Calculation for reliability of deep tunnel lining through optimization method of Kriging interpolation in collaboration with genetic algorithm
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Calculation for reliability of deep tunnel lining through optimization method of Kriging interpolation in collaboration with genetic algorithm
Vol. 34, Issue 9, (2013)
作者机构:
湖南大学 土木工程学院,长沙,410082
作者简介:
基金信息:
DOI:
CLC:TU457
Online First:13 September 2013,
Published:2013
稿件说明:
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SU Ya, YANG Ming-hui, SU Yong-hua, et al. Calculation for reliability of deep tunnel lining through optimization method of Kriging interpolation in collaboration with genetic algorithm[J]. 2013, 34(9).
DOI:
SU Ya, YANG Ming-hui, SU Yong-hua, et al. Calculation for reliability of deep tunnel lining through optimization method of Kriging interpolation in collaboration with genetic algorithm[J]. 2013, 34(9).DOI:
Calculation for reliability of deep tunnel lining through optimization method of Kriging interpolation in collaboration with genetic algorithm
Countermeasure to solve unknown parameters for Gauss correlation function in Kriging interpolation accurately is presented by means of global optimization ability of genetic algorithm. A universal equivalent explicit formulation for state equation is established by Kriging interpolation to solve a problem that mechanical state equation for lining structure can’t be expressed through basic parameters explicitly because of a highly nonlinear relation between state quantity and basic parameters. A state equation for stretch resistance is taken as an example
construction procedure of mechanical state equation for one tunnel’s lining structure through the universal equivalent explicit formulation in connection with Latin sample way is presented. An analysis approach for stability reliability degree of tunnel’s lining structure based on optimization method for Kriging interpolation in collaboration with genetic algorithm (GA) is founded through calculation mode of reliability degree index established based on constraint condition for the state equation. Some practical examples are given to illustrate the application
which manifests that the relative error between the present approach and the accurate one is found to be 4.8%; and the computational burden of this approach is only 2.5% of that when employing the precise one. The present approach is excellent over existing analogous approaches on computational burden and accuracy.