A Behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments
The deployment of a deep-diving long-range autonomous underwater vehicle (AUV) is a complex operation that requires the use of a risk informed decision-making process. Operational risk assessment is heavily dependent on expert subjective judgment. Expert judgments can be elicited either mathematical...
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Online Access: | https://eprints.soton.ac.uk/342034/ https://eprints.soton.ac.uk/342034/1/jtech-d-12-00005%25252E1.pdf |
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ftsouthampton:oai:eprints.soton.ac.uk:342034 2023-07-30T04:01:54+02:00 A Behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments Brito, Mario Griffiths, Gwyn Ferguson, James Hopkin, David Mills, Richard Pederson, Richard MacNeil, Erin 2012-11 text https://eprints.soton.ac.uk/342034/ https://eprints.soton.ac.uk/342034/1/jtech-d-12-00005%25252E1.pdf en eng https://eprints.soton.ac.uk/342034/1/jtech-d-12-00005%25252E1.pdf Brito, Mario, Griffiths, Gwyn, Ferguson, James, Hopkin, David, Mills, Richard, Pederson, Richard and MacNeil, Erin (2012) A Behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments. Journal of Atmospheric and Oceanic Technology, 29 (11), 1689-1703. (doi:10.1175/JTECH-D-12-00005.1 <http://dx.doi.org/10.1175/JTECH-D-12-00005.1>). Article PeerReviewed 2012 ftsouthampton https://doi.org/10.1175/JTECH-D-12-00005.1 2023-07-09T21:40:49Z The deployment of a deep-diving long-range autonomous underwater vehicle (AUV) is a complex operation that requires the use of a risk informed decision-making process. Operational risk assessment is heavily dependent on expert subjective judgment. Expert judgments can be elicited either mathematically or behaviorally. During mathematical elicitation experts are kept separate and provide their assessment individually. These are then mathematically combined to create a judgment that represents the group view. The limitation with this approach is that experts do not have the opportunity to discuss different views and thus remove bias from their assessment. In this paper a Bayesian behavioral approach to estimate and manage AUV operational risk is proposed. At an initial workshop, behavioral aggregation, reaching agreement on distributions of risks for faults or incidents, is followed by an agreed initial estimate of the likelihood of success of proposed risk mitigation methods. Post-expedition, a second workshop assesses the new data, compares observed to predicted risk, thus updating the prior estimate using Bayes’ rule. This feedback further educates the experts and assesses the actual effectiveness of the mitigation measures. Applying this approach to an AUV campaign in ice-covered waters in the Arctic showed that maximum error between the predicted and the actual risk was 9% and that the experts’ assessments of the effectiveness of risk mitigation led to a maximum of 24% in risk reduction. Article in Journal/Newspaper Arctic ice covered waters University of Southampton: e-Prints Soton Arctic Journal of Atmospheric and Oceanic Technology 29 11 1689 1703 |
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University of Southampton: e-Prints Soton |
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ftsouthampton |
language |
English |
description |
The deployment of a deep-diving long-range autonomous underwater vehicle (AUV) is a complex operation that requires the use of a risk informed decision-making process. Operational risk assessment is heavily dependent on expert subjective judgment. Expert judgments can be elicited either mathematically or behaviorally. During mathematical elicitation experts are kept separate and provide their assessment individually. These are then mathematically combined to create a judgment that represents the group view. The limitation with this approach is that experts do not have the opportunity to discuss different views and thus remove bias from their assessment. In this paper a Bayesian behavioral approach to estimate and manage AUV operational risk is proposed. At an initial workshop, behavioral aggregation, reaching agreement on distributions of risks for faults or incidents, is followed by an agreed initial estimate of the likelihood of success of proposed risk mitigation methods. Post-expedition, a second workshop assesses the new data, compares observed to predicted risk, thus updating the prior estimate using Bayes’ rule. This feedback further educates the experts and assesses the actual effectiveness of the mitigation measures. Applying this approach to an AUV campaign in ice-covered waters in the Arctic showed that maximum error between the predicted and the actual risk was 9% and that the experts’ assessments of the effectiveness of risk mitigation led to a maximum of 24% in risk reduction. |
format |
Article in Journal/Newspaper |
author |
Brito, Mario Griffiths, Gwyn Ferguson, James Hopkin, David Mills, Richard Pederson, Richard MacNeil, Erin |
spellingShingle |
Brito, Mario Griffiths, Gwyn Ferguson, James Hopkin, David Mills, Richard Pederson, Richard MacNeil, Erin A Behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments |
author_facet |
Brito, Mario Griffiths, Gwyn Ferguson, James Hopkin, David Mills, Richard Pederson, Richard MacNeil, Erin |
author_sort |
Brito, Mario |
title |
A Behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments |
title_short |
A Behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments |
title_full |
A Behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments |
title_fullStr |
A Behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments |
title_full_unstemmed |
A Behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments |
title_sort |
behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments |
publishDate |
2012 |
url |
https://eprints.soton.ac.uk/342034/ https://eprints.soton.ac.uk/342034/1/jtech-d-12-00005%25252E1.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic ice covered waters |
genre_facet |
Arctic ice covered waters |
op_relation |
https://eprints.soton.ac.uk/342034/1/jtech-d-12-00005%25252E1.pdf Brito, Mario, Griffiths, Gwyn, Ferguson, James, Hopkin, David, Mills, Richard, Pederson, Richard and MacNeil, Erin (2012) A Behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments. Journal of Atmospheric and Oceanic Technology, 29 (11), 1689-1703. (doi:10.1175/JTECH-D-12-00005.1 <http://dx.doi.org/10.1175/JTECH-D-12-00005.1>). |
op_doi |
https://doi.org/10.1175/JTECH-D-12-00005.1 |
container_title |
Journal of Atmospheric and Oceanic Technology |
container_volume |
29 |
container_issue |
11 |
container_start_page |
1689 |
op_container_end_page |
1703 |
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1772812642442805248 |