Monitoring bridge health using fuzzy case-based reasoning

Case-based reasoning (CBR), one of the artificial intelligence (AI) learning approaches, is drawing the attention of many researchers in Civil Engineering. However, due to vagueness and uncertainties in knowledge representation, attribute description, and similarity measures in CBR—especially when dealing with similarity assessment—it is difficult to find the cases from a case base which exactly match the query case. Therefore, fuzzy theories have been incorporated into CBR allowing for more robust, flexible, and accurate models. In this study, two fuzzy membership functions (trapezoidal and step-wise) and fuzzy numbers are used to measure the similarity between attribute values. They are integrated into CBR to develop a model used to monitor highway bridge health. This modelʹs learning capabilities have been validated using five different error-metrics, based on the cross-validation method. The code is implemented using the programming language C++, and all the cases used for both training and testing are extracted from the electronic bridge database of the Kansas Department of Transportation. It is shown from the experimental results that it is feasible to apply fuzzy case-based reasoning to monitor bridge health.