基于贝叶斯网络的隧道勘察设计期大变形灾害概率分级预测与应用研究

张志强; 范俊奇; 曾鹏; 石晓燕; 李天斌; 聂宇; 张振宏

doi:10.3799/dqkx.2022.274

Volume 48 Issue 5

May 2023

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Article Navigation > Earth Science > 2023 > 48(5): 1923-1934

Zhang Zhiqiang, Fan Junqi, Zeng Peng, Shi Xiaoyan, Li Tianbin, Nie Yu, Zhang Zhenhong, 2023. Probabilistic Classification Prediction of Tunnel Squeezing Based on Bayesian Network and Its Application during Investigation and Design Stage. Earth Science, 48(5): 1923-1934. doi: 10.3799/dqkx.2022.274

Citation:

Zhang Zhiqiang, Fan Junqi, Zeng Peng, Shi Xiaoyan, Li Tianbin, Nie Yu, Zhang Zhenhong, 2023. Probabilistic Classification Prediction of Tunnel Squeezing Based on Bayesian Network and Its Application during Investigation and Design Stage. Earth Science, 48(5): 1923-1934. doi: 10.3799/dqkx.2022.274

Citation:

Zhang Zhiqiang, Fan Junqi, Zeng Peng, Shi Xiaoyan, Li Tianbin, Nie Yu, Zhang Zhenhong, 2023. Probabilistic Classification Prediction of Tunnel Squeezing Based on Bayesian Network and Its Application during Investigation and Design Stage. Earth Science, 48(5): 1923-1934. doi: 10.3799/dqkx.2022.274

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Probabilistic Classification Prediction of Tunnel Squeezing Based on Bayesian Network and Its Application during Investigation and Design Stage

doi: 10.3799/dqkx.2022.274

1.
College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China
2.
Institute of National Defense Engineering, Academy of Military Sciences, Luoyang 471023, China
3.
Sichuan Intelligent Expressway Technology Co. Ltd., Chengdu 610041, China

Received Date: 2022-07-06
Available Online: 2023-06-06
Publish Date: 2023-05-25

Abstract

Abstract

Railway and highway tunnels located in southwest mountainous areas of China often suffer from tunnel squeezing due to high geo-stress, soft rock and fault fracture zone, which bring huge economic losses. To fulfill the requirements of tunnel alignment and design, considering the accessibility of prediction indexes in this stage, the buried depth, surrounding rock grade, equivalent tunnel diameter and rock strength were taken as prediction indexes. Data containing 151 groups of tunnel squeezing cases were collected and a database was established based on tunnels located in Southwest China. The Bayesian network was used to establish the probability classification prediction model of tunnel squeezing with incomplete data. The accuracy of the model was found to be 76.52% by ten-fold cross validation. Based on this model, the tunnel squeezing classification prediction software platform was established. It was applied in Baima tunnel of Jiu-Mian Expressway. The prediction accuracy was 71.11%. The research results of this paper can provide a technical support for the prediction of tunnel squeezing during investigation and design stage under similar geological conditions in Southwest China.
- high geo-stress tunnel,
- investigation and design stage,
- tunnel squeezing,
- Bayesian network,
- probabilistic classification prediction,
- engineering geology

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References(52)

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