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A Behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments

A Behavioral probabilistic risk assessment framework for managing autonomous underwater vehicle deployments
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 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.
0739-0572
1689-1703
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Griffiths, Gwyn
a0447dd5-c7cd-4bc9-b945-0da7ab236a08
Ferguson, James
55cf35a1-7b64-4192-ba85-10ebe2a78e1f
Hopkin, David
7b69e34c-9411-4e85-ac49-4e33c20b6357
Mills, Richard
04e6879b-fecb-41a7-b425-a1a0caf1adc2
Pederson, Richard
d15ccc95-1a73-400b-96d9-787be80e031a
MacNeil, Erin
afb63318-e326-4fcc-b319-084d6f86eb80
Brito, Mario
82e798e7-e032-4841-992e-81c6f13a9e6c
Griffiths, Gwyn
a0447dd5-c7cd-4bc9-b945-0da7ab236a08
Ferguson, James
55cf35a1-7b64-4192-ba85-10ebe2a78e1f
Hopkin, David
7b69e34c-9411-4e85-ac49-4e33c20b6357
Mills, Richard
04e6879b-fecb-41a7-b425-a1a0caf1adc2
Pederson, Richard
d15ccc95-1a73-400b-96d9-787be80e031a
MacNeil, Erin
afb63318-e326-4fcc-b319-084d6f86eb80

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).

Record type: Article

Abstract

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.

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Published date: November 2012
Organisations: Civil Maritime & Env. Eng & Sci Unit, Ocean Technology and Engineering

Identifiers

Local EPrints ID: 342034
URI: https://eprints.soton.ac.uk/id/eprint/342034
ISSN: 0739-0572
PURE UUID: c8a80782-27df-4347-a5c0-63b624b9beb9

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Date deposited: 10 Aug 2012 09:03
Last modified: 18 Jul 2017 05:31

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Contributors

Author: Mario Brito
Author: Gwyn Griffiths
Author: James Ferguson
Author: David Hopkin
Author: Richard Mills
Author: Richard Pederson
Author: Erin MacNeil

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