Reliability Evaluation of Slope in Spatially Variable Soils Using Sliced Inverse Regression-Based Extreme Gradient Boosting

被引：0

作者：

Deng, Zhi-Ping ^{[1
]}

Huang, Kai-Rong ^{[1
]}

Zheng, Ke-Hong ^{[1
]}

Niu, Jing-Tai ^{[1
]}

机构：

[1] Nanchang Inst Technol, Coll Water Conservancy & Ecol Engn, Nanchang, Peoples R China

来源：

GEO-RISK 2023: ADVANCES IN MODELING UNCERTAINTY AND VARIABILITY | 2023年 / 347卷

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Two key challenges facing the reliability analysis of slopes considering spatial variability of geotechnical parameters are high dimensionality and computational cost. To this end, this study proposes a slice inverse regression (SIR) based Extreme gradient boosting (XGBoost) method for reliability analysis of slopes considering spatial variability. This paper uses a typical slope as an example to illustrate the proposed method. The results show that the reliability analysis based on SIR and XGBoost can predict the failure probability of a slope with reasonable accuracy and efficiency. The results are compared with those obtained by other methods. The stability and generalization of the limit gradient boosting model with sliced inverse regression in terms of computational efficiency are further illustrated.

引用

页码：11 / 22

页数：12