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Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm

Li, AJ, Khoo, S, Lyamin, AV and Wang, Y (2016) Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm Automation in Construction, 65. pp. 42-50.

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The analysis of rock slope stability is a classical problem for geotechnical engineers. However, for practicing engineers, proper software is not usually user friendly, and additional resources capable of providing information useful for decision-making are required. This study developed a convenient tool that can provide a prompt assessment of rock slope stability. A nonlinear input–output mapping of the rock slope system was constructed using a neural network trained by an extreme learning algorithm. The training data was obtained by using finite element upper and lower bound limit analysis methods. The newly developed techniques in this study can either estimate the factor of safety for a rock slope or obtain the implicit parameters through back analyses. Back analysis parameter identification was performed using a terminal steepest descent algorithm based on the finite-time stability theory. This algorithm not only guarantees finite-time error convergence but also achieves exact zero convergence, unlike the conventional steepest descent algorithm in which the training error never reaches zero.

Item Type: Article
Subjects : Civil Engineering
Divisions : Faculty of Engineering and Physical Sciences > Civil and Environmental Engineering
Authors :
Li, AJ
Khoo, S
Lyamin, AV
Wang, Y
Date : May 2016
DOI : 10.1016/j.autcon.2016.02.004
Copyright Disclaimer : Crown Copyright © 2016 Published by Elsevier B.V. All rights reserved.
Depositing User : Symplectic Elements
Date Deposited : 04 Nov 2016 15:51
Last Modified : 31 Oct 2017 18:44

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