Earthquakes and Structures

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A novel liquefaction prediction framework for seismically-excited tunnel lining

  • Shafiei, Payam (Department of Civil Engineering, Qazvin Branch, Islamic Azad University) ;
  • Azadi, Mohammad (Department of Civil Engineering, Qazvin Branch, Islamic Azad University) ;
  • Razzaghi, Mehran Seyed (Department of Civil Engineering, Qazvin Branch, Islamic Azad University)
  • Received : 2021.09.21
  • Accepted : 2022.04.22
  • Published : 2022.04.25
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Abstract

A novel hybrid extreme machine learning-multiverse optimizer (ELM-MVO) framework is proposed to predict the liquefaction phenomenon in seismically excited tunnel lining inside the sand lens. The MVO is applied to optimize the input weights and biases of the ELM algorithm to improve its efficiency. The tunnel located inside the liquefied sand lens is also evaluated under various near- and far-field earthquakes. The results demonstrate the superiority of the proposed method to predict the liquefaction event against the conventional extreme machine learning (ELM) and artificial neural network (ANN) algorithms. The outcomes also indicate that the possibility of liquefaction in sand lenses under far-field seismic excitations is much less than the near-field excitations, even with a small magnitude. Hence, tunnels designed in geographical areas where seismic excitations are more likely to be generated in the near area should be specially prepared. The sand lens around the tunnel also has larger settlements due to liquefaction.

Keywords

References

  1. Amiri, M., Amnieh, H.B., Hasanipanah, M. and Khanli, L.M. (2016), "A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure", Eng. Comput., 32(4), 631-644. https://doi.org/10.1007/s00366-016-0442-5.
  2. Andreotti, G., Calvi, G.M., Soga, K., Gong, C. and Ding, W. (2020), "Cyclic model with damage assessment of longitudinal joints in segmental tunnel linings", Tunnell. Underground Space Technol., 103, 103472. https://doi.org/10.1016/j.tust.2020.103472.
  3. Azadi, M. and Bryson, L.S. (2018), "Effect of Width Variation of Liquefiable Sand Lens on Surface Settlement Due to Shallow Tunneling", In International Congress and Exhibition" Sustainable Civil Infrastructures: Innovative Infrastructure Geotechnology", Springer, Cham, November https://doi.org/10.1007/978-3-030-01920-4_13.
  4. Azadi, M. and Hosseini, S.M.M. (2010), "The uplifting behavior of shallow tunnels within the liquefiable soils under cyclic loadings", Tunnell. Underground Space Technol., 25(2), 158-167. https://doi.org/10.1016/j.tust.2009.10.004.
  5. Beheshti, K. (1998), The Investigation of the Behavior of Saturated Sand Lenses within the Soil Deposits under Dynamic Loading, Dissertation, Amirkabir University of Technology, Tehran, Iran.
  6. Cai, M., Hocine, O., Mohammed, A.S., Chen, X., Amar, M.N. and Hasanipanah, M. (2021), "Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential", Eng. Comput., 1-13. https://doi.org/10.1007/s00366-021-01392-w.
  7. Cetin, K.O., Cakir, E., Ilgac, M., Can, G., Soylemez, B., Elsaid, A. and Gor, M. (2021), "Geotechnical aspects of reconnaissance findings after 2020 January 24th, M6. 8 Sivrice-Elazig-Turkey earthquake", Bull. Earthquake Eng., 1-45. https://doi.org/10.1007/s10518-021-01112-1.
  8. Fattah, M.Y., Hamoo, M.J. and Dawood, S.H. (2015), "Dynamic response of a lined tunnel with transmitting boundaries", Earthq., Struct., 8(1), 275-304. https://doi.org/10.12989/eas.2015.8.1.275.
  9. Ficarella, E., Lamberti, L. and Degertekin, S.O. (2021), "Comparison of three novel hybrid metaheuristic algorithms for structural optimization problems", Comput. Struct., 244, 106395. https://doi.org/10.1016/j.compstruc.2020.106395.
  10. Gopirajan, P.V., Gopinath, K.P., Sivaranjani, G. and Arun, J. (2021), "Optimization of hydrothermal liquefaction process through machine learning approach: process conditions and oil yield", Biomass Convers. Biorefin., 1-10. https://doi.org/10.1007/s13399-020-01233-8.
  11. Holchin, J.D. and Vallejo, L.E. (1995), "The liquefaction of sand lenses due to cyclic loading", In Proc., 3rd International Conf. on Recent Advances in Geotechnical Earthquake Engineering and Dynamics, Missouri.
  12. Hosseini, S.M.M. and Azadi, M. (2012), "Effect of the location of liquefiable sand lenses on shallow tunnels during earthquake loading", Arab. J. Sci. Eng., 37(3), 575-586. https://doi.org/10.1007/s13369-012-0192-7.
  13. Huang, G.B., Zhu, Q.Y. and Siew, C.K. (2004), "Extreme learning machine: a new learning scheme of feedforward neural networks", In 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), Ieee, July. https://doi.org/10.1109/IJCNN.2004.1380068.
  14. Jafarnia, M. and Varzaghani, M.I. (2016), "Effect of near field earthquake on the monuments adjacent to underground tunnels using hybrid FEA-ANN technique", Earthq. Struct., 10(4), 757-768. https://doi.org/10.12989/eas.2016.10.4.757.
  15. Jahangir, H. and Eidgahee, D.R. (2021), "A new and robust hybrid artificial bee colony algorithm-ANN model for FRP-concrete bond strength evaluation", Compos Struct., 257, 113160. https://doi.org/10.1016/j.compstruct.2020.113160.
  16. Jain, A.K., Mao, J. and Mohiuddin, K.M. (1996), "Artificial neural networks: A tutorial", Comput., 29(3), 31-44. https://doi.org/10.1109/2.485891
  17. Karlik, B. and Olgac, A.V. (2011), "Performance analysis of various activation functions in generalized MLP architectures of neural networks", Int J. Artif Intell. Expert Syst., 1(4), 111-122.
  18. Kim, S., Tom, T.H., Takeda, M. and Mase, H. (2021), "A framework for transformation to nearshore wave from global wave data using machine learning techniques: Validation at the Port of Hitachinaka, Japan", Ocean Eng., 221, 108516. https://doi.org/10.1016/j.oceaneng.2020.108516.
  19. Li, J., Cheng, J.H., Shi, J.Y. and Huang, F. (2012), "Brief introduction of back propagation (BP) neural network algorithm and its improvement", In Advances in computer science and information engineering, Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30223-7_87.
  20. Lin, S.S., Shen, S.L., Zhou, A. and Xu, Y.S. (2021), "Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods", Autom. Constr., 122, 103490. https://doi.org/10.1016/j.autcon.2020.103490.
  21. Marcelino, P., de Lurdes Antunes, M., Fortunato, E. and Gomes, M.C. (2021), "Machine learning approach for pavement performance prediction", Int J. Pavement Eng., 22(3), 341-354. https://doi.org/10.1080/10298436.2019.1609673.
  22. Miranda, L., Caldeira, L., Serra, J. and Gomes, R.C. (2020), "Dynamic behaviour of Tagus River sand including liquefaction", Bull. Earthquake Eng., 18(10), 4581-4604. https://doi.org/10.1007/s10518-020-00881-5.
  23. Mirjalili, S., Mirjalili, S.M. and Hatamlou, A. (2016), "Multi-verse optimizer: A nature-inspired algorithm for global optimization", Neural Comput. Appl., 27(2), 495-513. https://doi.org/10.1007/s00521-015-1870-7.
  24. Moosazadeh, S., Namazi, E., Aghababaei, H., Marto, A., Mohamad, H. and Hajihassani, M. (2019), "Prediction of building damage induced by tunnelling through an optimized artificial neural network", Eng. Comput., 35(2), 579-591. https://doi.org/10.1007/s00366-018-0615-5.
  25. Nguyen, H., Vu, T., Vo, T.P. and Thai, H.T. (2021), "Efficient machine learning models for prediction of concrete strengths", Constr. Build Mater., 266, 120950. https://doi.org/10.1016/j.conbuildmat.2020.120950.
  26. Pashangpishe, Y. (2004), Mechanism of Soil Deformation Due to Double Lenses Liquefaction and Critical Depth Determination, Master Thesis, Amirkabir University of Technology, Tehran, Iran.
  27. Prasad, B.R., Eskandari, H. and Reddy, B.V. (2009), "Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN", Constr. Build Mater., 23(1), 117-128. https://doi.org/10.1016/j.conbuildmat.2008.01.014.
  28. Priddy, K.L. and Keller, P.E. (2005), Artificial Neural Networks: An Introduction, SPIE Press.
  29. Seyrfar, A., Ataei, H., Movahedi, A. and Derrible, S. (2021), "Data-driven approach for evaluating the energy efficiency in multifamily residential buildings", Pract Period. Struct Des. Constr., 26(2), 04020074. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000555.
  30. Shokri (1996), "Evaluation of the liquefaction potential of sand lenses", Dissertation, Amirkabir University of Technology, Tehran, Iran.
  31. Taylor, E.J. and Madabhushi, S.P.G. (2020), "Remediation of liquefaction-induced floatation of non-circular tunnels", Tunnell. Underground Space Technol., 98, 103301. https://doi.org/10.1016/j.tust.2020.103301.
  32. Tien Bui, D., Abdullahi, M.A.M., Ghareh, S., Moayedi, H. and Nguyen, H. (2021), "Fine-tuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete", Eng. Comput., 37(1), 701-712. https://doi.org/10.1007/s00366-019-00850-w.
  33. Tsinidis, G., de Silva, F., Anastasopoulos, I., Bilotta, E., Bobet, A., Hashash, Y.M. nd Fuentes, R. (2020), "Seismic behaviour of tunnels: From experiments to analysis", Tunnell. Underground Space Technol., 99, 103334. https://doi.org/10.1016/j.tust.2020.10333.
  34. Vallejo, L.E. (1988), "The liquefaction of sand lenses during an earthquake", In Earthquake Engineering and Soil Dynamics II-Recent Advances in Ground-Motion Evaluation, ASCE, June.
  35. Zhao, K., Wang, Q., Wu, Q., Chen, S., Zhuang, H. and Chen, G. (2020), "Stability of immersed tunnel in liquefiable seabed under wave loadings", Tunnell. Underground Space Technol., 102, 103449. https://doi.org/10.1016/j.tust.2020.103449.
  36. Li, S., Zhang, Y., Cao, M. and Wang, Z. (2022), "Study on excavation sequence of pilot tunnels for a rectangular tunnel using numerical simulation and field monitoring method", Rock Mech. Rock Eng., 1-17. https://doi.org/10.1007/s00603-022-02814-x.
  37. Zhang, Y., Tang, J., Cheng, Y., Huang, L., Guo, F., Yin, X. and Li, N. (2022), "Prediction of landslide displacement with dynamic features using intelligent approaches", Int. J. Mining Sci. Technol., https://doi.org/10.1016/j.ijmst.2022.02.004.