Long-term performance of bridge elements using integrated deterioration method incorporating elman neural network

Bu, G; Lee, J; Guan, H; Loo, YC; Blumenstein, M

Long-term performance of bridge elements using integrated deterioration method incorporating elman neural network

Bu, G Lee, J Guan, H Loo, YC Blumenstein, M

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Publication Type:: Journal Article
Citation:: Applied Mechanics and Materials, 2012, 204-208 pp. 1980 - 1987
Issue Date:: 2012-11-27

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Field	Value	Language
dc.contributor.author	Bu, G	en_US
dc.contributor.author	Lee, J	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	2012-11-27	en_US
dc.identifier.citation	Applied Mechanics and Materials, 2012, 204-208 pp. 1980 - 1987	en_US
dc.identifier.isbn	9783037854846	en_US
dc.identifier.issn	1660-9336	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121429
dc.description.abstract	Currently, probabilistic deterioration modeling techniques have been employed in most state-of-the-art Bridge Management Systems (BMSs) to predict future bridge condition ratings. As confirmed by many researchers, the reliability of the probabilistic deterioration models rely heavily on the sufficient amount of condition data together with their well-distributed historical deterioration patterns over time. However, inspection records are usually insufficient in most bridge agencies. As a result, a typical standalone probabilistic model (e.g. state-based or time-based model) is not promising for forecasting a reliable bridge long-term performance. To minimise the shortcomings of lacking condition data, an integrated method using a combination of state- and time-based techniques has recently been developed and has demonstrated an improved performance as compared to the standalone techniques. However, certain shortcomings still remain in the integrated method which necessities further improvement. In this study, the core component of the state-based modeling is replaced by an Elman Neural Networks (ENN). The integrated method incorporated with ENN is more effective in predicting long-term bridge performance as compared to the typical deterioration modeling techniques. As part of comprehensive case studies, this paper presents the deterioration prediction of 52 bridge elements with material types of Steel (S), Timber (T) and Other (O). These elements are selected from 94 bridges (totaling 4,115 inspection records). The enhanced reliability of the proposed integrated method incorporating ENN is confirmed. © (2012) Trans Tech Publications, Switzerland.	en_US
dc.relation.ispartof	Applied Mechanics and Materials	en_US
dc.relation.isbasedon	10.4028/www.scientific.net/AMM.204-208.1980	en_US
dc.title	Long-term performance of bridge elements using integrated deterioration method incorporating elman neural network	en_US
dc.type	Journal Article
utslib.citation.volume	204-208	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	09 Engineering	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
pubs.volume	204-208	en_US

Abstract:

Currently, probabilistic deterioration modeling techniques have been employed in most state-of-the-art Bridge Management Systems (BMSs) to predict future bridge condition ratings. As confirmed by many researchers, the reliability of the probabilistic deterioration models rely heavily on the sufficient amount of condition data together with their well-distributed historical deterioration patterns over time. However, inspection records are usually insufficient in most bridge agencies. As a result, a typical standalone probabilistic model (e.g. state-based or time-based model) is not promising for forecasting a reliable bridge long-term performance. To minimise the shortcomings of lacking condition data, an integrated method using a combination of state- and time-based techniques has recently been developed and has demonstrated an improved performance as compared to the standalone techniques. However, certain shortcomings still remain in the integrated method which necessities further improvement. In this study, the core component of the state-based modeling is replaced by an Elman Neural Networks (ENN). The integrated method incorporated with ENN is more effective in predicting long-term bridge performance as compared to the typical deterioration modeling techniques. As part of comprehensive case studies, this paper presents the deterioration prediction of 52 bridge elements with material types of Steel (S), Timber (T) and Other (O). These elements are selected from 94 bridges (totaling 4,115 inspection records). The enhanced reliability of the proposed integrated method incorporating ENN is confirmed. © (2012) Trans Tech Publications, Switzerland.

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http://hdl.handle.net/10453/121429