A Hybrid FEM–ANN Approach for Slope Instability Prediction

被引：56
作者：
Verma A.K. [1 ]
Singh T.N. [2 ]
Chauhan N.K. [1 ]
Sarkar K. [3 ]
机构：
[1] Department of Mining Engineering, Indian School of Mines, Dhanbad
[2] Department of Earth Science, Indian Institute of Technology Bombay, Mumbai
[3] Department of Applied Geology, Indian School of Mines, Dhanbad
来源：
Journal of The Institution of Engineers (India): Series A | 2016年 / 97卷 / 3期
关键词：
ANN; FEM; FOS; LEM; Neurons;
D O I：
10.1007/s40030-016-0168-9
中图分类号：
学科分类号：
摘要：
Assessment of slope stability is one of the most critical aspects for the life of a slope. In any slope vulnerability appraisal, Factor Of Safety (FOS) is the widely accepted index to understand, how close or far a slope from the failure. In this work, an attempt has been made to simulate a road cut slope in a landslide prone area in Rudrapryag, Uttarakhand, India which lies near Himalayan geodynamic mountain belt. A combination of Finite Element Method (FEM) and Artificial Neural Network (ANN) has been adopted to predict FOS of the slope. In ANN, a three layer, feed- forward back-propagation neural network with one input layer and one hidden layer with three neurons and one output layer has been considered and trained using datasets generated from numerical analysis of the slope and validated with new set of field slope data. Mean absolute percentage error estimated as 1.04 with coefficient of correlation between the FOS of FEM and ANN as 0.973, which indicates that the system is very vigorous and fast to predict FOS for any slope. © 2016, The Institution of Engineers (India).
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页码：171 / 180
页数：9
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