Prediction of Soil Liquefaction Using Machine Learning Approaches

In the geotechnical engineering field, the assessment of liquefaction potential is a critical aspect of site evaluation. This work focuses on the application of support vector machines (SVM) to improve the accuracy of liquefaction potential evaluation. Input data were collected from the authors' previous study and include parameters such as groundwater table (GWT), depth, fineness content, peak ground acceleration (PGA), corrected SPT-N value, total stress, and effective overburden stress. Radial basis function (RBF), linear, polynomial, and sigmoid are the four SVM kernel functions that are examined in this study to model liquefaction-related data using three approaches: grid search cross-validation, k-fold cross-validation, and fuzzy c-clustering means (FCM). Several performance metrics, including accuracy, precision, recall, and the area under the receiver operating characteristics (ROC) curve (AUC), among others, are used to evaluate the developed machine learning (ML) models. The linear and polynomial functions, for the grid search cross-validation approach, show higher performance with an accuracy of 94.64%, recall of 95.55%, F1-score of 96.63, and AUC of 0.99 on the testing data. For the k-fold partitioning approach, the RBF yields higher performance metrics compared to the other three functions, with an accuracy of 92.73%, precision of100%, F1-score of 95.0%, and AUC of 0.98. In the FCM technique, the linear and polynomial kernels again yield greater accuracy, precision, F1-score, and specificity, while, the AUC values of the sigmoid and RBF kernels are higher. The current analysis recommends the RBF over other mathematical functions based on the k-fold partitioning technique after evaluating all performance matrices. Keywords: liquefaction; support vector machine; mathematical functions; divisional approaches; performance metricsfunctions; divisional approaches; performance metrics