Computers and Concrete

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Computational intelligence models for predicting the frictional resistance of driven pile foundations in cold regions

  • Shiguan Chen (School of Architecture and Civil Engineering, Xi'an University of Science and Technology) ;
  • Huimei Zhang (College of Sciences, Xi'an University of Science and Technology) ;
  • Kseniya I. Zykova (Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Mishref Campus) ;
  • Hamed Gholizadeh Touchaei (Department of Civil Engineering, Southern Illinois University Edwardsville) ;
  • Chao Yuan (College of Sciences, Xi'an University of Science and Technology) ;
  • Hossein Moayedi (Institute of Research and Development, Duy Tan University) ;
  • Binh Nguyen Le (Institute of Research and Development, Duy Tan University)
  • Received : 2022.12.06
  • Accepted : 2023.05.08
  • Published : 2023.08.25
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Abstract

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m2), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.

Keywords

Acknowledgement

The authors are grateful for financial support from the National Natural Science Foundation of China (12172280).

References

  1. Abedini, M. and Zhang, C. (2022), "Residual capacity assessment of post-damaged RC columns exposed to high strain rate loading", Steel Compos. Struct., 45(3), 389-408. https://doi.org/10.12989/scs.2022.45.3.389.
  2. Adnan Ikram, R.M., Khan, I., Moayedi, H., Ahmadi Dehrashid, A., Elkhrachy, I. and Nguyen Le, B. (2023), "Novel evolutionary-optimized neural network for predicting landslide susceptibility", Environ. Dev. Sustainab., 2023, 1-33. https://doi.org/10.1007/s10668-023-03356-0.
  3. Adnan, R.M., Dai, H.L., Kuriqi, A., Kisi, O. and Zounemat-Kermani, M. (2023a), "Improving drought modeling based on new heuristic machine learning methods", Ain Shams Eng. J., 14(10), 102168. https://doi.org/10.1016/j.asej.2023.102168.
  4. Adnan, R.M., Dai, H.L., Mostafa, R.R., Islam, A.R.M.T., Kisi, O., Elbeltagi, A. and Zounemat-Kermani, M. (2023b), "Application of novel binary optimized machine learning models for monthly streamflow prediction", Appl. Water Sci., 13(5), 110. https://doi.org/10.1007/s13201-023-01913-6.
  5. Adnan, R.M., Mostafa, R.R., Dai, H.L., Heddam, S., Kuriqi, A. and Kisi, O. (2023c), "Pan evaporation estimation by relevance vector machine tuned with new metaheuristic algorithms using limited climatic data", Eng. Appl. Comput. Fluid Mech., 17(1), 2192258. https://doi.org/10.1080/19942060.2023.2192258.
  6. Aghakhani, M. and Naderian, P. (2015), "Modeling and optimization of dilution in SAW in the presence of Cr2O3 nano-particles", Int. J. Adv. Manuf. Technol., 78, 1665-1676. https://doi.org/10.1007/s00170-014-6733-3.
  7. Ahangar-Asr, A., Faramarzi, A., Mottaghifard, N. and Javadi, A.A. (2011), "Modeling of permeability and compaction characteristics of soils using evolutionary polynomial regression", Comput. Geosci., 37(11), 1860-1869. https://doi.org/10.1016/j.cageo.2011.04.015.
  8. Al-Salloum, Y.A., Shah, A.A., Abbas, H., Alsayed, S.H., Almusallam, T.H. and Al-Haddad, M. (2012), "Prediction of compressive strength of concrete using neural networks", Comput. Concrete, 10(2), 197-217. https://doi.org/10.12989/cac.2012.10.2.197.
  9. Almufti, S., Asaad, R. and Salim, B. (2018), "Review on elephant herding optimization algorithm performance in solving optimization problems", Int. J. Eng. Technol., 7, 6109-6114.
  10. Asteris, P.G., Armaghani, D.J., Hatzigeorgiou, G.D., Karayannis, C.G. and Pilakoutas, K. (2019), "Predicting the shear strength of reinforced concrete beams using artificial neural networks", Comput. Concrete, 24(5), 469-488. https://doi.org/10.12989/cac.2019.24.5.469.
  11. ASTM (2013), 4945-13: Standard Test Method for High Strain Testing of Piles, American Society for Testing and Materials International, West Conshohocken, PA, USA.
  12. Barolli, L., Hellinckx, P. and Enokido, T. (2019), Advances on Broad-Band Wireless Computing, Communication and Applications: Proceedings of the 14th International Conference on Broad-Band Wireless Computing, Communication and Applications (BWCCA-2019), Springer Nature, Cham, Switzerland.
  13. Benali, A. and Nechnech, A. (2011), "Prediction of the pile capacity in purely coherent soils using the approach of the artificial neural networks", International Seminar, Innovation and Valorization in Civil Engineering and Construction Materials, Rabat, Morocco, November.
  14. Bilgehan, M. and Turgut, P. (2010), "The use of neural networks in concrete compressive strength estimation", Comput. Concrete, 7(3), 271-283. https://doi.org/10.12989/cac.2010.7.3.271.
  15. Bornholdt, S. and Graudenz, D. (1992), "General asymmetric neural networks and structure design by genetic algorithms", Neural netw., 5(2), 327-334. https://doi.org/10.1109/ICSMC.1993.384939.
  16. Chen, J., Tong, H., Yuan, J., Fang, Y. and Gu, R. (2022), "Permeability prediction model modified on Kozeny-Carman for building foundation of clay soil", Build., 12(11), 1798. https://doi.org/10.3390/buildings12111798.
  17. Cheng, F., Li, J., Zhou, L. and Lin, G. (2023), "Fragility analysis of nuclear power plant structure under real and spectrum-compatible seismic waves considering soil-structure interaction effect", Eng. Struct., 280, 115684. https://doi.org/10.1016/j.engstruct.2023.115684.
  18. Cheng, G.D. (2001), "International achievements of study on frozen soil mechanics and engineering-Summary of the international symposium on ground freezing and frost action in soils", Adv. Earth Sci., 16(3), 293. https://doi.org/10.11867/j.issn.1001-8166.2001.03.0293.
  19. Dang, P., Cui, J., Liu, Q. and Li, Y. (2023), "Influence of source uncertainty on stochastic ground motion simulation: A case study of the 2022 Mw 6.6 Luding, China, earthquake", Stoch. Environ. Res. Risk Assess., 37, 2943-2960. https://doi.org/10.1007/s00477-023-02427-y.
  20. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002), "A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE Trans. Evol. Comput., 6(2), 182-197. https://doi.org/10.1109/4235.996017.
  21. Dreyfus, G. (2005), Neural Networks: Methodology and Applications, Springer Science & Business Media, Berlin, Germany.
  22. Fu, Z., Yang, B., Shan, M., Li, T., Zhu, Z., Ma, C., Zhang, X., Gou, G., Wang, Z. and Gao, W. (2020), "Hydrogen embrittlement behavior of SUS301L-MT stainless steel laser-arc hybrid welded joint localized zones", Corros. Sci., 164, 108337. https://doi.org/10.1016/j.corsci.2019.108337.
  23. Garrett, J. (1994), "Where and why artificial neural networks are applicable in civil engineering", J. Comput. Civil Eng., 8, 129-130. http://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(129).
  24. Ge, X., Yang, Z., Still, B. and Li, Q. (2012), "Experimental study of frozen soil mechanical properties for seismic design of pile foundations", Cold Regions Engineering 2012: Sustainable Infrastructure Development in a Changing Cold Environment, American Society of Civil Engineers, Reston, VA, USA.
  25. Ghaedi, M., Ansari, A., Habibi, M. and Asghari, A. (2014), "Removal of malachite green from aqueous solution by zinc oxide nanoparticle loaded on activated carbon: kinetics and isotherm study", J. Indust. Eng. Chem., 20(1), 17-28. https://doi.org/10.1016/j.jiec.2013.04.031.
  26. Goh, A.T.C. (1995), "Back-propagation neural networks for modeling complex systems", Artif. Intell. Eng., 9(3), 143-151. https://doi.org/10.1016/0954-1810(94)00011-S.
  27. Goh, A.T.C. (1996), "Pile driving records reanalyzed using neural networks", J. Geotech. Eng., 122(6), 492-495. https://doi.org/10.1061/(ASCE)0733-9410(1996)122:6(492).
  28. Gu, M., Cai, X., Fu, Q., Li, H., Wang, X., Mao, B. (2022), "Numerical analysis of passive piles under surcharge load in extensively deep soft soil", Build., 12(11), 1988. https://doi.org/10.3390/buildings12111988.
  29. Guo, K., Gou, G., Lv, H. and Shan, M. (2022), "Jointing of CFRP/5083 aluminum alloy by induction brazing: Processing, connecting mechanism, and fatigue performance", Coat., 12(10), 1559. https://doi.org/10.3390/coatings12101559.
  30. Hansen, N., Muller, S.D. and Koumoutsakos, P. (2003), "Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)", Evol. Comput., 11(1), 1-18. https://doi.org/10.1162/106365603321828970.
  31. Hansen, N. and Ostermeier, A. (2001), "Completely derandomized self-adaptation in evolution strategies", Evol. Comput., 9(2), 159-195. https://doi.org/10.1162/106365601750190398.
  32. Hodhod, O.A., Said, T.E. and Ataya, A.M. (2018), "Prediction of creep in concrete using genetic programming hybridized with ANN", Comput. Concrete., 21(5), 513-523. https://doi.org/10.12989/cac.2018.21.5.513.
  33. Holland, J.H. (1992), "Genetic algorithms", Sci. Am., 267(1), 66-73. https://doi.org/10.1038/scientificamerican0792-66
  34. Hong, Y., Yao, M. and Wang, L. (2023), "A multi-axial bounding surface py model with application in analyzing pile responses under multi-directional lateral cycling", Comput. Geotech., 157, 105301. https://doi.org/10.1016/j.compgeo.2023.105301.
  35. Hou, X., Zhang, L., Su, Y., Gao, G., Liu, Y., Na, Z., Xu, Q., Ding, T., Xiao, L. and Li, L. (2023), "A space crawling robotic bio-paw (SCRBP), enabled by triboelectric sensors for surface identification", Nano Energy, 105, 108013. https://doi.org/10.1016/j.nanoen.2022.108013.
  36. Houck, C.R., Joines, J. and Kay, M.G. (1995), "A genetic algorithm for function optimization: A Matlab implementation", Ncsu-ie tr, 95(9), 1-10.
  37. Huang, H., Guo, M., Zhang, W., Zeng, J., Yang, K. and Bai, H. (2021a), "Numerical investigation on the bearing capacity of RC columns strengthened by HPFL-BSP under combined loadings", J. Build. Eng., 39, 102266. https://doi.org/10.1016/j.jobe.2021.102266.
  38. Huang, H., Huang, M., Zhang, W., Guo, M. and Liu, B. (2022a), "Progressive collapse of multistory 3D reinforced concrete frame structures after the loss of an edge column", Struct. Infrastruct. Eng., 18(2), 249-265. https://doi.org/10.1080/15732479.2020.1841245.
  39. Huang, H., Huang, M., Zhang, W. and Yang, S. (2021b), "Experimental study of predamaged columns strengthened by HPFL and BSP under combined load cases", Struct. Infrastruct. Eng., 17(9), 1210-1227. https://doi.org/10.1080/15732479.2020.1801768.
  40. Huang, H., Li, M., Yuan, Y. and Bai, H. (2022b), "Theoretical analysis on the lateral drift of precast concrete frame with replaceable artificial controllable plastic hinges", J. Build. Eng., 62, 105386. https://doi.org/10.1016/j.jobe.2022.105386.
  41. Huang, S., Huang, M. and Lyu, Y. (2021c), "Seismic performance analysis of a wind turbine with a monopile foundation affected by sea ice based on a simple numerical method", Eng. Appl. Comput. Fluid Mech., 15(1), 1113-1133. https://doi.org/10.1080/19942060.2021.1939790.
  42. Huang, S., Lyu, Y., Sha, H. and Xiu, L. (2021d), "Seismic performance assessment of unsaturated soil slope in different groundwater levels", Landslides, 18(8), 2813-2833. https://doi.org/10.1007/s10346-021-01674-w.
  43. Ikram, R.M.A., Dehrashid, A.A., Zhang, B., Chen, Z., Le, B.N. and Moayedi, H. (2023a), "A novel swarm intelligence: Cuckoo optimization algorithm (COA), and SailFish optimizer (SFO), in landslide susceptibility assessment", Stoch. Environ. Res. Risk Assess., 37(5), 1717-1743. https://doi.org/10.1007/s00477-022-02361-5.
  44. Ikram, R.M.A., Hazarika, B.B., Gupta, D., Heddam, S. and Kisi, O. (2023b), "Streamflow prediction in mountainous region using new machine learning and data preprocessing methods: a case study", Neural Comput. Appl., 35(12), 9053-9070. https://doi.org/10.1007/s00521-022-08163-8.
  45. Jahed, A.D., Hasanipanah, M., Mahdiyar, A., Abd Majid, M.Z., Bakhshandeh Amnieh, H. and Tahir, M. (2018), "Airblast prediction through a hybrid genetic algorithm-ANN model", Neural Comput. Appl., 29(9), 619-629. https://doi.org/10.1007/s00521-016-2598-8.
  46. Jia, S., Dai, Z., Zhou, Z., Ling, H., Yang, Z., Qi, L., Wang, Z., Zhang, X., Thanh, H.V. and Soltanian, M.R. (2023), "Upscaling dispersivity for conservative solute transport in naturally fractured media", Water Res., 235, 119844. https://doi.org/10.1016/j.watres.2023.119844.
  47. Jianbin, Z., Jiewen, T. and Yongqiang, S. (2010), "An ANN Model for Predicting Level Ultimate Bearing Capacity of PHC Pipe Pile", Earth and Space 2010: Engineering, Science, Construction, and Operations in Challenging Environments, American Society of Civil Engineers, Reston, VA, USA.
  48. Kiefa, M.A. (1998), "General regression neural networks for driven piles in cohesionless soils", J. Geotech. Geoenviron. Eng., 124(12), 1177-1185. https://doi.org/10.1061/(ASCE)1090-0241(1998)124:12(1177).
  49. Knowles, J.D. and Corne, D.W. (2000), "Approximating the nondominated front using the pareto archived evolution strategy", Evol. Comput., 8(2), 149-172. https://doi.org/10.1162/106365600568167.
  50. Li, J., Chen, M. and Li, Z. (2022), "Improved soil-structure interaction model considering time-lag effect", Comput. Geotech., 148, 104835. https://doi.org/10.1016/j.compgeo.2022.104835.
  51. Likins, G. and Rausche, F. (2004), "Correlation of CAPWAP with static load tests", Proceedings of the Seventh International Conference on the Application of Stresswave Theory to Piles, Kuala Lumpur, Malaysia, August.
  52. Liu, C., Cui, J., Zhang, Z., Liu, H., Huang, X. and Zhang, C. (2021), "The role of TBM asymmetric tail-grouting on surface settlement in coarse-grained soils of urban area: Field tests and FEA modelling", Tunnell. Undergr. Sp. Technol., 111, 103857. https://doi.org/10.1016/j.tust.2021.103857.
  53. Liu, C., Peng, Z., Cui, J., Huang, X., Li, Y. and Chen, W. (2023a), "Development of crack and damage in shield tunnel lining under seismic loading: Refined 3D finite element modeling and analyses", Thin Wall. Struct., 185, 110647. https://doi.org/10.1016/j.tws.2023.110647.
  54. Liu, H., Yue, Y., Liu, C., Spencer Jr, B. and Cui, J. (2023b), "Automatic recognition and localization of underground pipelines in GPR B-scans using a deep learning model", Tunnell. Undergr. Sp. Technol., 134, 104861. https://doi.org/10.1016/j.tust.2022.104861.
  55. Liu, J., Wang, T. and Wen, Z. (2018), "Research on pile performance and state-of-the-art practice in cold regions", Sci. Cold Arid Reg., 10(1), 1-11. https://doi.org/10.3724/SP.J.1226.2018.00001.
  56. Lunardini, V. (1996), "Climatic warming and the degradation of warm permafrost", Permafr. Perigl. Process., 7(4), 311-320. https://doi.org/10.1002/(SICI)1099-1530(199610)7:4%3C311::AID-PPP234%3E3.0.CO;2-H.
  57. Masouleh, S.F. and Fakharian, K. (2008), "Application of a continuum numerical model for pile driving analysis and comparison with a real case", Comput. Geotech., 35(3), 406-418. https://doi.org/10.1016/j.compgeo.2007.08.009.
  58. Mirjalili, S. (2016), "Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems", Neural Comput. Appl., 27(4), 1053-1073. https://doi.org/10.1007/s00521-015-1920-1.
  59. Moayedi, H., Abdullahi, M.M., Nguyen, H. and Rashid, A.S.A. (2021), "Comparison of dragonfly algorithm and Harris hawks optimization evolutionary data mining techniques for the assessment of bearing capacity of footings over two-layer foundation soils", Eng. Comput., 37(1), 437-447. https://doi.org/10.1007/s00366-019-00834-w.
  60. Moayedi, H., Canatalay, P.J., Ahmadi Dehrashid, A., Cifci, M.A., Salari, M., Le, B.N. (2023a), "Multilayer perceptron and their comparison with two nature-inspired hybrid techniques of biogeography-based optimization (BBO), and backtracking search algorithm (BSA), for assessment of landslide Susceptibility", Land, 12(1), 242. https://doi.org/10.3390/land12010242.
  61. Moayedi, H. and Hayati, S. (2018), "Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods", Appl. Soft Comput., 66, 208-219. https://doi.org/10.1016/j.asoc.2018.02.027.
  62. Moayedi, H. and Khasmakhi, M.A.S.A. (2023), "Wildfire susceptibility mapping using two empowered machine learning algorithms", Stoch. Environ. Res. Risk Assess., 37(1), 49-72. https://doi.org/10.1007/s00477-022-02273-4.
  63. Moayedi, H., Mehrabi, M., Mosallanezhad, M., Rashid, A.S.A. and Pradhan, B. (2019), "Modification of landslide susceptibility mapping using optimized PSO-ANN technique", Eng. Comput., 35(3), 967-984. https://doi.org/10.1007/s00366-018-0644-0.
  64. Moayedi, H., Salari, M., Dehrashid, A.A. and Le, B.N. (2023b), "Groundwater quality evaluation using hybrid model of the multi-layer perceptron combined with neural-evolutionary regression techniques: Case study of Shiraz plain", Stoch. Environ. Res. Risk Assess., 37, 2961-2976. https://doi.org/10.1007/s00477-023-02429-w.
  65. Moayedi, H., Varamini, N., Mosallanezhad, M. and Foong, L.K. (2022), "Applicability and comparison of four nature-inspired hybrid techniques in predicting driven piles' friction capacity", Transp. Geotech., 37, 100875. https://doi.org/10.1016/j.trgeo.2022.100875.
  66. Moayedi, H., Yildizhan, H., Al-Bahrani, M. and Le Van, B. (2023c), "Appraisal of energy loss reduction in green buildings using large-scale experiments compiled with swarm intelligent solutions", Sustain. Energy Technol. Assess., 57, 103215. https://doi.org/10.1016/j.seta.2023.103215.
  67. Moayedi, H., Yildizhan, H., Aungkulanon, P., Escorcia, Y.C., Al-Bahrani, M., Le, B.N. (2023d), "Green building's heat loss reduction analysis through two novel hybrid approaches", Sustain. Energy Technol. Assess., 55, 102951. https://doi.org/10.1016/j.seta.2022.102951.
  68. Naderian, P., Aghakhani, M. and Khoshboo, S. (2022), "Modelling the hardness of weld metal in the submerged arc welding of low carbon steel plates: Addition of CR2O3 nanoparticles", Adv. Mater. Process. Technol., 9(1), 221-236. https://doi.org/10.1080/2374068X.2022.2091186.
  69. Nicolai, G., Ibsen, L.B., O'Loughlin, C. and White, D. (2017), "Quantifying the increase in lateral capacity of monopiles in sand due to cyclic loading", Geotech. Lett., 7(3), 245-252. https://doi.org/10.1680/jgele.16.00187.
  70. Ornek, M., Laman, M., Demir, A. and Yildiz, A. (2012), "Prediction of bearing capacity of circular footings on soft clay stabilized with granular soil", Soil. Found., 52(1), 69-80. https://doi.org/10.1016/j.sandf.2012.01.002.
  71. Padmini, D., Ilamparuthi, K. and Sudheer, K. (2008), "Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models", Comput. Geotech., 35(1), 33-46. https://doi.org/10.1016/j.compgeo.2007.03.001.
  72. Pal, M. and Deswal, S. (2008), "Modeling pile capacity using support vector machines and generalized regression neural network", J. Geotech. Geoenviron. Eng., 134(7), 1021-1024. https://doi.org/10.1061/(ASCE)1090-0241(2008)134:7(1021).
  73. Park, H., Seok, J. and Hwang, D. (2006), "Hybrid neural network and genetic algorithm approach to the prediction of bearing capacity of driven piles", Proceedings of the 6th European Conference on Numerical Methods in Geotechnical Engineering-Numerical Methods in Geotechnical Engineering, Graz, Austria, September.
  74. Pasdarpour, M., Ghazavi, M., Teshnehlab, M. and Sadrnejad, S.A. (2009), "Optimal design of soil dynamic compaction using genetic algorithm and fuzzy system", Soil Dyn. Earthq. Eng., 29(7), 1103-1112. https://doi.org/10.1016/j.soildyn.2008.09.003.
  75. Poormirzaee, R., Moghadam, R.H. and Zarean, A. (2015), "Inversion seismic refraction data using particle swarm optimization: A case study of Tabriz, Iran", Arab. J. Geosci., 8(8), 5981-5989. https://doi.org/10.1007/s12517-014-1662-x.
  76. Sadowski, L., Nikoo, M. and Nikoo, M. (2018), "Concrete compressive strength prediction using the imperialist competitive algorithm", Comput. Concrete., 22(4), 355-363. https://doi.org/10.12989/cac.2018.22.4.355.
  77. Shabani, S., Varamesh, S., Moayedi, H. and Le Van, B. (2023), "Modeling the susceptibility of an uneven-aged broad-leaved forest to snowstorm damage using spatially explicit machine learning", Environ. Sci. Pollut. Res., 30(12), 34203-34213. https://doi.org/10.1007/s11356-022-24660-8.
  78. Shahin, M.A. and Jaksa, M.B. (2009), Contemporary Topics in In Situ Testing, Analysis, and Reliability of Foundations, American Society of Civil Engineers, Reston, VA, USA.
  79. Shahin, M.A., Jaksa, M.B. and Maier, H.R. (2001), "Artificial neural network applications in geotechnical engineering", Aust. Geomech., 36(1), 49-62.
  80. Simpson, P. (1990), Artificial Neural System-Foundation, Paradigm, Application and Implementation, Pergamon Press New York, New York, NY, USA.
  81. Soleimanbeigi, A. and Hataf, N. (2006), "Prediction of settlement of shallow foundations on reinforced soils using neural networks", Geosynthet. Int., 13(4), 161-170. https://doi.org/10.1680/gein.2006.13.4.161.
  82. Tahwia, A.M., Heniegal, A., Elgamal, M.S. and Tayeh, B.A. (2021), "The prediction of compressive strength and non-destructive tests of sustainable concrete by using artificial neural networks", Comput. Concrete., 27(1), 21-28. https://doi.org/10.12989/cac.2021.27.1.021.
  83. Teh, C., Wong, K., Goh, A. and Jaritngam, S. (1997), "Prediction of pile capacity using neural networks", J. Comput. Civil Eng., 11(2), 129-138. https://doi.org/10.1061/(ASCE)0887-3801(1997)11:2(129).
  84. Tsai, H.C. and Liao, M.C. (2019), "Knowledge-based learning for modeling concrete compressive strength using genetic programming", Comput. Concrete., 23(4), 255-265. https://doi.org/10.12989/cac.2019.23.4.255.
  85. Vanishree, J. and Ramesh, V. (2018), "Optimization of size and cost of static var compensator using dragonfly algorithm for voltage profile improvement in power transmission systems", Int. J. Renewab. Energy Res. (IJRER), 8(1), 56-66.
  86. Vundavilli, P.R. and Pratihar, D.K. (2009), "Soft computing-based gait planners for a dynamically balanced biped robot negotiating sloping surfaces", Appl. Soft Comput., 9(1), 191-208. https://doi.org/10.1016/j.asoc.2008.04.004.
  87. Wang, C., Zhao, W. and Cui, Y. (2020), "Changes in the seasonally frozen ground over the eastern Qinghai-Tibet Plateau in the past 60 years", Front. Earth Sci., 8, 270. https://doi.org/10.3389/feart.2020.00270.
  88. Wang, G.G., Deb, S. and Coelho, L.D.S. (2015), "Elephant herding optimization", 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), Bali, Indonesia, December.
  89. Wang, G.G., Deb, S., Gao, X.Z. and Coelho, L.D.S. (2016), "A new metaheuristic optimisation algorithm motivated by elephant herding behaviour", Int. J. Bio Inspir. Comput., 8(6), 394-409. https://doi.org/10.1504/IJBIC.2016.081335.
  90. Wang, G.G., Deb, S., Zhao, X. and Cui, Z. (2018), "A new monarch butterfly optimization with an improved crossover operator", Oper. Res., 18(3), 731-755. https://doi.org/10.1007/s12351-016-0251-z.
  91. Whitley, D. (1994), "A genetic algorithm tutorial", Stat. Comput., 4(2), 65-85. https://doi.org/10.1007/BF00175354.
  92. Wikelski, M., Moskowitz, D., Adelman, J.S., Cochran, J., Wilcove, D.S. and May, M.L. (2006), "Simple rules guide dragonfly migration", Biol. Lett., 2(3), 325-329. https://doi.org/10.1098/rsbl.2006.0487.
  93. Wu, M., Ba, Z. and Liang, J. (2022a), "A procedure for 3D simulation of seismic wave propagation considering source-path-site effects: Theory, verification and application", Earthq. Eng. Struct. Dyn., 51(12), 2925-2955. https://doi.org/10.1002/eqe.3708.
  94. Wu, Z., Xu, J., Li, Y. and Wang, S. (2022b), "Disturbed state concept-based model for the uniaxial strain-softening behavior of fiber-reinforced soil", Int. J. Geomech., 22(7), 04022092. https://doi.org/10.1061/(ASCE)GM.1943-5622.0002415.
  95. Xu, J., Lan, W., Ren, C., Zhou, X., Wang, S. and Yuan, J. (2021), "Modeling of coupled transfer of water, heat and solute in saline loess considering sodium sulfate crystallization", Cold Reg. Sci. Technol., 189, 103335. https://doi.org/10.1016/j.coldregions.2021.103335.
  96. Xue, X. (2018), "Evaluation of concrete compressive strength based on an improved PSO-LSSVM model", Comput. Concrete., 21(5), 505-511. https://doi.org/10.12989/cac.2018.21.5.505.
  97. Yang, S.T., Li, X.Y., Yu, T.L., Wang, J., Fang, H., Nie, F., He, B., Zhao, L., Lu, W.M. and Yan, S.S. (2022), "High-performance neuromorphic computing based on ferroelectric synapses with excellent conductance linearity and symmetry", Adv. Funct. Mater., 32(35), 2202366. https://doi.org/10.1002/adfm.202202366.
  98. Yin, Z.Y., Jin, Y.F., Shen, J.S. and Hicher, P.Y. (2018), "Optimization techniques for identifying soil parameters in geotechnical engineering: Comparative study and enhancement", Int. J. Numer. Anal. Method. Geomech., 42(1), 70-94. https://doi.org/10.1002/nag.2714.
  99. Zhan, C., Dai, Z., Soltanian, M.R. and de Barros, F.P. (2022), "Data-Worth analysis for heterogeneous subsurface structure identification with a stochastic deep learning framework", Water Resour. Res., 58(11), e2022WR033241. https://doi.org/10.1029/2022WR033241.
  100. Zhan, C., Dai, Z., Yang, Z., Zhang, X., Ma, Z., Thanh, H.V. and Soltanian, M.R. (2023), "Subsurface sedimentary structure identification using deep learning: A review", Earth Sci. Rev., 239, 104370. https://doi.org/10.1016/j.earscirev.2023.104370
  101. Zhang, X., Yu, S., Wang, W., Guan, J., Xu, Z. and Chen, X. (2022), "Nonlinear seismic response of the bridge pile foundation with elevated and embedded caps in frozen soils", Soil Dyn. Earthq. Eng., 161, 107403. https://doi.org/10.1016/j.soildyn.2022.107403.
  102. Zhang, Z., Wu, Q., Xun, X., Wang, B. and Wang, X. (2018), "Climate change and the distribution of frozen soil in 1980- 2010 in northern northeast China", Quarter. Int., 467, 230-241. https://doi.org/10.1016/j.quaint.2018.01.015.
  103. Zhao, L., Cheng, G., Li, S., Zhao, X. and Wang, S. (2000), "Thawing and freezing processes of active layer in Wudaoliang region of Tibetan Plateau", Chin. Sci. Bull., 45, 2181-2187. https://doi.org/10.1007/BF02886326.
  104. Zhou, Y., Wang, X., Niu, F., He, F., Guo, C., Liu, D. and Jiang, D. (2021), "Frost jacking characteristics of transmission tower pile foundations with and without thermosyphons in permafrost regions of Qinghai-Tibet plateau", J. Cold Reg. Eng., 35(2), 04021004. https://doi.org/10.1061/(ASCE)CR.1943-5495.0000246.
  105. Zhu, Q., Chen, J., Gou, G., Chen, H. and Li, P. (2017), "Ameliorated longitudinal critically refracted-Attenuation velocity method for welding residual stress measurement", J. Mater. Process. Technol., 246, 267-275. https://doi.org/10.1016/j.jmatprotec.2017.03.022.
  106. Zorlu, K., Gokceoglu, C., Ocakoglu, F., Nefeslioglu, H., Acikalin, S. (2008), "Prediction of uniaxial compressive strength of sandstones using petrography-based models", Eng. Geol., 96(3-4), 141-158. https://doi.org/10.1016/j.enggeo.2007.10.009.