Safety Prediction Model for Reinforced Highway Slope using a Machine Learning Method

Abstract

Recycled plastic pin (RPP) has been proved to be an effective and inexpensive solution for shallow slope stabilization. Current practice suggests conducting numerical modeling to find out the desired factor of safety (FS) using RPP in the design of landslide repair. While the slope stability is heavily dependent on soil strength parameters and slope geometry, RPP length and spacing can also play a significant role in reaching the target factor of safety for the highway slope. During this study, a safety prediction model was developed using both statistical and machine learning (ML) approaches to use RPP in slope stabilization. Initially, parametric study was conducted using five different soil strengths, six slope heights, three slope ratios, three RPP lengths, and five RPP spacing configurations. Using the strength reduction techniques of Finite Element Modeling Software PLAXIS 2D, FS was determined for more than 1,000 combinations. Afterwards, a statistical approach was undertaken to determine a safety prediction model containing all possible parameters. Finally, an ML approach was conducted for safety model. The ML approach was found to be more accurate than the classical statistical approach with 85% accuracy of predicting the FS for an RPP reinforced highway slope. The developed model was validated against the values obtained from the numerical modeling, which indicated that the SF obtained from the developed model was in good agreement with those from finite element method (FEM) analysis.