Evaluation of lateral spreading utilizing artificial neural network and genetic programming

Due to its critical impact and significant destructive nature during and after seismic events, soil liquefaction and liquefaction-induced lateral ground spreading have been increasingly important topics in the geotechnical earthquake engineering field during the past four decades. the aim of this research is to develop an empirical model for the assessment of liquefaction-induced lateral ground spreading. this study includes three main stages: compilation of liquefaction-induced lateral ground spreading data from available earthquake case histories (the total number of 525 data points), detecting importance level of seismological, topographical and geotechnical parameters for the resulted deformations, and proposing an empirical relation to predict horizontal ground displacement in both ground slope and free face conditions. the statistical parameters and parametric study presented for this model indicate the superiority of the current relation over the already introduced relations and its applicability for engineers.