Artificial Neural Network Model of Bridge Deterioration
Accurate prediction of bridge condition is essential for the planning of maintenance, repair, and rehabilitation. An examination of the assumptions (for example, maintenance independency) of the existing Markovian model reveals possible limitations in its ability to adequately model the procession of deterioration for these purposes. This study uses statistical analysis to identify significant factors influencing the deterioration and develops an application model for estimating the future condition of bridges. Based on data derived from historical maintenance and inspection of concrete decks in Wisconsin, this study identifies 11 significant factors and develops an artificial neural network (ANN) model to predict associated deterioration. An analysis of the application of ANN finds that it performs well when modeling deck deterioration in terms of pattern classification. The developed model has the capacity to accurately predict the condition of bridge decks and therefore provide pertinent information for maintenance planning and decisionmaking at both the project and the network levels.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/08873828
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Supplemental Notes:
- Abstract reprinted with permission from ASCE
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Authors:
- Huang, Ying-Hua
- Publication Date: 2010-11
Language
- English
Media Info
- Media Type: Print
- Features: References; Tables;
- Pagination: pp 597-602
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Serial:
- Journal of Performance of Constructed Facilities
- Volume: 24
- Issue Number: 6
- Publisher: American Society of Civil Engineers
- ISSN: 0887-3828
- Serial URL: http://ascelibrary.org/toc/jpcfev/27/1
Subject/Index Terms
- TRT Terms: Bridge decks; Bridges; Neural networks; Statistical analysis; Structural deterioration and defects
- Geographic Terms: Wisconsin
- Subject Areas: Bridges and other structures; Highways;
Filing Info
- Accession Number: 01322983
- Record Type: Publication
- Files: TRIS
- Created Date: Dec 22 2010 8:31AM