Abstract
The widely used bridge management systems require appropriate preliminary deterioration diagnosis and modeling. This paper presents a fuzzy rule-based inference system for bridge damage diagnosis and prediction which aims to provide bridge designers with valuable information about the impacts of design factors on bridge deterioration. The validity of these influence parameters is verified by an input variable identification method. Fuzzy logic is utilized to handle uncertainties and imprecision involved. A modified mountain clustering method is employed to create the training data set. A fuzzy partitioning algorithm is implemented to construct the membership functions of the input variables and to induce the fuzzy rules from the numerical data. The generated rule base is checked and optimized based on the similarity measures among the input fuzzy sets. Illustrative examples show that the system has a high classification accuracy rate with a small number of rules.
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