Service Life Prediction of Masonry Arch Bridges using Artificial Neural Networks
This paper presents a methodology for predicting reliability based remaining service lives and estimation of serviceability conditions for masonry arch bridges using Artificial Neural Networks (ANNs). In this ANNs analysis, training was processed by Back-Propagation (BP) Algorithm with corresponding parameters. The critical failure mode of the masonry arch bridge is based on axle loads. The parameters for Back-Propagation are mean values ( M μ ) and standard deviations ( M σ ) of proposed safety margin of the masonry arch bridge. Those parameters were used to predict the serviceability condition of the masonry arch bridges. Finally, the remaining service life of the masonry arch bridge was determined using a target failure probability, while assuming that the current rate of loading magnitude and frequency are constant for future prediction. Proposed methodology is illustrated with a case study bridge selected in the national road network of Sri Lanka.
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Corporate Authors:
National Concrete Bridge Council
Portland Cement Association, 5420 Old Orchard Road
Skokie, IL United States 60077-1083 -
Authors:
- Narasinghe, S B
- Karunananda, PAK
- Dissanayake, P B R
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Conference:
- HPC: Build Fast, Build to Last. The 2006 Concrete Bridge Conference
- Location: Reno NV, United States
- Date: 2006-5-7 to 2006-5-10
- Publication Date: 2006
Language
- English
Media Info
- Media Type: CD-ROM
- Features: Figures; References; Tables;
- Pagination: 12p
- Monograph Title: HPC: Build Fast, Build to Last
Subject/Index Terms
- TRT Terms: Algorithms; Arch bridges; Backpropagation; Failure; Failure analysis; Masonry bridges; Neural networks; Reliability; Service life; Serviceability
- Uncontrolled Terms: Reliability index
- Subject Areas: Bridges and other structures; Design; Highways; I24: Design of Bridges and Retaining Walls;
Filing Info
- Accession Number: 01036227
- Record Type: Publication
- Files: TRIS
- Created Date: Oct 27 2006 8:14AM