Efficient Seismic Stability Analysis of Embankment Slopes Subjected to Water Level Changes Using Gradient Boosting Algorithms

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 study proposes an efficient seismic slope stability analysis approach by introdu...

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Veröffentlicht in:Frontiers in earth science (Lausanne) Jg. 9
Hauptverfasser: Wang, Luqi, Wu, Jiahao, Zhang, Wengang, Wang, Lin, Cui, Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Frontiers Media S.A 02.12.2021
Schlagworte:
CatBoost
embankment
LightGBM
machine learning
seismic slope stability
XGBoost
ISSN:2296-6463, 2296-6463
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Zusammenfassung: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 study 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). A database consisting of 600 datasets is prepared for model calibration and evaluation, where the factor of safety (FS) is regarded as the output and four influential factors are selected as the inputs. For each dataset, the FS corresponding to the four inputs is evaluated using the commercial geotechnical software of Slide2. 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 in this study to explore the relative importance of the four features. Results show that all the three gradient boosting algorithms (i.e., CatBoost, LightGBM, and XGBoost) perform well in the prediction of FS for both the training dataset and testing dataset. Among the four influencing factors, the friction angle φ is the most important feature variable, followed by horizontal seismic coefficient K h , cohesion c , and saturated permeability k s .
ISSN:2296-6463
2296-6463
DOI:10.3389/feart.2021.807317