Ground motion predictive modelling based on genetic algorithms

Abstract

This study aims to utilise genetic algorithms for the estimation of peak ground accelerations (PGA). A case study is carried out for the earthquake data from south-west Turkey. The input parameters used for the development of attenuation relationship are magnitude, depth of earthquake, epicentral distance, average shear wave velocity and slope height of the site. Earthquake database compiled by the Earthquake Research Institute of Turkey was used for model development. An important contribution to this study is the slope/hill data included into the dataset. Developed empirical model has a good correlation (R = 0.78 and 0.75 for the training and overall datasets) between measured and estimated PGA values. The proposed model is also compared with local empirical predictive models and its results are found to be reasonable. The slope-hill effect found to be an important parameter for the estimation of PGA. © Author(s) 2011.