Deterioration prediction of concrete bridges with artificial neural network (ANN) derived from discrete condition data

At present deterioration caused by service conditions and deferred maintenance of old bridges are diagnosed using a condition monitoring systemwhere a condition rating is given to each and every element based on visual inspection. Evaluating these conditions to arrive at a meaningful decision criterion is a challenge faced by many road authorities in the world. Whilst there have been many different methods proposed in research, they haven't been widely accepted by the authorities. Some of these methods include Markov process, Gamma process and deterministic methods where a condition curve is derived from a large amount of discrete condition data. In this paper, an attempt has been made to use the artificial neural networks to forecast deterioration using condition data from level 2 inspections. Backward-Propagation-Method (BPM) of artificial neural network (ANN) has been applied to forecast bridge deterioration. Condition data has been obtained from one local council in Victoria, Australia to derive the models