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Research paper | Open Access

Heterogeneity identification method for surrounding rock of large-section rock tunnel faces based on support vector machine

Wenhao Yi, Mingnian Wang, Jianjun Tong( ), Siguang Zhao, Jiawang Li, Dengbin Gui, Xiao Zhang
Southwest Jiaotong University, Chengdu, China
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Abstract

Purpose

The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.

Design/methodology/approach

Relying on the support vector machine (SVM)-based classification model, the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face, and the identification calculation was carried out for the five test tunnels. Then, the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.

Findings

The results show that compared with the two classification models based on neural networks, the SVM-based classification model has a higher classification accuracy when the sample size is small, and the average accuracy can reach 87.9%. After the samples are replaced, the SVM-based classification model can still reach the same accuracy, whose generalization ability is stronger.

Originality/value

By applying the identification method described in this paper, the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified, and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting, and can provide a basis for local optimization of support parameters.

Keywords

Support vector machineHigh-speed railwayHeterogeneityRock tunnelSurrounding rock

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Railway Sciences
Volume 2 Issue 1,
April 2023
Pages 48-67
DOI: 10.1108/RS-01-2023-0006
Cite this article:
Yi W, Wang M, Tong J, et al. Heterogeneity identification method for surrounding rock of large-section rock tunnel faces based on support vector machine. Railway Sciences, 2023, 2(1): 48-67. https://doi.org/10.1108/RS-01-2023-0006

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Received: 30 January 2023
Revised: 06 February 2023
Accepted: 06 February 2023
Published: 11 April 2023
© Wenhao Yi, Mingnian Wang, Jianjun Tong, Siguang Zhao, Jiawang Li, Dengbin Gui and Xiao Zhang. Published in Railway Sciences.

This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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