Cheng, J., Wang, B., Cao, C. orcid.org/0000-0002-6668-4164 et al. (1 more author) (2023) A quantitative risk assessment model for domino accidents of hazardous chemicals transportation. Processes, 11 (5). 1442. ISSN 2227-9717
Abstract
In recent years, hazardous materials transportation accidents have received increasing attention. Previous studies have focused on accidents involving a single vehicle. When vehicles loaded with materials gather on a stretch of road, a potential domino accident might cause terrible incidents. This paper prompts a quantitative risk assessment (QRA) model to estimate the risk of multi-vehicle incidents. The model calculates the possibility of leakage and explosion of hazardous chemicals using a dynamic Bayesian network (DBN). For different types of hazardous chemicals, the model uses event trees to list different scenarios and analyzes the probability of domino accidents caused by each scenario. The FN-curve and potential loss of life (PLL) are used as an index to evaluate social risk. A case involving multiple vehicles in the JinShan District, Shanghai, is analyzed. The result of the case shows that the state of the driver, the type of road, weather factors and the distance between vehicles have vital impacts on the societal risk resulting from hazardous materials transportation accidents.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | QRA; hazardous materials transportation; domino effect; dynamic Bayesian network; FN curve |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Nov 2023 15:34 |
Last Modified: | 29 Nov 2023 15:34 |
Status: | Published |
Publisher: | MDPI AG |
Refereed: | Yes |
Identification Number: | 10.3390/pr11051442 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:205614 |