Improving the reliability of a bridge deterioration model for a decision support system using artificial intelligence

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

Improving the reliability of a bridge deterioration model for a decision support system using artificial intelligence

Lee, J Guan, H Blumenstein, M

Loo, YC

Permalink

Publication Type:: Chapter
Citation:: Structural Health Monitoring in Australia, 2011, pp. 201 - 223
Issue Date:: 2011-01-01

Closed Access

	Filename	Description	Size
	Chapter8_final.pdf	Accepted Manuscript version	585.81 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Lee, J	en_US
dc.contributor.author	Guan, H	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.contributor.author	Loo, YC	en_US
dc.date.issued	2011-01-01	en_US
dc.identifier.citation	Structural Health Monitoring in Australia, 2011, pp. 201 - 223	en_US
dc.identifier.isbn	9781617288609	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117640
dc.description.abstract	Bridge Management Systems (BMSs) have been developed since the early 1990s, as a decision support system (DSS) for effective Maintenance, Repair and Rehabilitation (MR&R) activities in a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations through BMSs. However, available historical condition ratings are very limited in all bridge agencies. This constitutes the major barrier for achieving reliable future structural performances. To alleviate this problem, a Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. Its reliability has been verified using existing condition ratings obtained from the Maryland Department of Transportation, USA. This is achieved through establishing the correlation between known condition ratings and related non-bridge factors such as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to determine the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this chapter, the prediction accuracy based on four and nine BPM-generated historical condition ratings as input data are also compared, using traditional bridge deterioration modeling techniques, i.e., deterministic and stochastic methods. The condition ratings are available. This implies that the BPM can be utilized to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models. ©2011 Nova Science Publishers, Inc. All rights reserved.	en_US
dc.relation.ispartof	Structural Health Monitoring in Australia	en_US
dc.title	Improving the reliability of a bridge deterioration model for a decision support system using artificial intelligence	en_US
dc.type	Chapter
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 since the early 1990s, as a decision support system (DSS) for effective Maintenance, Repair and Rehabilitation (MR&R) activities in a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations through BMSs. However, available historical condition ratings are very limited in all bridge agencies. This constitutes the major barrier for achieving reliable future structural performances. To alleviate this problem, a Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. Its reliability has been verified using existing condition ratings obtained from the Maryland Department of Transportation, USA. This is achieved through establishing the correlation between known condition ratings and related non-bridge factors such as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to determine the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this chapter, the prediction accuracy based on four and nine BPM-generated historical condition ratings as input data are also compared, using traditional bridge deterioration modeling techniques, i.e., deterministic and stochastic methods. The condition ratings are available. This implies that the BPM can be utilized to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models. ©2011 Nova Science Publishers, Inc. All rights reserved.

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

http://hdl.handle.net/10453/117640