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Research on deformation prediction of tunnel surrounding rock using the model combining firefly algorithm and nonlinear auto-regressive dynamic neural network

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Abstract

Tunnel surrounding rock deformation is dynamic, sensitive to time and space, nonlinear, and highly complicated. By combining the firefly algorithm (FA) and nonlinear auto-regressive (NAR) dynamic neural network method, an algorithm model was proposed for predicting dynamic nonlinear surrounding rock deformation. The FA improved the prediction accuracy of the NAR dynamic neural network by determining the optimum values of two network parameters—delay order and number of units in the hidden layer; combined with the monitoring results of Beishan exploration tunnel (BET), this is demonstrated by a comparative analysis of predictions yielded by the FA–NAR dynamic neural network and by the least squares support vector machine (LS-SVM). In general, the comparation shows that the FA–NAR dynamic neural network model yielded predictions that are fundamentally consistent with measurements and exhibits higher prediction accuracy than the LS-SVM. Results also show that the surrounding rock deformation prediction of BET for March 4, 2020 was marginally smaller than 2.43 mm.

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Acknowledgements

The authors wish to extend their sincere thanks to the support from the China Atomic Energy Authority (CAEA) for China’s URL Development Program and the Geological Disposal Program.

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Authors and Affiliations

  1. College of Defense Engineering, Army Engineering University of PLA, Nanjing, 210007, Jiangsu, China

    Yue Pan, Shuling Cai, Shiku Pu, Jianli Duan, Lei Gao & Erbing Li

  2. CNNC Key Laboratory on Geological Disposal of High-Level Radioactive Waste, Beijing Research Institute of Uranium Geology, Beijing, 100029, China

    Liang Chen, Ju Wang & Hongsu Ma

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  1. Yue Pan
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Correspondence to Erbing Li.

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Pan, Y., Chen, L., Wang, J. et al. Research on deformation prediction of tunnel surrounding rock using the model combining firefly algorithm and nonlinear auto-regressive dynamic neural network. Engineering with Computers 37, 1443–1453 (2021). https://doi.org/10.1007/s00366-019-00894-y

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