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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.