Intelligent Optimization of Supporting Parameters for Large Underground Caverns Based on DE-LSSVM

WU Kai; YANG Xue-lian; LI Jia

doi:10.11988/ckyyb.20180147

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Journal of Changjiang River Scientific Research Institute ›› 2019, Vol. 36 ›› Issue (9) : 115-120. DOI: 10.11988/ckyyb.20180147

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Intelligent Optimization of Supporting Parameters for Large Underground Caverns Based on DE-LSSVM

WU Kai¹,YANG Xue-lian¹,LI Jia²

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Abstract

To tackle the time-consuming heavy workload in optimizing the supporting parameters for large caverns, an intelligent optimization method is proposed by combining differential evolution algorithm (DE) and the least squares support vector machine (LSSVM). The learning samples are produced by orthogonal design and FLAC^3D numerical simulation, and the optimal parameters of LSSVM are determined in global ranges by DE algorithm. Thus, the LSSVM with optimal parameters are used to describe the nonlinear relationship between supporting parameters and evaluation index. The DE algorithm is used again to search for the optimal supporting parameters in global ranges. The present method is applied to optimize the supporting parameters of underground caverns, and results demonstrate that the optimization method is of good application value in optimizing the supporting parameters of large underground caverns.

Key words

underground caverns / least squares support vector machine / differential evolution algorithm / intelligent optimization / supporting parameters

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WU Kai,YANG Xue-lian,LI Jia. Intelligent Optimization of Supporting Parameters for Large Underground Caverns Based on DE-LSSVM[J]. Journal of Changjiang River Scientific Research Institute. 2019, 36(9): 115-120 https://doi.org/10.11988/ckyyb.20180147

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