worst method
dc.subjectHydrocarbon berth
dc.subjectMooring risk
dc.subjectRule-based system
dc.subject.classificationEngineering, Environmental
dc.subject.classificationEngineering, Civil
dc.subject.classificationEnvironmental Sciences
dc.subject.classificationStatistics & Probability
dc.subject.classificationWater Resources
dc.subject.classificationEngineering
dc.subject.classificationEnvironmental Sciences & Ecology
dc.subject.classificationMathematics
dc.subject.otherArctic vehicles
dc.subject.otherCoal deposits
dc.subject.otherCost reduction
dc.subject.otherCrime
dc.subject.otherDrug products plants
dc.subject.otherGas oils
dc.subject.otherGas plants
dc.subject.otherHealth hazards
dc.subject.otherHuman resource management
dc.subject.otherLosses
dc.subject.otherMarine industry
dc.subject.otherMineral oils
dc.subject.otherMooring cables
dc.subject.otherOil shale
dc.subject.otherPetroleum tar
dc.subject.otherPlant life extension
dc.subject.otherResource valuation
dc.subject.otherRisk analysis
dc.subject.otherRisk assessment
dc.subject.otherRisk management
dc.subject.otherRisk perception
dc.subject.otherWaterway transportation
dc.subject.otherBAD methods
dc.subject.otherBayesia n networks
dc.subject.otherComprehensive risks
dc.subject.otherFuzzy best bad method
dc.subject.otherHuman errors
dc.subject.otherHydrocarbon berth
dc.subject.otherMitigation strategy
dc.subject.otherMooring risk
dc.subject.otherRisk assessment framework
dc.subject.otherRules based systems
dc.subject.otherBayesian analysis
dc.subject.otherdecision making
dc.subject.otherenvironmental monitoring
dc.subject.otherfuzzy mathematics
dc.subject.otherhydrocarbon
dc.subject.otherRedundancy
dc.titleA comprehensive risk assessment framework for mooring risks at hydrocarbon berths using fuzzy rule-based Bayesian network and multi-attribute decision-making
dc.typeArticle
rdl.citation.openalex13
rdl.citation.scopus11
rdl.citation.wos11
rdl.oa.statusclosed
rdl.scopus.quartileQ1
rdl.sdgSDG09
rdl.source.indexSCOPUS
rdl.source.indexWOS
rdl.wos.indexScience Citation Index Expanded (SCI-EXPANDED)