Modelling long-term bridge deterioration at structural member level using Artificial Intelligence techniques

Hong, G; Wang, XP; Lee, J; Loo, YC; Blumenstein, M

Modelling long-term bridge deterioration at structural member level using Artificial Intelligence techniques

Hong, G Wang, XP Lee, J Loo, YC Blumenstein, M

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Publication Type:: Journal Article
Citation:: Applied Mechanics and Materials, 2011, 99-100 pp. 444 - 453
Issue Date:: 2011-10-11

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Field	Value	Language
dc.contributor.author	Hong, G	en_US
dc.contributor.author	Wang, XP	en_US
dc.contributor.author	Lee, J	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	2011-10-11	en_US
dc.identifier.citation	Applied Mechanics and Materials, 2011, 99-100 pp. 444 - 453	en_US
dc.identifier.isbn	9783037852453	en_US
dc.identifier.issn	1660-9336	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119492
dc.description.abstract	Efficient use of public funds for structural integrity of bridge networks requires an effective bridge asset management technology. To achieve this, a reliable deterioration model is essential in any Bridge Management System (BMS). The deterioration rate is calculated based on historical condition ratings obtained from the structural element-level bridge inspections. Although most bridge authorities have previously conducted inspection and maintenance tasks, these past inspection records are incompatible with what are required by a typical BMS as input. Such incompatibility is a major cause for the deficiency of the current BMS outcomes. Artificial Intelligence (AI)-based bridge deterioration model has recently been developed to minimise uncertainties in predicting deterioration of structural bridge members (e.g. beams, piers etc). This model contains two components: (1) using Neural Network-based Backward Prediction Model (BPM) to generate unavailable historical condition ratings; and (2) using Time Delay Neural Network (TDNN) to perform long-term performance prediction of bridge structural members. However new problems have emerged in the process of TDNN prediction. This is because the BPM-generated condition ratings are used together with the actual condition ratings. The incompatibility between the two sets of data produces unreliable prediction outcomes during the TDNN process. This research is thus to develop a new process based on the existing method, thereby overcoming the abovementioned problems. To achieve this, the actual overall condition ratings are replaced by the BPM forward predicted condition ratings. Consequently, the outcome of this study can improve accuracy of long-term bridge deterioration prediction. © (2011) Trans Tech Publications.	en_US
dc.relation.ispartof	Applied Mechanics and Materials	en_US
dc.relation.isbasedon	10.4028/www.scientific.net/AMM.99-100.444	en_US
dc.title	Modelling long-term bridge deterioration at structural member level using Artificial Intelligence techniques	en_US
dc.type	Journal Article
utslib.citation.volume	99-100	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	99-100	en_US

Abstract:

Efficient use of public funds for structural integrity of bridge networks requires an effective bridge asset management technology. To achieve this, a reliable deterioration model is essential in any Bridge Management System (BMS). The deterioration rate is calculated based on historical condition ratings obtained from the structural element-level bridge inspections. Although most bridge authorities have previously conducted inspection and maintenance tasks, these past inspection records are incompatible with what are required by a typical BMS as input. Such incompatibility is a major cause for the deficiency of the current BMS outcomes. Artificial Intelligence (AI)-based bridge deterioration model has recently been developed to minimise uncertainties in predicting deterioration of structural bridge members (e.g. beams, piers etc). This model contains two components: (1) using Neural Network-based Backward Prediction Model (BPM) to generate unavailable historical condition ratings; and (2) using Time Delay Neural Network (TDNN) to perform long-term performance prediction of bridge structural members. However new problems have emerged in the process of TDNN prediction. This is because the BPM-generated condition ratings are used together with the actual condition ratings. The incompatibility between the two sets of data produces unreliable prediction outcomes during the TDNN process. This research is thus to develop a new process based on the existing method, thereby overcoming the abovementioned problems. To achieve this, the actual overall condition ratings are replaced by the BPM forward predicted condition ratings. Consequently, the outcome of this study can improve accuracy of long-term bridge deterioration prediction. © (2011) Trans Tech Publications.

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http://hdl.handle.net/10453/119492