Abstract
Embankments are widespread throughout the world, and their safety under seismic conditions is a primary concern in the geotechnical engineering community since the failure events may lead to disastrous consequences. This chapter proposes an efficient seismic slope stability analysis approach by introducing advanced gradient boosting algorithms, namely categorical boosting (CatBoost), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost). As an illustration, the proposed approach is applied to the seismic stability analysis of a hypothetical embankment example subjected to water level changes. For comparison, the predictive performance of CatBoost, LightGBM, and XGBoost is investigated. Moreover, the Shapley additive explanations (SHAP) method is used to explore the relative importance of the four features.
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Wengang, Z., Hanlong, L., Lin, W., **ng, Z., Yanmei, Z. (2023). Efficient Seismic Stability Analysis of Slopes Subjected to Water Level Changes Using Gradient Boosting Algorithms. In: Application of Machine Learning in Slope Stability Assessment. Springer, Singapore. https://doi.org/10.1007/978-981-99-2756-2_8
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DOI: https://doi.org/10.1007/978-981-99-2756-2_8
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