Prediction of human error probability during the hydrocarbon road tanker loading operation using a hybrid technique of fuzzy sets, Bayesian network and CREAM
Objectives. The hydrocarbon road tanker loading operation is vulnerable to human error. The present study aimed to develop a methodology for predicting human error probabilities (HEPs) in various subtasks of this operation. Methods. First, task analysis was performed using hierarchal task analysis. Then, HEP was calculated using a hybrid technique of fuzzy set theory (FST), Bayesian network (BN) and cognitive reliability and error analysis method (CREAM). FST was used for handling uncertainties regarding common performance conditions (CPCs) and the BN was employed for modeling the interrelationships among CPCs and HEPs. The weighted sum algorithm was used for quantifying conditional probability tables in the network. Results. Twenty-six subtasks were required for completing the road tanker loading operation. Investigating the internal parts of the tanker before the loading operation and attaching the ground rode clamp were the subtasks with highest HEPs. Working conditions and crew collaboration were the CPCs with the highest contribution to these errors. HEP was most sensitive to crew collaboration. Conclusion. Improving collaboration among the driver, site operators and control room operators, as well as increasing the knowledge of the road tanker driver regarding the hazards of incompatible chemicals, are the best practices for reducing HEPs in this operation.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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| Enthalten in: |
International journal of occupational safety and ergonomics : JOSE - 28(2022), 3 vom: 16. Sept., Seite 1342-1352 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ghasemi, Fakhradin [VerfasserIn] |
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| Links: |
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| Themen: |
Accident prevention |
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| Anmerkungen: |
Date Completed 29.06.2022 Date Revised 29.06.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1080/10803548.2021.1889877 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM321522524 |
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| 100 | 1 | |a Ghasemi, Fakhradin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Prediction of human error probability during the hydrocarbon road tanker loading operation using a hybrid technique of fuzzy sets, Bayesian network and CREAM |
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| 500 | |a Date Completed 29.06.2022 | ||
| 500 | |a Date Revised 29.06.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Objectives. The hydrocarbon road tanker loading operation is vulnerable to human error. The present study aimed to develop a methodology for predicting human error probabilities (HEPs) in various subtasks of this operation. Methods. First, task analysis was performed using hierarchal task analysis. Then, HEP was calculated using a hybrid technique of fuzzy set theory (FST), Bayesian network (BN) and cognitive reliability and error analysis method (CREAM). FST was used for handling uncertainties regarding common performance conditions (CPCs) and the BN was employed for modeling the interrelationships among CPCs and HEPs. The weighted sum algorithm was used for quantifying conditional probability tables in the network. Results. Twenty-six subtasks were required for completing the road tanker loading operation. Investigating the internal parts of the tanker before the loading operation and attaching the ground rode clamp were the subtasks with highest HEPs. Working conditions and crew collaboration were the CPCs with the highest contribution to these errors. HEP was most sensitive to crew collaboration. Conclusion. Improving collaboration among the driver, site operators and control room operators, as well as increasing the knowledge of the road tanker driver regarding the hazards of incompatible chemicals, are the best practices for reducing HEPs in this operation | ||
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| 700 | 1 | |a Ghasemi, Arash |e verfasserin |4 aut | |
| 700 | 1 | |a Kalatpour, Omid |e verfasserin |4 aut | |
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