Machine learning approaches for prediction of fine-grained soils liquefaction

Abstract

Since soil liquefaction is a dimension that increases the amount and severity of losses in an earthquake, it is vital to estimate the liquefaction potential accurately. Traditionally, several analytical inferences were made for the prediction of soil liquefaction. However, it is necessary to use machine learning methods to establish nonlinear relationships of soil physical characteristics and develop an accurate classification model. In this study, the applicability of seven different machine learning algorithms; decision trees, logistic regression, support vector machines, k-nearest neighbors, stochastic gradient descent, random forest, and artificial neural network, were investigated on a data set obtained from field experiments (Standard Penetration Test) on soils in Adapazari region after the 1999 earthquake. Performance metrics such as accuracy, recall, precision, F1 score, and receiver operating characteristic evaluated algorithms. As a result of experimental studies, the decision tree algorithm performed best on the dataset, with an overall accuracy of 90%. The decision tree model provides an easy and effective tool for evaluating ground liquefaction potential to decision-makers. As a result of the decision tree study, it was observed that the mean grain size (D50) soil feature has the most significant effect on the liquefaction potential.