Computational Intelligence Application in Modeling Seismic Record and Soil Test Data at a Specified Area

Abstract

Computational intelligence can be applied to solve various engineering problems, where earthquake related issue is one of important research topics, as this natural hazard occurs quite often worldwide every year. In this study, a genetic algorithm based neural network model is developed to improve the reliability of predicting peak ground acceleration, the key element to evaluate earthquake response and to setup seismic design standard. Three seismic parameters including local magnitude, epicenter distance, and epicenter depth, are taken in the input layer for developing the estimation model. Then, two geological conditions including standard penetration test value and shear wave velocity, are added for developing a new model to reflect the site response more adequately. Based on the earthquake records and soil test data from 86 checking stations within 24 seismic subdivision zones in Taiwan area, the results show that the combination of using neural network and genetic algorithm can achieve a better performance than that of using neural network model solely. This preferred model can be extended to predict peak ground acceleration at unchecked sites, and can be applied to check the design standard in building code. This study may provide a new approach to solve this type of nonlinear seismic problem.