ANN-based structural element performance model for reliable bridge asset management

Son, JB; Lee, JH; Guan, H; Loo, YC; Blumenstein, M

ANN-based structural element performance model for reliable bridge asset management

Son, JB Lee, JH Guan, H Loo, YC Blumenstein, M

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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. 775 - 780
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Son, JB	en_US
dc.contributor.author	Lee, JH	en_US
dc.contributor.author	Guan, H	en_US
dc.contributor.author	Loo, YC	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	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. 775 - 780	en_US
dc.identifier.isbn	9780415616577	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120594
dc.description.abstract	Bridge Management Systems (BMSs) have been developed to assist in the management of a large bridge network. Historical condition ratings obtained from bridge inspections are major resources for predicting future deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for predicting reliable future structural performance. To alleviate this problem, A Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings which is crucial for bridge deterioration models to be able to predict more accurate solutions. Nevertheless, there are still considerable limitations in the existing bridge deterioration models. In view of this, feasibility study ofTime Delay Neural Network (TDNN) using BPM-generated historical condition ratings is conducted as an alternative to existing bridge deterioration models. It is anticipated that the TDNN using BPM-generated data can lead to further improvement of the current BMS outcome. © 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	ANN-based structural element performance model for reliable bridge asset management	en_US
dc.type	Conference Proceeding
utslib.for	0803 Computer Software	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/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Bridge Management Systems (BMSs) have been developed to assist in the management of a large bridge network. Historical condition ratings obtained from bridge inspections are major resources for predicting future deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for predicting reliable future structural performance. To alleviate this problem, A Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings which is crucial for bridge deterioration models to be able to predict more accurate solutions. Nevertheless, there are still considerable limitations in the existing bridge deterioration models. In view of this, feasibility study ofTime Delay Neural Network (TDNN) using BPM-generated historical condition ratings is conducted as an alternative to existing bridge deterioration models. It is anticipated that the TDNN using BPM-generated data can lead to further improvement of the current BMS outcome. © 2011 Taylor & Francis Group, London.

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http://hdl.handle.net/10453/120594