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Back analysis of geomechanical parameters in underground engineering using artificial bee colony.

Zhu C, Zhao H, Zhao M - ScientificWorldJournal (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Civil Engineering, Henan Polytechnic University, Jiaozuo 454003, China.

ABSTRACT
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.

Show MeSH
Fitness with different parameters of kernel function.
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Related In: Results  -  Collection


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fig14: Fitness with different parameters of kernel function.

Mentions: In this study, the RBF kernel function was adopted. The relationship between fitness and cycle is listed in Figure 14 with σ = 10 and σ = 1. The performance of LSSVM is listed in Figure 15 using σ = 10 and σ = 1. Its show selecting the appropriate kernel parameters is important to back analysis. But there is not any guide to select kernel function and its parameters according to LSSVM theory. It can be acquired by error-and-trial.


Back analysis of geomechanical parameters in underground engineering using artificial bee colony.

Zhu C, Zhao H, Zhao M - ScientificWorldJournal (2014)

Fitness with different parameters of kernel function.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4127281&req=5

fig14: Fitness with different parameters of kernel function.
Mentions: In this study, the RBF kernel function was adopted. The relationship between fitness and cycle is listed in Figure 14 with σ = 10 and σ = 1. The performance of LSSVM is listed in Figure 15 using σ = 10 and σ = 1. Its show selecting the appropriate kernel parameters is important to back analysis. But there is not any guide to select kernel function and its parameters according to LSSVM theory. It can be acquired by error-and-trial.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Civil Engineering, Henan Polytechnic University, Jiaozuo 454003, China.

ABSTRACT
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.

Show MeSH