References
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Pashangpishe, Y. (2004), Mechanism of Soil Deformation Due to Double Lenses Liquefaction and Critical Depth Determination, Master Thesis, Amirkabir University of Technology, Tehran, Iran.
- 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.
- Priddy, K.L. and Keller, P.E. (2005), Artificial Neural Networks: An Introduction, SPIE Press.
- 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.
- Shokri (1996), "Evaluation of the liquefaction potential of sand lenses", Dissertation, Amirkabir University of Technology, Tehran, Iran.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.