Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network
- Submitting institution
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Liverpool John Moores University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 1984
- Type
- D - Journal article
- DOI
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10.1016/j.ress.2020.107070
- Title of journal
- Reliability Engineering and System Safety
- Article number
- 107070
- First page
- -
- Volume
- 203
- Issue
- -
- ISSN
- 0951-8320
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2020
- URL
-
-
- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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B - LOOM
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Supported by EU-H2020 (RESET-730888, €1.4m 2017-2021) and four national key technology grants (2017YFE0118000, NSFC-51920105014, CSS-201706950084 and 2018AHB003), this work has contributed to the first Northeast Asian Logistics Award in recognition of outstanding contribution to logistics research development in the region (EiC of AJSL, ktyeo@inu.ac.kr). It has led to a keynote address fully-funded by the Norwegian Research Council (9th TARG workshop, chairman Salman.Nazir@usn.no). It has been used as a key reference to improve human reliability and safety in operations of autonomous ships from on-shore control rooms by Kongsberg Group (Manager, stig.wiggen@kdi.kongsberg.com).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -