M5 Soft Computing Techniques for Assessment of Soil Liquefaction

Abstract

It is essential to precisely estimate the liquefaction potential because soil liquefaction is a factor that raises the quantity and intensity of losses in an earthquake. In the past, the prediction of soil liquefaction was based on multiple analytical inferences. The purpose of this research is to develop an M5 model for both classification and regression in order to investigate the suitability of the M5 decision tree for liquefaction assessment. Additionally, the divisional approaches of fuzzy clustering means (FCM), kfold clustering, and grid search cross-validation (Gridsearch CV) are investigated in order to create effective regression and classification models. In this work, specific models are developed using a data set of 200 boreholes from standard penetration tests on soils in the Dinajpur region. The efficacy of the constructed models is assessed using several performance measures, such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of correlation (R) for regression models, and accuracy, precision, and AUC value for classification models. Based on the results, it was found that the M5 decision tree regression model shows R = 0.95, IoA = 0.86, and IoS = 0.96 for testing and R = 0.93, IoA = 0.88, and IoS = 0.96 for training data. On the other hand, the classification model shows accuracy = 95%, recall = 1, and F1 score = 0.97 for testing and 98.75%, 1, and 0.99 for training, respectively. Both of these results were found for the Kfold technique, which predicts a more accurate value than other divisional approaches.