Implementation of Elman neural networks for enhancing reliability of integrated bridge deterioration model

Bu, GP; Lee, JH; Guan, H; Loo, YC; Blumenstein, M

Implementation of Elman neural networks for enhancing reliability of integrated bridge deterioration model

Bu, GP Lee, JH Guan, H Loo, YC Blumenstein, M

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Publication Type:: Journal Article
Citation:: Australian Journal of Structural Engineering, 2014, 15 (1), pp. 51 - 63
Issue Date:: 2014-02-12

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Field	Value	Language
dc.contributor.author	Bu, GP	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	2014-02-12	en_US
dc.identifier.citation	Australian Journal of Structural Engineering, 2014, 15 (1), pp. 51 - 63	en_US
dc.identifier.issn	1328-7982	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118024
dc.description.abstract	Probabilistic modelling is one of the most prominent techniques in bridge deterioration forecast. It can be classified into two types, namely, state- and time-based models. Reliability of both modelling techniques in forecasting long-term performance rely heavily on sufficient amount of bridge condition rating data being available together with well-distributed deterioration pattern over the age of bridge. However, it can be problematic when the available condition rating records are insufficient. In order to overcome this problem, an integrated deterioration method incorporating both the state- and time-based models has recently been developed. Despite such development and advancement, certain issues still remain with some cases of given condition data that cannot be used to produce reliable long-term performance curve. Aiming to achieve enhanced prediction performance, an Elman neural networks (ENN) technique is incorporated in the integrated method to replace the third-order polynomial regression function, the latter being the core component for long-term prediction in the state-based model. In the present study, the ENN are able to generate more reliable deterioration patterns than a typical deterministic method. The results demonstrate that the integrated method incorporating ENN are more effective in handling various situations of condition data quantities and distributions for generating long-term performance curves. © Institution of Engineers Australia, 2014.	en_US
dc.relation.ispartof	Australian Journal of Structural Engineering	en_US
dc.relation.isbasedon	10.7158/S12-046.2014.15.1	en_US
dc.title	Implementation of Elman neural networks for enhancing reliability of integrated bridge deterioration model	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	15	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	1202 Building	en_US
utslib.for	1204 Engineering Design	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.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

Probabilistic modelling is one of the most prominent techniques in bridge deterioration forecast. It can be classified into two types, namely, state- and time-based models. Reliability of both modelling techniques in forecasting long-term performance rely heavily on sufficient amount of bridge condition rating data being available together with well-distributed deterioration pattern over the age of bridge. However, it can be problematic when the available condition rating records are insufficient. In order to overcome this problem, an integrated deterioration method incorporating both the state- and time-based models has recently been developed. Despite such development and advancement, certain issues still remain with some cases of given condition data that cannot be used to produce reliable long-term performance curve. Aiming to achieve enhanced prediction performance, an Elman neural networks (ENN) technique is incorporated in the integrated method to replace the third-order polynomial regression function, the latter being the core component for long-term prediction in the state-based model. In the present study, the ENN are able to generate more reliable deterioration patterns than a typical deterministic method. The results demonstrate that the integrated method incorporating ENN are more effective in handling various situations of condition data quantities and distributions for generating long-term performance curves. © Institution of Engineers Australia, 2014.

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http://hdl.handle.net/10453/118024