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Realising advanced risk assessment of vessel traffic flows near offshore wind farms

Yu, Q, Liu, K, Chang, C-H and Yang, Z (2020) Realising advanced risk assessment of vessel traffic flows near offshore wind farms. Reliability Engineering & System Safety, 203. ISSN 0951-8320

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Abstract

Offshore wind farms (OWFs) are relatively new installations at sea. Accident records related to vessel collisions with OWFs are insufficient to support a full quantitative risk analysis using traditional probabilistic approaches. This paper aims to develop a semi-qualitative risk model to assess the vessel-turbine collision risks by incorporating Bayesian networks (BN) with evidential reasoning (ER) approaches. First, a BN is trained based on Automatic Identification Systems (AIS) data to characterise real vessel traffic flows, including the detailed information and relationships between traffic flow parameters. Secondly, through synthesising expert judgements by ER, five risk factors influencing the probability and consequence of vessel-turbine collisions are identified (incl. the associated conditional probabilities) in the established BN. Finally, the updated BN with ER input is tested through ten real scenarios and validated by processing a validity framework. This paper pioneers the use of multi-data-driven BNs to characterise traffic flows and assess vessel-turbine collision risk for navigational safety assurance near OWFs. The research findings provide empirical evidence of using ER to supplement BN subjective data to advance its applications in risk analysis.

Item Type: Article
Uncontrolled Keywords: 01 Mathematical Sciences, 09 Engineering, 15 Commerce, Management, Tourism and Services
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TC Hydraulic engineering. Ocean engineering
Divisions: Maritime & Mechanical Engineering
Publisher: Elsevier BV
Date Deposited: 14 Jul 2020 14:14
Last Modified: 14 Jul 2020 14:14
DOI or Identification number: 10.1016/j.ress.2020.107086
URI: http://researchonline.ljmu.ac.uk/id/eprint/13299

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