Stochastic processes for modelling bridge deterioration

Aboura, K; Samali, B; Crews, K; Li, J

Stochastic processes for modelling bridge deterioration

Aboura, K Samali, B

Crews, K

Li, J

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Publication Type:: Conference Proceeding
Citation:: Futures in Mechanics of Structures and Materials - Proceedings of the 20th Australasian Conference on the Mechanics of Structures and Materials, ACMSM20, 2008, pp. 533 - 538
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Aboura, K	en_US
dc.contributor.author	Samali, B https://orcid.org/0000-0002-3426-7745	en_US
dc.contributor.author	Crews, K https://orcid.org/0000-0003-1450-1423	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Futures in Mechanics of Structures and Materials - Proceedings of the 20th Australasian Conference on the Mechanics of Structures and Materials, ACMSM20, 2008, pp. 533 - 538	en_US
dc.identifier.isbn	9780415491969	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10971
dc.description.abstract	Traditionally, bridge management systems were designed using Markov chain models. Recently, researchers applied the gamma process successfully to structural deterioration problems. The stochastic process captures the temporal variability of degradation, and has been applied to a range of problems in structures. We report on a study for the modelling of the condition of bridges in the state of NSW. The study encompasses large amounts of data spanning more than 15 years. We argue for the applicability of the gamma process and other stochastic processes. While the gamma process has been adopted in the past decade on grounds of mathematical tractability and physical motivation, we also observe another distribution for the deterioration at different times. The finding promotes the stochastic process modelling direction taken in the past decade and brings forth new models for the time-dependent reliability analysis of bridges. © 2009 Taylor & Francis Group, London.	en_US
dc.relation.ispartof	Futures in Mechanics of Structures and Materials - Proceedings of the 20th Australasian Conference on the Mechanics of Structures and Materials, ACMSM20	en_US
dc.title	Stochastic processes for modelling bridge deterioration	en_US
dc.type	Conference Proceeding
utslib.for	090506 Structural Engineering	en_US
dc.location.activity	Toowoomba	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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

Traditionally, bridge management systems were designed using Markov chain models. Recently, researchers applied the gamma process successfully to structural deterioration problems. The stochastic process captures the temporal variability of degradation, and has been applied to a range of problems in structures. We report on a study for the modelling of the condition of bridges in the state of NSW. The study encompasses large amounts of data spanning more than 15 years. We argue for the applicability of the gamma process and other stochastic processes. While the gamma process has been adopted in the past decade on grounds of mathematical tractability and physical motivation, we also observe another distribution for the deterioration at different times. The finding promotes the stochastic process modelling direction taken in the past decade and brings forth new models for the time-dependent reliability analysis of bridges. © 2009 Taylor & Francis Group, London.

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http://hdl.handle.net/10453/10971