Evaluation of Liquefaction Potential of Soil Using Genetic Programming

Liquefaction of soil is one of the major causes for the significant damages to the buildings, lifeline systems and harbor facilities caused by the earthquakes. At present artificial intelligence techniques such as artificial neural network (ANN) and support vector machine (SVM) based models are found to be more efficient compared to statistical methods. The present study discusses about the evaluation of liquefaction potential of soil based on cone penetration test (CPT) data obtained after 1999 Chi-Chi, Taiwan, earthquake using evolutionary artificial intelligence technique known as genetic programming (GP). A comparative study is made among the existing three CPT based statistical methods and the developed GP model for prediction of liquefied and non-liquefied cases in terms of percentage success rate with respect to the field observations.