Analysis of Bridge Condition Rating Data Using Neural Networks

Abstract

Currently bridges are evaluated using either a visual inspection process or a detailed structural analysis. When bridge evaluation is conducted by a visual inspection, a subjective rating is assigned to a bridge component. With analytical evaluation, the rating is computed based on the load applied and the resistance of the bridge component. There have been several attempts to correlate the subjective rating to the analytical rating. The conventional statistical analyses, as well as methods based on fuzzy logic, have not been very successful in providing a clear relationship between the two rating systems. This paper describes the application of neural network systems in developing the relation between subjective ratings and bridge parameters as well as that between subjective and analytical ratings. It is shown that neural networks can be trained and used successfully in estimating a rating based on bridge parameters. The specific application problem for railroad bridges in the commuter rail system in the Chicago metropolitan area is presented. The study showed that a successful training of a network can be achieved, especially if the input data set contains parameters with a diverse combination of intercorrelation coefficients. When the relationship between the bridge subjective rating and bridge parameters was investigated, the network had a prediction rating of about 73%. The study also investigated the relation between the subjective and analytical rating. In this case, the prediction rate was about 43%. Compared with conventional statistical methods and the fuzzy‐logic approach, the neural network system had a much better performance ratio in establishing the relation between the bridge rating and bridge parameters.