Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques

Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety reliability. Before the widespread usage of computers, was conducted through semi analytical methods, or charts. Presently, have developed many computational tools perform more efficiently. The challenge associated furthering methods is create a reliable solution estimations involving number geometric mechanical variables. objective this investigate application tree-based models, including decision tree (DT), random forest (RF), AdaBoost, in classification under seismic loading conditions. input variables used modelling were height, inclination, cohesion, friction angle, peak ground acceleration classify safe unsafe slopes. training data intelligence models resulted from series analyses performed using standard geotechnical engineering software commonly practice. Upon construction model assessment use calculation accuracy, F1-score, recall, precision indices. All could efficiently status, AdaBoost providing highest performance both development parts. proposed can be as screening tool during stage feasibility studies related infrastructure projects, according their expected status