Dynamic occupational risk model for offshore operations in harsh environments
The expansion of offshore oil exploitation into remote areas (e.g., Arctic) with harsh environments has significantly increased occupational risks. Among occupational accidents, slips, trips and falls from height (STFs) account for a significant portion. Thus, a dynamic risk assessment of the three...
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ftunivtasecite:oai:ecite.utas.edu.au:120389 2023-05-15T15:07:22+02:00 Dynamic occupational risk model for offshore operations in harsh environments Song, G Khan, FI Wang, H Leighton, S Yuan, Z Liu, H 2016 https://doi.org/10.1016/j.ress.2016.01.021 http://ecite.utas.edu.au/120389 en eng Elsevier Sci Ltd http://dx.doi.org/10.1016/j.ress.2016.01.021 Song, G and Khan, FI and Wang, H and Leighton, S and Yuan, Z and Liu, H, Dynamic occupational risk model for offshore operations in harsh environments, Reliability Engineering and System Safety, 150 pp. 58-64. ISSN 0951-8320 (2016) [Refereed Article] http://ecite.utas.edu.au/120389 Medical and Health Sciences Public Health and Health Services Environmental and Occupational Health and Safety Refereed Article PeerReviewed 2016 ftunivtasecite https://doi.org/10.1016/j.ress.2016.01.021 2019-12-13T22:19:28Z The expansion of offshore oil exploitation into remote areas (e.g., Arctic) with harsh environments has significantly increased occupational risks. Among occupational accidents, slips, trips and falls from height (STFs) account for a significant portion. Thus, a dynamic risk assessment of the three main occupational accidents is meaningful to decrease offshore occupational risks. Bow-tie Models (BTs) were established in this study for the risk analysis of STFs considering extreme environmental factors. To relax the limitations of BTs, Bayesian networks (BNs) were developed based on BTs to dynamically assess risks of STFs. The occurrence and consequence probabilities of STFs were respectively calculated using BTs and BNs, and the obtained probabilities verified BNs' rationality and advantage. Furthermore, the probability adaptation for STFs was accomplished in a specific scenario with BNs. Finally, posterior probabilities of basic events were achieved through diagnostic analysis, and critical basic events were analyzed based on their posterior likelihood to cause occupational accidents. The highlight is systematically analyzing STF accidents for offshore operations and dynamically assessing their risks considering the harsh environmental factors. This study can guide the allocation of prevention resources and benefit the safety management of offshore operations. Article in Journal/Newspaper Arctic eCite UTAS (University of Tasmania) Arctic Reliability Engineering & System Safety 150 58 64 |
institution |
Open Polar |
collection |
eCite UTAS (University of Tasmania) |
op_collection_id |
ftunivtasecite |
language |
English |
topic |
Medical and Health Sciences Public Health and Health Services Environmental and Occupational Health and Safety |
spellingShingle |
Medical and Health Sciences Public Health and Health Services Environmental and Occupational Health and Safety Song, G Khan, FI Wang, H Leighton, S Yuan, Z Liu, H Dynamic occupational risk model for offshore operations in harsh environments |
topic_facet |
Medical and Health Sciences Public Health and Health Services Environmental and Occupational Health and Safety |
description |
The expansion of offshore oil exploitation into remote areas (e.g., Arctic) with harsh environments has significantly increased occupational risks. Among occupational accidents, slips, trips and falls from height (STFs) account for a significant portion. Thus, a dynamic risk assessment of the three main occupational accidents is meaningful to decrease offshore occupational risks. Bow-tie Models (BTs) were established in this study for the risk analysis of STFs considering extreme environmental factors. To relax the limitations of BTs, Bayesian networks (BNs) were developed based on BTs to dynamically assess risks of STFs. The occurrence and consequence probabilities of STFs were respectively calculated using BTs and BNs, and the obtained probabilities verified BNs' rationality and advantage. Furthermore, the probability adaptation for STFs was accomplished in a specific scenario with BNs. Finally, posterior probabilities of basic events were achieved through diagnostic analysis, and critical basic events were analyzed based on their posterior likelihood to cause occupational accidents. The highlight is systematically analyzing STF accidents for offshore operations and dynamically assessing their risks considering the harsh environmental factors. This study can guide the allocation of prevention resources and benefit the safety management of offshore operations. |
format |
Article in Journal/Newspaper |
author |
Song, G Khan, FI Wang, H Leighton, S Yuan, Z Liu, H |
author_facet |
Song, G Khan, FI Wang, H Leighton, S Yuan, Z Liu, H |
author_sort |
Song, G |
title |
Dynamic occupational risk model for offshore operations in harsh environments |
title_short |
Dynamic occupational risk model for offshore operations in harsh environments |
title_full |
Dynamic occupational risk model for offshore operations in harsh environments |
title_fullStr |
Dynamic occupational risk model for offshore operations in harsh environments |
title_full_unstemmed |
Dynamic occupational risk model for offshore operations in harsh environments |
title_sort |
dynamic occupational risk model for offshore operations in harsh environments |
publisher |
Elsevier Sci Ltd |
publishDate |
2016 |
url |
https://doi.org/10.1016/j.ress.2016.01.021 http://ecite.utas.edu.au/120389 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
http://dx.doi.org/10.1016/j.ress.2016.01.021 Song, G and Khan, FI and Wang, H and Leighton, S and Yuan, Z and Liu, H, Dynamic occupational risk model for offshore operations in harsh environments, Reliability Engineering and System Safety, 150 pp. 58-64. ISSN 0951-8320 (2016) [Refereed Article] http://ecite.utas.edu.au/120389 |
op_doi |
https://doi.org/10.1016/j.ress.2016.01.021 |
container_title |
Reliability Engineering & System Safety |
container_volume |
150 |
container_start_page |
58 |
op_container_end_page |
64 |
_version_ |
1766338894035419136 |