An artificial neural network approach to inhomogeneous soil slope stability predictions based on limit analysis methods

被引：49

作者：

Qian, Z. G. ^{[1
]}

Li, A. J. ^{[2
]}

Chen, W. C. ^{[3
]}

Lyamin, A., V ^{[4
]}

Jiang, J. C. ^{[5
]}

机构：

[1] Deakin Univ, Sch Engn, Geelong, Vic 3217, Australia

[2] Natl Taiwan Univ Sci & Technol, Dept Civil & Construct Engn, Taiwan Bldg Technol Ctr, Taipei 106, Taiwan

[3] Natl Taiwan Univ Sci & Technol, Dept Civil & Construct Engn, Taipei 106, Taiwan

[4] Univ Newcastle, Ctr Geotech & Mat Modelling, Callaghan, NSW 2308, Australia

[5] Univ Tokushima, Dept Civil & Environm Engn, Tokushima, Japan

来源：

SOILS AND FOUNDATIONS | 2019年 / 59卷 / 02期

关键词：

Stability number; Stability chart; Limit analysis; Filled slope; Artificial neural network; FINITE-ELEMENTS; 3D FAILURES; CHARTS; EQUILIBRIUM; MODEL;

D O I：

10.1016/j.sandf.2018.10.008

中图分类号：

P5 [地质学];

学科分类号：

0709 ; 081803 ;

摘要：

The assessment of soil slope stability is an important task in geotechnical designs. This study uses finite element upper bound (UB) and lower bound (LB) limit analysis (LA) methods to investigate inhomogeneous soil slope stability on the basis of the conventional Mohr-Coulomb parameters. The obtained stability numbers are presented in inhomogeneous soil slope stability charts. In order to minimize manual reading errors when using the chart solutions, an artificial neural network (ANN) is employed to develop a stability assessment tool for the slopes investigated in this paper. The slope stability analysis using the ANN-based tool is convenient, and the predictions it provides are highly accurate. (C) 2019 Production and hosting by Elsevier B.V. on behalf of The Japanese Geotechnical Society.

引用

页码：556 / 569

页数：14

共 48 条

[1] Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research [J].