Service Life Prediction of Masonry Arch Bridges Using Artificial Neural Networks
By: P. B. R. Dissanayake and S. B. Narasinghe
Open Access
|Jan 2008Abstract
This paper presents a methodology to predict reliability based remaining service lives and estimation of serviceability conditions of 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 value (|UM) and standard deviation (o~M) 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 from the national road network of Sri Lanka.
DOI: https://doi.org/10.4038/engineer.v41i1.7079 | Journal eISSN: 2550-3219
Language: English
Page range: 13 - 16
Published on: Jan 30, 2008
Published by: The Institution of Engineers, Sri Lanka
In partnership with: Paradigm Publishing Services
Keywords:
© 2008 P. B. R. Dissanayake, S. B. Narasinghe, published by The Institution of Engineers, Sri Lanka
This work is licensed under the Creative Commons License.