An integrated risk assessment model for safe Arctic navigation
Safety is always the first concern for a ship's navigation in the Arctic. Ships navigating in the Arctic may face two main accident scenarios, i.e., getting stuck in the ice and ship-ice collision. More specifically, excessive speed may cause severe hull damage, while a very low speed may lead...
Published in: | Transportation Research Part A: Policy and Practice |
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Online Access: | https://doi.org/10.1016/j.tra.2020.10.017 https://research.chalmers.se/en/publication/520323 |
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ftchalmersuniv:oai:research.chalmers.se:520323 2023-05-15T14:36:02+02:00 An integrated risk assessment model for safe Arctic navigation Zhang, Chi Zhang, Di Zhang, Mingyang Lang, Xiao Mao, Wengang 2020 text https://doi.org/10.1016/j.tra.2020.10.017 https://research.chalmers.se/en/publication/520323 unknown http://dx.doi.org/10.1016/j.tra.2020.10.017 https://research.chalmers.se/en/publication/520323 Transport Systems and Logistics Marine Engineering Oceanography Hydrology Water Resources Ship-ice collision Risk assessment Bayesian Network Stuck in the ice Safe speed 2020 ftchalmersuniv https://doi.org/10.1016/j.tra.2020.10.017 2022-12-11T07:12:14Z Safety is always the first concern for a ship's navigation in the Arctic. Ships navigating in the Arctic may face two main accident scenarios, i.e., getting stuck in the ice and ship-ice collision. More specifically, excessive speed may cause severe hull damage, while a very low speed may lead to a high probability of getting stuck in the ice. Based on this multi-risk perspective, an integrated risk assessment model was proposed to obtain the overall risk using the Bayesian Network (BN), in which the probabilities of accident occurrence and the severities of the possible consequences for ships getting stuck in the ice and for ship-ice collision could be estimated. Then, the voyage data collected from Yong Sheng's Arctic sailing in 2013 were inputted into the integrated risk assessment model to perform a case study. A sensitivity analysis was performed to validate the proposed model and reveal the inherent mechanisms behind these two accidental scenarios. The proposed model can be applied to identify the safe speed for Arctic navigation under various ice conditions, a duty that is traditionally performed by well-trained crew members, but which entails too many uncertainties. The results can, to some extent, provide useful suggestions for navigators. They are imperative in supporting decision-making to shape the Arctic policy and to enhance the safety of Arctic shipping. Other/Unknown Material Arctic Chalmers University of Technology: Chalmers research Arctic Transportation Research Part A: Policy and Practice 142 101 114 |
institution |
Open Polar |
collection |
Chalmers University of Technology: Chalmers research |
op_collection_id |
ftchalmersuniv |
language |
unknown |
topic |
Transport Systems and Logistics Marine Engineering Oceanography Hydrology Water Resources Ship-ice collision Risk assessment Bayesian Network Stuck in the ice Safe speed |
spellingShingle |
Transport Systems and Logistics Marine Engineering Oceanography Hydrology Water Resources Ship-ice collision Risk assessment Bayesian Network Stuck in the ice Safe speed Zhang, Chi Zhang, Di Zhang, Mingyang Lang, Xiao Mao, Wengang An integrated risk assessment model for safe Arctic navigation |
topic_facet |
Transport Systems and Logistics Marine Engineering Oceanography Hydrology Water Resources Ship-ice collision Risk assessment Bayesian Network Stuck in the ice Safe speed |
description |
Safety is always the first concern for a ship's navigation in the Arctic. Ships navigating in the Arctic may face two main accident scenarios, i.e., getting stuck in the ice and ship-ice collision. More specifically, excessive speed may cause severe hull damage, while a very low speed may lead to a high probability of getting stuck in the ice. Based on this multi-risk perspective, an integrated risk assessment model was proposed to obtain the overall risk using the Bayesian Network (BN), in which the probabilities of accident occurrence and the severities of the possible consequences for ships getting stuck in the ice and for ship-ice collision could be estimated. Then, the voyage data collected from Yong Sheng's Arctic sailing in 2013 were inputted into the integrated risk assessment model to perform a case study. A sensitivity analysis was performed to validate the proposed model and reveal the inherent mechanisms behind these two accidental scenarios. The proposed model can be applied to identify the safe speed for Arctic navigation under various ice conditions, a duty that is traditionally performed by well-trained crew members, but which entails too many uncertainties. The results can, to some extent, provide useful suggestions for navigators. They are imperative in supporting decision-making to shape the Arctic policy and to enhance the safety of Arctic shipping. |
author |
Zhang, Chi Zhang, Di Zhang, Mingyang Lang, Xiao Mao, Wengang |
author_facet |
Zhang, Chi Zhang, Di Zhang, Mingyang Lang, Xiao Mao, Wengang |
author_sort |
Zhang, Chi |
title |
An integrated risk assessment model for safe Arctic navigation |
title_short |
An integrated risk assessment model for safe Arctic navigation |
title_full |
An integrated risk assessment model for safe Arctic navigation |
title_fullStr |
An integrated risk assessment model for safe Arctic navigation |
title_full_unstemmed |
An integrated risk assessment model for safe Arctic navigation |
title_sort |
integrated risk assessment model for safe arctic navigation |
publishDate |
2020 |
url |
https://doi.org/10.1016/j.tra.2020.10.017 https://research.chalmers.se/en/publication/520323 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
http://dx.doi.org/10.1016/j.tra.2020.10.017 https://research.chalmers.se/en/publication/520323 |
op_doi |
https://doi.org/10.1016/j.tra.2020.10.017 |
container_title |
Transportation Research Part A: Policy and Practice |
container_volume |
142 |
container_start_page |
101 |
op_container_end_page |
114 |
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1766308737232928768 |