Amin Keshavarz

Lateral spreading is one of the most significant destructive and catastrophic phenomena associated with liquefaction caused by earthquake and can impose very serious damages to structures and engineering facilities. The aim of this study is to evaluate liquefaction induced lateral spreading and finding new relations using gene expression programming (GEP) that is a new and developed generation of genetic algorithms approaches. Since there are complicated, nonlinear and higher order relationships between many factors affecting the lateral spreading, GEP is assumed to be capable of finding complex and accurate relationships between these factors. This study includes three main stages: (i) compilation of available database (484 data), (ii) dividing data into training and testing categories, and (iii) building new models and propose new relationships to predict ground displacement in free face, gentle slope and general ground conditions. The results of modeling each of the different ground conditions are presented in the form of mathematical equations. At the end, the final GEP models for 3 different cases of ground conditions are compared with multiple linear regression (MLR) and other published models. The statistical parameters indicate the higher accuracy of the GEP models over other relations.