Service Life Prediction of Masonry Arch Bridges Using Artificial Neural Networks

Abstract

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 (|U, M) 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.