Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Wu KJ (2016) Under the condition of high ground stress soft rock tunnel deformation analysis and control research. Dissertation, South China University of Technology

  2. Shen CR, Jiang BS, Wu XS et al (2010) Application of tunnel monitoring measurement information feedback in surrounding rock dynamic classification. In: The 2nd international conference on information science and engineering. https://doi.org/10.1109/ICISE.2010.5690819

  3. Xiong XB (2014) Research on grey system model and its application on displacement prediction in tunnel surrounding rock. Open Mech Eng J 8:514–518. https://doi.org/10.2174/1874155X01408010514

    Article  Google Scholar 

  4. Ding WT, Li SC, Wang SG (2006) Prediction of grey system model on convergence of surrounding rock deformation of Longtan tunnel. Rock Soil Mech 27(supp):118–121

    Google Scholar 

  5. Sun ZJ (2012) Stress predicting on the surrounding rocks of tunnel based on BP neural network. Appl Mech Mater 170–173:1638–1642. https://doi.org/10.4028/www.scientific.net/AMM.170-173.1638

    Article  Google Scholar 

  6. Leu SS, Chen CN, Chang SL (2001) Data mining for tunnel support stability: neural network approach. Autom Constr 10:429–441. https://doi.org/10.1016/s0926-5805(00)00078-9

    Article  Google Scholar 

  7. Mahdevari S, Torabi SR (2012) Prediction of tunnel convergence using artificial neural networks. Tunn Undergr Space Technol 8:218–228. https://doi.org/10.1016/j.tust.2011.11.002

    Article  Google Scholar 

  8. Yao BZ, Yang CY, Yao JB et al (2010) Tunnel surrounding rock displacement prediction using support vector machine. Int J Comput Intell Syst 3(6):843–852. https://doi.org/10.1080/1875891.2010.9727746

    Article  Google Scholar 

  9. Yao B, Yao J, Zhang M et al (2014) Improved support vector machine regression in multi-step-ahead prediction for rock displacement surrounding a tunnel. Sci Iran 21(4):1309–1316

    Google Scholar 

  10. Yao BZ, Yang CY, Yu B et al (2010) Applying support vector machines to predict tunnel surrounding rock displacement. Appl Mech Mater 29–32:1717–1721. https://doi.org/10.4028/www.scientific,net/AMM.19-32.1717

    Article  Google Scholar 

  11. Wu QD, Yan B, Zhang C et al (2014) Displacement prediction of tunnel surrounding rock: a comparison of support vector machine and artificial neural network. Math Probl Eng 2014:1–6. https://doi.org/10.1155/2014/351496

    Article  Google Scholar 

  12. Lan Q, Shuang L, Fang C (2011) Research on support vector machine prediction on surrounding rock deformation based on fuzzy information granulation. In: International conference on consumer electronics. https://doi.org/10.1109/CECNET.2011.5769451

  13. Fattahi H (2016) Application of improved support vector regression model for prediction of deformation modulus of a rock mass. Eng Comput. https://doi.org/10.1007//s00366-016-0433-6

    Article  Google Scholar 

  14. Li XB, Wang QS, Yao JR et al (2008) Chaotic time series prediction for surrounding rock’s deformation of deep mine lanes in soft rock. J Cent South Univ Technol 15:224–229. https://doi.org/10.1007/s11771-008-0043-6

    Article  Google Scholar 

  15. Mao GX, Xia YY, Liu LW (2011) Time series forecasting tunnel surrounding rock displacement. Adv Mater Res 261–263:1789–1793. https://doi.org/10.4028/www.scientific.net/AMR/261-263.1789

    Article  Google Scholar 

  16. Li C (2011) Macro-grammatical evolution for nonlinear time series modeling—a case study of reservoir inflow forecasting. Eng Comput 27:393–404. https://doi.org/10.1007//s00366-011-0212-3

    Article  Google Scholar 

  17. Wei J, Qi J, Wu Y et al (2013) Prediction of the deformation of the surrounfing rock around tunnels by GA-BP network model. Appl Mech Mater 256–259:1157–1160. https://doi.org/10.4028/www.scientific.net/AMM.256-259.1157

    Article  Google Scholar 

  18. Wang XR, Wang YH, Jia XF (2009) Intelligent direct analysis of physical and mechanical parameters of tunnel surrounding rock based on adaptive immunity algorithm and BP neural network. Acad Xi’an Jiaotong Univ 21(1):22–30

    MathSciNet  Google Scholar 

  19. Ping J (2013) Application of grey theory and wavelet neural network in slope displacements prediction. J Appl Sci 13(21):4764–4768. https://doi.org/10.3923/jas.2013.4764.4768

    Article  Google Scholar 

  20. Jing HW, Wu JH, Ma B et al (2012) Prediction model and its application of deep mine tunnel surrounding rock deformation based on fuzzy-gray system. J China Coal Soc 37(7):33–38

    Google Scholar 

  21. Wang DD, Qiu GQ, Xie WB et al (2012) Deformation prediction model of surrounding rock based on GA-LSSVM-Markov. Nat Sci 4(2):85–90. https://doi.org/10.4236/ns.2012.42013

    Article  Google Scholar 

  22. Jao JB, Yao BZ, Li L et al (2012) Hybrid model for displacement prediction of tunnel surrounding rock. Neural Netw World 22(3):263–275

    Article  Google Scholar 

  23. Jiang YZ, Liu HZ, Liu JY (2013) LS-SVM-Markov model for dam deformation prediction. Appl Mech Mater 423–426:1144–1149. https://doi.org/10.4028/www.scientific.net/AMM.423-426.1144

    Article  Google Scholar 

  24. Wang XR, Wang YH (2009) Intelligent direct analysis of physical and mechanical parameters of tunnel surrounding rock based on adaptive immunity algorithm and BP neural network. J Pharm Anal 21(1):22–30

    Google Scholar 

  25. Zhao C (2016) Optimization and simulation of multi-echelon inventory in supply chain under stochastic demand based on control theory. Dissertation, University of International Business and Economics

  26. Wang KW, Deng C, Li JP, Zhang YY et al (2017) Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network. Epidemiol Infect 145(6):1118–1129. https://doi.org/10.1017/S090268816003216

    Article  Google Scholar 

  27. Liu SP, Zhang Y (2012) City fire forecasts and analysis based on nonlinear auto-regressive time-series model. Appl Mech Mater 241–244:1550–1555. https://doi.org/10.4028/www.scientific.net/amm.241-244.1550

    Article  Google Scholar 

  28. Yassin IM, Zabidi A, Salleh MKM et al (2013) Nonlinear auto-regressive (NAR) model. In: IEEE 3rd international conference on system engineering and technology

  29. Fan N, Wang SW, Liu CX (2017) Wheel wear prediction of High-Speed train using NAR and BP neural networks. In: IEEE international conference on internet of thing (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE smart data (SmartData)

  30. Xu RJ (2017) Research of rail traffic flow forecasting based on delay nonlinear autoregressive the neural network. Dissertation, Chongqing University

  31. Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol, pp 79–89

    Google Scholar 

  32. Yang XS (2009) Firefly algorithms for multimodal optimization. Mathematics 5792:169–178. https://doi.org/10.1007/978-3-642-04944-6_14

    Article  MathSciNet  MATH  Google Scholar 

  33. Yang XS, Deb S (2010) Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. Stud Comput Intell 284:101–111. https://doi.org/10.1007/978-3-642-12538-6_9

    Article  MATH  Google Scholar 

  34. Li YC, Zhou SJ, Wu C (2015) Bionic intelligent algorithm and its application in civil engineering. Science Press, Beijing, pp 153–166

    Google Scholar 

  35. Wang J, Chen L, Su R et al (2018) The Beishan underground research laboratory for geological disposal of high-level radioactive waste in China: planning, site selection, site characterization and in situ tests. J Rock Mech Geotech Eng 10:411–435. https://doi.org/10.1016/j.jrmge.2018.03.002

    Article  Google Scholar 

  36. Li YX (2015) Parameter study on firefly algorithm. Logist Eng Manag 9:195–197

    Google Scholar 

Download references

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.

Author information

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

Authors
  1. Yue Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ju Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongsu Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shuling Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shiku Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jianli Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lei Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Erbing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erbing Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-019-00894-y

Keywords

Search

Navigation