Application of Machine Learning on Slope Stability Prediction: From Data to Reliable Decisions Using Interpretable Models

The stability of embankments in geotechnical engineering is of paramount importance, as their failure can lead to catastrophic incidents, resulting in both human and financial losses, and causing adverse impacts on the economy and environment of the affected region. In this study, machine learning (ML) was implemented to address a binary classification problem (stable vs. unstable) aimed at predicting embankment stability. A comprehensive study was conducted using data from ۶۲۷ embankments with diverse geometric and geotechnical characteristics. Nine ML models were developed and tested, among which the Gradient Boosting method demonstrated the best performance. In addition to evaluating the performance of the optimal model, an interpretability approach was employed to ensure transparency and explainability in the model’s decision-making process, elucidating the influence of input parameters on the output. The findings indicate that integrating geometric information and geotechnical properties of embankments plays a crucial role in accurately predicting their stability, thereby assisting engineers in designing safe and optimized embankments. These insights can enhance safety and durability in dam construction projects and mitigate the risk of unforeseen failures.

کلیدواژه ها:

Slope Stability ، Geotechnics ، Clustering ، Machine Learning