Risk Assessment for Bridge Maintenance Projects: Neural Networks versus Regression Techniques

Abstract

Bridge risk assessment often serves as the basis for bridge maintenance priority ranking and optimization and conducted periodically for the purpose of safety. This paper presents an application of artificial neural networks in bridge risk assessment, in which back-propagation neural networks are developed to model bridge risk score and risk categories. The study investigated and utilized 506 bridge maintenance projects to develop the models. It is shown that neural networks have a very strong capability of modeling and classifying bridge risks. The average accuracies for risk score and risk categories are both over 96%. A comparative study is conducted with an alternative methodology using multiple regression techniques. The results revealed that neural networks achieved much better performances than regression analysis models. In addition an integrated forecasting approach was utilized to combine neural networks and regression analysis to generate hybrid models, which produced better accuracies than any of the individually developed models.