| 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, similarity measure, solution transformation, case retrieval, and case inference in CBR---especially when dealing with similarity assessment---it is difficult to find the cases from a case base which are exactly the same as the query one. Therefore, fuzzy theories have been incorporated into CBR, which promises more robust, flexible, and accurate models. In this research, fuzzy case-based reasoning (FCBR) has been used to develop a model for bridge management. This model can deal with more than one objective, namely, predicting the future health condition of a bridge deck, and recommending the appropriate maintenance, rehabilitation and replacement (MR&R) actions. The FCBR model's learning capabilities have been validated using 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 bridge engineering and management. |
