A Genetic Algorithm for the Classification of Earthquake Damages in Buildings

Abstract

In this paper an efficient classification system in the area of earthquake engineering is reported. The proposed method uses a set of artificial accelerograms to examine several types of damages in specific structures. With the use of seismic accelerograms, a set of twenty seismic parameters have been extracted to describe earthquakes. Previous studies based on artificial neural networks and neuro-fuzzy classification systems present satisfactory classification results in different types of earthquake damages. In this approach a genetic algorithm (GA) was used to find the optimal feature subset of the seismic parameters that minimizes the computational cost and maximizes the classification performance. Experimental results indicate that the use of the GA was able to classify the structural damages with classification rates up to 92%.

Neuro-fuzzy techniques for the classification of earthquake damages in buildings

“…Fuzzy logic concepts have been incorporated (fuzzyfication of the seismic parameters) and correct classification results up to 84% and 82% for the structural and architectural damages were recorded, respectively. Some preliminary results based on an artificial neural network classifier have been reported in [4].…”

Fuzzy Inference Systems for Automatic Classification of Earthquake Damages

“…Thus, the set of damage indices can be considered as a fuzzy set and fuzzy methods are appropriate to be applied to such classification procedures. Previous studies [4][5][6] attempt to classify the structural damages in buildings. The first approach is based on the shape similarity of accelerograms.…”