Soil liquefaction assessment using machine learning

Liquefaction is one of the prominent factors leading to damage to soil and structures. In this study, the relationship between liquefaction potential and soil parameters is determined by applying feature importance methods to Random Forest (RF), Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) algorithms. Feature importance methods consist of permutation and Shapley Additive exPlanations (SHAP) importances along with the used model's built-in feature importance method if it exists. These suggested approaches incorporate an extensive dataset of geotechnical parameters, historical liquefaction events, and soil properties. The feature set comprises 18 parameters that are gathered from 161 field cases. Algorithms are used to determine the optimum performance feature set. Compared to other approaches, the study assesses how well these algorithms predict soil liquefaction potential. Early findings show that the algorithms perform well, demonstrating their capacity to identify non-linear connections and improve prediction accuracy. Among the feature set, σ^,_v (psf), MSF, CSR_{σ, v}, FC%, V_{s∗,40f t}(f ps) and N_1,60,CS are the ones that have the highest deterministic power on the result. The study's contribution is that, in the absence of extensive data for liquefaction assessment, the proposed method estimates the liquefaction potential using five parameters with promising accuracy.