基于人工智能变形预测隧道坍塌失效概率评估方法

吴波; 丘伟兴; 徐世祥; 蔡俊华; 李贻材; 张耀

doi:10.3799/dqkx.2022.147

Volume 49 Issue 11

Nov. 2024

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Article Navigation > Earth Science > 2024 > 49(11): 4204-4215

Wu Bo, Qiu Weixing, Xu Shixiang, Cai Junhua, Li Yicai, Zhang Yao, 2024. A Method for Assessing Probability of Tunnel Collapse Based on Artificial Intelligence Deformation Prediction. Earth Science, 49(11): 4204-4215. doi: 10.3799/dqkx.2022.147

Citation:

Wu Bo, Qiu Weixing, Xu Shixiang, Cai Junhua, Li Yicai, Zhang Yao, 2024. A Method for Assessing Probability of Tunnel Collapse Based on Artificial Intelligence Deformation Prediction. Earth Science, 49(11): 4204-4215. doi: 10.3799/dqkx.2022.147

Citation:

Wu Bo, Qiu Weixing, Xu Shixiang, Cai Junhua, Li Yicai, Zhang Yao, 2024. A Method for Assessing Probability of Tunnel Collapse Based on Artificial Intelligence Deformation Prediction. Earth Science, 49(11): 4204-4215. doi: 10.3799/dqkx.2022.147

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A Method for Assessing Probability of Tunnel Collapse Based on Artificial Intelligence Deformation Prediction

doi: 10.3799/dqkx.2022.147

Wu Bo^{1, 2, 3
,},
Qiu Weixing^{1, 6},
Xu Shixiang^{1
,
,},
Cai Junhua⁴,
Li Yicai⁵,
Zhang Yao⁵

1.
College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China
2.
School of Civil and Architectural Engineering, East China University of Technology, Nanchang 330013, China
3.
School of Architectural Engineering, Guangzhou City Construction College, Guangzhou 510925, China
4.
Sanming Traffic Construction Group Co., Ltd., Sanming 365000, China
5.
The 1st Engineering Ltd. of China Railway 11th Bureau Group, Xiangyang 441104, China
6.
School of Civil Engineering, Central South University, Changsha 410075, China

Received Date: 2022-04-18
Publish Date: 2024-11-25

Abstract

Abstract

When a tunnel collapse occurs, decision makers often do not have enough reaction time to take appropriate reinforcement measures. Advance prediction of tunnel collapse failure probability has become a key issue in tunnel engineering construction. As for assessing the tunneling collapse failure probability and providing basic risk-controlling strategies, in this study it proposes a novel multi-source information fusion approach that combines the cloud model (CM), the multi-output gaussian process regression (MOGPR), and the improved D-S evidence theory. The fusion of multiple monitoring data (vault displacement, horizontal convergence displacement) reduces data uncertainty and improves the accuracy and robustness of assessment results. In addition, the surrounding rock deformation predicted by artificial intelligence is used as a source of information to obtain an advanced collapse failure probability assessment. As a result, decision makers have a longer response time before the collapse occurs. Applying the method to the Jinzhupa tunnel provides decision makers with more response time. In the end, only a small amount of deformation cracks were generated in the surrounding rock support, avoiding the tunnel collapse.
- tunnel collapse,
- failure probability assessment,
- cloud model,
- D-S evidence theory,
- multi-output gaussian process regression,
- safety engineering,
- engineering geology

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References(24)

References

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