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Back Analysis of Geomechanical Parameters in Underground Engineering Using Artificial Bee Colony

Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC) algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal al...

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Detalles Bibliográficos
Autores principales: Zhu, Changxing, Zhao, Hongbo, Zhao, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4127281/
https://www.ncbi.nlm.nih.gov/pubmed/25136680
http://dx.doi.org/10.1155/2014/693812
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author Zhu, Changxing
Zhao, Hongbo
Zhao, Ming
author_facet Zhu, Changxing
Zhao, Hongbo
Zhao, Ming
author_sort Zhu, Changxing
collection PubMed
description Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC) algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal algorithm to search the unknown geomechanical parameters for the problem with analytical solution. To the problem without analytical solution, optimal back analysis is time-consuming, and least square support vector machine (LSSVM) was used to build the relationship between unknown geomechanical parameters and displacement and improve the efficiency of back analysis. The proposed method was applied to a tunnel with analytical solution and a tunnel without analytical solution. The results show the proposed method is feasible.
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spelling pubmed-41272812014-08-18 Back Analysis of Geomechanical Parameters in Underground Engineering Using Artificial Bee Colony Zhu, Changxing Zhao, Hongbo Zhao, Ming ScientificWorldJournal Research Article Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC) algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal algorithm to search the unknown geomechanical parameters for the problem with analytical solution. To the problem without analytical solution, optimal back analysis is time-consuming, and least square support vector machine (LSSVM) was used to build the relationship between unknown geomechanical parameters and displacement and improve the efficiency of back analysis. The proposed method was applied to a tunnel with analytical solution and a tunnel without analytical solution. The results show the proposed method is feasible. Hindawi Publishing Corporation 2014 2014-07-17 /pmc/articles/PMC4127281/ /pubmed/25136680 http://dx.doi.org/10.1155/2014/693812 Text en Copyright © 2014 Changxing Zhu et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhu, Changxing
Zhao, Hongbo
Zhao, Ming
Back Analysis of Geomechanical Parameters in Underground Engineering Using Artificial Bee Colony
title Back Analysis of Geomechanical Parameters in Underground Engineering Using Artificial Bee Colony
title_full Back Analysis of Geomechanical Parameters in Underground Engineering Using Artificial Bee Colony
title_fullStr Back Analysis of Geomechanical Parameters in Underground Engineering Using Artificial Bee Colony
title_full_unstemmed Back Analysis of Geomechanical Parameters in Underground Engineering Using Artificial Bee Colony
title_short Back Analysis of Geomechanical Parameters in Underground Engineering Using Artificial Bee Colony
title_sort back analysis of geomechanical parameters in underground engineering using artificial bee colony
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4127281/
https://www.ncbi.nlm.nih.gov/pubmed/25136680
http://dx.doi.org/10.1155/2014/693812
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