Uncertainty propagation in quantitative risk assessment modeling for fire in road tunnels

Meng, Q; Qu, X

Uncertainty propagation in quantitative risk assessment modeling for fire in road tunnels

Meng, Q Qu, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2012, 42 (6), pp. 1454 - 1464
Issue Date:: 2012-05-08

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Field	Value	Language
dc.contributor.author	Meng, Q	en_US
dc.contributor.author	Qu, X https://orcid.org/0000-0003-0973-3756	en_US
dc.date.issued	2012-05-08	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2012, 42 (6), pp. 1454 - 1464	en_US
dc.identifier.issn	1094-6977	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115165
dc.description.abstract	Road tunnels are critical transportation infrastructures that provide underground passageways for motorists and commuters. Fire in road tunnels in combination with tunnel safety provisions failure may lead to catastrophic consequences, and thus, necessitates a robust and reliable approach to assess tunnel risks. This article proposes a quantitative risk assessment model for fire in road tunnel by taking into consideration two types of uncertainties. A Monte Carlo-based estimation method is developed to propagate parameter uncertainty in quantitative risk assessment model consisting of event tree analysis as well as consequence estimation models. The percentile-based individual risks and α-cut-based societal risks are put up and the risk indices are proven to be very useful for tunnel operators with distinct risk attitudes to assess the safety level of a road tunnel. Finally, the proposed research methodology is applied to Singapore KPE road tunnels. © 1998-2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews	en_US
dc.relation.isbasedon	10.1109/TSMCC.2012.2190982	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Uncertainty propagation in quantitative risk assessment modeling for fire in road tunnels	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	42	en_US
utslib.for	090507 Transport Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	42	en_US

Abstract:

Road tunnels are critical transportation infrastructures that provide underground passageways for motorists and commuters. Fire in road tunnels in combination with tunnel safety provisions failure may lead to catastrophic consequences, and thus, necessitates a robust and reliable approach to assess tunnel risks. This article proposes a quantitative risk assessment model for fire in road tunnel by taking into consideration two types of uncertainties. A Monte Carlo-based estimation method is developed to propagate parameter uncertainty in quantitative risk assessment model consisting of event tree analysis as well as consequence estimation models. The percentile-based individual risks and α-cut-based societal risks are put up and the risk indices are proven to be very useful for tunnel operators with distinct risk attitudes to assess the safety level of a road tunnel. Finally, the proposed research methodology is applied to Singapore KPE road tunnels. © 1998-2012 IEEE.

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http://hdl.handle.net/10453/115165