Convolutional neural networks prediction of the factor of safety of random layered slopes by the strength reduction method

被引：17

作者：

Soranzo, Enrico ^{[1
]}

Guardiani, Carlotta ^{[1
]}

Chen, Yiru ^{[1
]}

Wang, Yunteng ^{[1
]}

Wu, Wei ^{[1
]}

机构：

[1] Univ Nat Resources & Life Sci, Inst Geotech Engn, Feistmantelstr 4, A-1180 Vienna, Austria

来源：

ACTA GEOTECHNICA | 2023年 / 18卷 / 06期

关键词：

Convolutional neural networks; Machine learning; Slope stability; Strength reduction method; STABILITY PREDICTION; SURFACE; EMBANKMENTS;

D O I：

10.1007/s11440-022-01783-3

中图分类号：

P5 [地质学];

学科分类号：

0709 ; 081803 ;

摘要：

The strength reduction method is often used to predict the stability of soil slopes with complex soil properties and failure mechanisms. However, it requires a considerable computational effort. In this paper, we make use of a convolutional neural network to reduce the computational cost. The factor of safety of 600 slopes with different inclination and soil properties is first calculated with the strength reduction method. A convolutional neural network is then trained and validated. We demonstrate the performance of our approach and show how to augment the dataset to further enhance its capability and prevent overfitting.

引用

页码：3391 / 3402

页数：12

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