隧道围岩变形预测及趋势判断方法

Hazard Control in Tunnelling and Underground Engineering

2019, Vol. 1

Issue (4): 103-108 DOI:

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Deformation prediction and trend judgment method of tunnel surrounding rock

FU Junsheng

No.3 Engineering Co., Ltd., CCCC First Harbor Engineering Co., Ltd., Dalian 116083, Liaoning, China

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Abstract In order to give full play to the role and value of monitoring and measurement in the tunnel construction process, the Nalang Tunnel was used as the background of the engineering example.The support vector machine was used as the theoretical basis, and its model parameters were optimized by using particle swarm optimization and chaos theory. A chaos optimized PSO-SVM model was constructed to achieve accurate prediction of tunnel deformation. The re-calibration range method was used to judge the development trend of tunnel deformation to prove the accuracy of the aforementioned prediction effect. The case study showed that the model parameters of the support vector machine could be effectively optimized by the trial algorithm and the particle swarm algorithm, and the chaos theory could effectively weaken the residual sequence of the prediction results. The average relative errors of the prediction results were less than 2%, which validated the research. The validity of the prediction model; at the same time, the re-standard range analysis showed that the tunnel deformation will continue to increase, but the increase rate tended to decrease. The obtained results were consistent with the prediction results, which verified the accuracy of the former analysis results. The study found that it provided a new idea for tunnel deformation prediction and was worth further promotion and application.

Key words： tunnel deformation prediction support vector machine particle swarm optimization rescaling range analysis

Received: 15 November 2019 Published: 09 March 2020

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FU Junsheng. Deformation prediction and trend judgment method of tunnel surrounding rock[J]. Hazard Control in Tunnelling and Underground Engineering, 2019, 1(4): 103-108.

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http://tunnel.sdujournals.com/EN/ OR http://tunnel.sdujournals.com/EN/Y2019/V1/I4/103

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