Abstract
Abstract This research provides a comparative study of intelligent systems in structural damage assessment after the occurrence of an earthquake. Seismic response data of a reinforced concrete structure subjected to 100 different levels of seismic excitation are utilized to study the structural damage pattern described by a well-known damage index, the maximum inter-story drift ratio (MISDR). Through a time-frequency analysis of the accelerograms, a set of seismic features is extracted. The aim of this study is to analyze the performance of three different techniques for the set of the proposed seismic features: an artificial neural network (ANN), a Mamdani-type fuzzy inference system (FIS), and a Sugeno-type FIS. The performance of the models is evaluated in terms of the mean square error (MSE) between the actual calculated and estimated MISDR values derived from the proposed models. All models provide small MSE values. Yet, the ANN model reveals a slightly better performance.
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