A system for bridge network condition assessment and prediction

Samali, B; Crews, KI; Aboura, K; Ariyaratne, W; Manamperi, PB

A system for bridge network condition assessment and prediction

Samali, B

Crews, KI

Aboura, K Ariyaratne, W Manamperi, PB

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Publication Type:: Conference Proceeding
Citation:: Incorporating Sustainable Practice in Mechanics of Structures and Materials - Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials, 2011, pp. 739 - 744
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Samali, B https://orcid.org/0000-0002-3426-7745	en_US
dc.contributor.author	Crews, KI https://orcid.org/0000-0003-1450-1423	en_US
dc.contributor.author	Aboura, K	en_US
dc.contributor.author	Ariyaratne, W	en_US
dc.contributor.author	Manamperi, PB	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Incorporating Sustainable Practice in Mechanics of Structures and Materials - Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials, 2011, pp. 739 - 744	en_US
dc.identifier.isbn	9780415616577	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16438
dc.description.abstract	Traditionally, bridge management systems were designed using a Markov chain decision model. Based on the analysis of 15 years of bridge inspection data, we apply the gamma process instead. After extracting all relevant information, enough data was collected on the condition paths of elements to build a deterioration model.The element conditions followa time period in full condition then start deteriorating.We consider a random variable for the last time the condition was observed to be 100%.We consider the stochastic deterioration process that follows. The amalgamation of the two part process through probabilistic arguments creates a new stochastic process. The novel stochastic process characteristics are derived through the data to provide a predictive model for the element, bridge and network conditions.We showcase a software solution for bridge network condition assessment, monitoring and prediction. © 2011 Taylor & Francis Group, London.	en_US
dc.relation.ispartof	Incorporating Sustainable Practice in Mechanics of Structures and Materials - Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials	en_US
dc.title	A system for bridge network condition assessment and prediction	en_US
dc.type	Conference Proceeding
utslib.for	0905 Civil Engineering	en_US
dc.location.activity	Melbourne, Australia	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	closed_access
pubs.publication-status	Published	en_US

Abstract:

Traditionally, bridge management systems were designed using a Markov chain decision model. Based on the analysis of 15 years of bridge inspection data, we apply the gamma process instead. After extracting all relevant information, enough data was collected on the condition paths of elements to build a deterioration model.The element conditions followa time period in full condition then start deteriorating.We consider a random variable for the last time the condition was observed to be 100%.We consider the stochastic deterioration process that follows. The amalgamation of the two part process through probabilistic arguments creates a new stochastic process. The novel stochastic process characteristics are derived through the data to provide a predictive model for the element, bridge and network conditions.We showcase a software solution for bridge network condition assessment, monitoring and prediction. © 2011 Taylor & Francis Group, London.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/16438