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Q-method optimization of tunnel surrounding rock classification by fuzzy reasoning model and support vector machine

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Abstract

This work aims to cope with the increasingly complex geological conditions in the process of traffic tunnel construction in China, improve the stability of tunnel construction, and reduce the probability of collapse, water gushing and other hazards in the construction process. The instability and deformation mechanism of tunnel surrounding rock is explored based on the theoretical knowledge of support vector machine (SVM), fuzzy reasoning, and Q classification method, and combined with the characteristics of tunnel surrounding rock in China. Moreover, a classification model via SVM and fuzzy reasoning is constructed for tunnel surrounding rock, followed by the outline of the shortcomings of tunnel surrounding rock classification in China. Then, the corresponding optimization method is put forward according to Q classification method. Finally, simulation experiments are conducted on arch collapse surrounding rock to evaluate the deformation stability. Experimental results demonstrate that the unstable area of tunnel surrounding rock increases with the increase in tunnel span, and the supporting treatment area required by the tunnel is also greatly enlarged. With the increase in span, the settlement value of vault increases continuously, and the increase rate varies with the hardness of surrounding rock. Moreover, the influence of the change of structural plane spacing of surrounding rock on the stability of surrounding rock gradually reduces. The influence of surrounding rock with larger hardness is more significant than that of surrounding rock with smaller hardness. Furthermore, there is a corresponding relationship between the hardness of rock and the rebound value. Besides, through the comparison between the actual surrounding rock test and the surrounding rock grade, there is no significantly corresponding relationship between the rebound value and the surrounding rock grade. Therefore, the rebound instrument can be used as an auxiliary tool for determining the surrounding rock grade. The research conclusion is that the classification optimization method of tunnel surrounding rock reported here facilitates the classification speed of tunnel surrounding rock.

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Acknowledgements

The authors acknowledge the help from the university colleagues.

Funding

This research was funded by Key research and development program of Shandong province (Grant number: 2019GSF111022, Dr. Peng He); National Natural Science Foundation of China (Grant number: 51909150, Dr. Peng He).

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Authors and Affiliations

  1. Department of Civil Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Feng Jiang, Peng He, Gang Wang, Chengcheng Zheng, Zhiyong Xiao & Yue Wu

  2. School of Data Science and Software Engineering, Qingdao University, Qingdao, 266590, China

    Zhihan Lv

Authors
  1. Feng Jiang
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  2. Peng He
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  3. Gang Wang
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  4. Chengcheng Zheng
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  5. Zhiyong Xiao
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  6. Yue Wu
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  7. Zhihan Lv
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Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication. Feng Jiang contributed to the conception of the study; Peng He contributed significantly to analysis Gang Wang contributed manuscript preparation; Chengcheng Zheng and Zhiyong Xiao performed the data analyses and wrote the manuscript; Yue Wu helped perform the analysis with constructive discussions. Zhihan Lv provides important help in the process of article revision.

Corresponding author

Correspondence to Gang Wang.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by Irfan Uddin.

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Jiang, F., He, P., Wang, G. et al. Q-method optimization of tunnel surrounding rock classification by fuzzy reasoning model and support vector machine. Soft Comput 26, 7545–7558 (2022). https://doi.org/10.1007/s00500-021-06581-9

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