Skip to main content
SpringerLink
Log in

3D prediction of tunneling-induced ground movements based on a hybrid ANN and empirical methods

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

Abstract

Tunnel construction in urban areas causes ground displacement which may distort and damage overlying buildings and municipal utilities. It is therefore extremely important to predict tunneling-induced ground movements in tunneling projects. To predict the tunneling-induced ground movements, artificial neural networks (ANNs) have been used as flexible non-linear approximation functions. These methods, however, have significant limitations that decrease their accuracy and applicability. To overcome these problems, the use of optimization algorithms to train ANNs is of advantage. In this paper, a hybrid particle swarm optimization (PSO) algorithm-based ANN is developed to predict the maximum surface settlement and inflection points in transverse and longitudinal directions. Subsequently, the transverse and longitudinal troughs were obtained by means of empirical equations and 3D surface settlement troughs were ploted. For this purpose, extensive data consisting of measured settlements from 123 settlement markers, geotechnical properties and tunneling parameters were collected from the Karaj Urban Railway Project in Iran. The optimum values of PSO parameters were determined with the help of sensitivity analysis. On the other hand, to find the optimal architecture of the network, trial-and-error method was used. The final hybrid model including eight inputs, a hidden layer and three outputs was used to predict transverse and longitudinal tunneling-induced ground movements. The results demonstrated that the proposed model can very accurately predict three-dimensional ground movements induced by tunneling.

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. Peck RB (1969) Deep excavations and tunnelling in soft ground. In: Proceedings of the 7th international conference on soil mechanics and foundation engineering, State of the art volume, Mexico City, pp 225–290

  2. Attewell PB, Woodman JP (1982) Predicting the dynamics of ground settlement and its derivatives caused by tunnelling in soils. Ground Eng 15(8):13–22, 36

    Google Scholar 

  3. Park KH (2004) Elastic solution for tunnelling-induced ground movements in clays. ASCE Int J Geomech 4(4):310–318

    Article  Google Scholar 

  4. Zhao C, Lavasan AA, Barciaga T, Kämper C, Mark P, Schanz T (2017) Prediction of tunnel lining forces and deformations using analytical and numerical solutions. Tunn Undergr Space Technol 64:164–176

    Article  Google Scholar 

  5. Wang HN, Chen XP, Jiang MJ, Song F, Wu L (2018) The analytical predictions on displacement and stress around shallow tunnels subjected to surcharge loadings. Tunn Undergr Space Technol 71:403–427

    Article  Google Scholar 

  6. Yu H, Cai C, Bobet A, Zhao X, Yuan Y (2019) Analytical solution for longitudinal bending stiffness of shield tunnels. Tunn Undergr Space Technol 83:27–34

    Article  Google Scholar 

  7. Lee KM, Rowe RK, Lo KY (1992) Subsidence owing to tunnelling I: estimating the gap parameter. Can Geotech J 29:929–940

    Article  Google Scholar 

  8. Mroueh H, Shahrour I (2003) A full 3-D finite element analysis of tunneling–adjacent structures interaction. Comput Geotech 30(3):245–253

    Article  Google Scholar 

  9. Namazi E, Mohamad H, Jorat ME, Hajihassani M (2011) Investigation on the effects of twin tunnel excavations beneath a road underpass. Electron J Geotech Eng 16(1):1–8

    Google Scholar 

  10. Ahmed M, Iskander M (2011) Analysis of tunnelling-induced ground movements using transparent soil models. J Geotech Geoenviron Eng ASCE 137:525–535

    Article  Google Scholar 

  11. Divall S, Goodey RJ (2012) Apparatus for centrifuge modelling of sequential twin-tunnel construction. Int J Phys Model Geotech 12(3):102–111

    Google Scholar 

  12. He C, Feng K, Fang Y, Jiang YC (2012) Surface settlement caused by twin-parallel shield tunnelling in sandy cobble strata. J Zhejiang Univ Sci A (Appl Phys Eng) 13(11):858–869

    Article  Google Scholar 

  13. Sun X, Chen F, Miao C, Song P, Li G, Zhao C, Xia X (2018) Physical modeling of deformation failure mechanism of surrounding rocks for the deep-buried tunnel in soft rock strata during the excavation. Tunn Undergr Space Technol 74:247–261

    Article  Google Scholar 

  14. Meguid MA, Saada O, Nunes MA, Mattar J (2008) Physical modeling of tunnels in soft ground: a review. Tunnelling Underground Space Technol 23:185–198

    Article  Google Scholar 

  15. Neaupane KM, Adhikari NR (2006) Prediction of tunnelling-induced ground movement with the multi-layer perceptron. Tunn Undergr Space Technol 21:151–159

    Article  Google Scholar 

  16. Santos OJ Jr, Celestino TB (2008) Artificial neural networks of Sao Paulo subway tunnel settlement data. Tunn Undergr Space Technol 23:481–491

    Article  Google Scholar 

  17. Boubou R, Emerilault F, Kastner R (2010) Artificial neural network application for the prediction of ground surface movements induced by shield tunnelling. Can Geotech J 47:1214–1233

    Article  Google Scholar 

  18. Suwansawat S, Einstein HH (2006) Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling. Tunn Undergr Space Technol 21(2):133–150

    Article  Google Scholar 

  19. Gori M. Tesi A (1992) On the problem of local minima in backpropagation. IEEE Trans Pattern Anal Mach Intell 14(1):76–86

    Article  Google Scholar 

  20. Kröse B, Smagt PVD (1996) An introduction to neural networks. The University of Amsterdam, Amsterdam

    Google Scholar 

  21. Priddy KL, Keller PE (2005) Artificial neural networks: an introduction. In: Bellingham, Society of Photo-Optical Instrumentation Engineers (SPIE)

  22. Chen XL, Fu JP, Yao JL, Gan JF (2018) Prediction of shear strength for squat RC walls using a hybrid ANN–PSO model. Eng Comput 34(2):367–383

    Article  Google Scholar 

  23. Hasanipanah M, Shahnazar A, Bakhshandeh Amnieh H, Jahed Armaghani D (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO-SVR model. Eng Comput 33(1):23–31

    Article  Google Scholar 

  24. Gaur S, Ch S, Graillot D, Chahar BR, Kumar DN (2013) Application of artificial neural networks and particle swarm optimization for the management of groundwater resources. Water Resour Manag 27(3):927–941

    Article  Google Scholar 

  25. Moosazadeh S, Namazi E, Aghababaei H, Marto A, Mohamad H, Hajihassani M (2018) Prediction of building damage induced by tunnelling through an optimized artificial neural network. Eng Comput 2018:1–13

    Google Scholar 

  26. Hajihassani M, Jahed Armaghani D, Kalatehjari R (2018) Applications of particle swarm optimization in geotechnical engineering: a comprehensive review. Geotech Geol Eng. https://doi.org/10.1007/s10706-017-0356-z

    Article  Google Scholar 

  27. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  MathSciNet  MATH  Google Scholar 

  28. Rosenblatt F (1959) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65:386–408

    Article  Google Scholar 

  29. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by backpropagation errors. Nature 323:533–536

    Article  MATH  Google Scholar 

  30. Hopfield JJ, Tank DW (1986) Computing with neural circuits—a model. Science 233:625–633

    Article  Google Scholar 

  31. Jain AK, Mao J, Mohiuddin KM (1996) Artificial neural networks: a tutorial. Computer 29(3):31–44

    Article  Google Scholar 

  32. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, Perth, Australia, pp 1942–1948

  33. Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of IEEE international congress on evolutionary computation, pp 69–73

  34. Hajihassani M, Jahed Armaghani DJ, Sohaei H, Mohamad ET, Marto A (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67

    Article  Google Scholar 

  35. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization an overview. Swarm Intell 1:33–57

    Article  Google Scholar 

  36. Bansal JC, Singh PK, Saraswat M, Verma A, Jadon SS, Abraham A (2011) Inertia weight strategies in particle swarm optimization. In: Third World congress on nature biologically inspired computing IEEE, pp 640–647

  37. Clerc M, Kennedy J (2002) The particle swarm explosion, stability, and convergence in a multi-dimensional complex space. IEEE Trans Evolut Comput 6(1):58–73

    Article  Google Scholar 

  38. Diamantidis NA, Karlis D, Giakoumakis EA (2000) Unsupervised stratification of cross validation for accuracy estimation. Artif Intell 116(1):1–16

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mining, Faculty of Engineering, Urmia University, Urmia, 5756151818, Iran

    M. Hajihassani

  2. Built Environment Engineering Department, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, 1010, New Zealand

    R. Kalatehjari

  3. Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia Kuala Lumpur, 54100, Kuala Lumpur, Malaysia

    A. Marto

  4. Civil and Environmental Engineering Department, Universiti Teknologi PETRONAS, Perak, Malaysia

    H. Mohamad

  5. Tunnel Rod Construction Consulting Engineers Ins., North Sohrevardi Street, Tehran, 1555913351, Iran

    M. Khosrotash

Authors
  1. M. Hajihassani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Kalatehjari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Marto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. H. Mohamad
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. Khosrotash
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Hajihassani.

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

Hajihassani, M., Kalatehjari, R., Marto, A. et al. 3D prediction of tunneling-induced ground movements based on a hybrid ANN and empirical methods. Engineering with Computers 36, 251–269 (2020). https://doi.org/10.1007/s00366-018-00699-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-018-00699-5

Keywords

Search

Navigation