Geomechanics and Engineering

  • 제31권4호
  • /
  • Pages.339-352
  • /
  • 2022
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Slope stability prediction using ANFIS models optimized with metaheuristic science

  • Gu, Yu-tian (College of Geoscience & Surveying Engineering, China University of Mining and Technology (Beijing)) ;
  • Xu, Yong-xuan (China Construction Second Engineering Bureau LTD.) ;
  • Moayedi, Hossein (Institute of Research and Development, Duy Tan University) ;
  • Zhao, Jian-wei (School of Electrical and Information Engineering, China University of Mining & Technology (Beijing)) ;
  • Le, Binh Nguyen (Institute of Research and Development, Duy Tan University)
  • 투고 : 2021.07.20
  • 심사 : 2022.10.28
  • 발행 : 2022.11.25
⟨ 이전 논문 다음 논문 ⟩

초록

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (FS) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the FS behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

키워드

참고문헌

  1. Balakrishnan, K., Dhanalakshmi, R. and Khaire, U.M. (2022), "A novel control factor and Brownian motion-based improved Harris Hawks Optimization for feature selection", J. Ambient Intell. Humanized Comput., 1-23. https://doi.org/10.1007/s12652-021-03621-y.
  2. Bhatt, P. (2022), "Harmonics mitigated multi-objective energy optimization in PV integrated rural distribution network using modified TLBO algorithm", Renew. Energ. Focus, 40, 13-22. https://doi.org/10.1016/j.ref.2021.11.001.
  3. Bui, X.N., Muazu, M.A. and Nguyen, H. (2019), "Optimizing Levenberg-Marquardt backpropagation technique in predicting factor of safety of slopes after two-dimensional OptumG2 analysis", Eng. Comput., 1-12. https://doi.org/10.1007/s00366-019-00741-0.
  4. Chen, Z., Liu, Z., Yin, L. and Zheng, W. (2022), "Statistical analysis of regional air temperature characteristics before and after dam construction", Urban Climate, 41 101085. https://doi.org/10.1016/j.uclim.2022.101085
  5. Cho, S.E. (2007), "Effects of spatial variability of soil properties on slope stability", Eng. Geol., 92(3-4), 97-109. https://doi.org/10.1016/j.enggeo.2007.03.006.
  6. Dai, J., Feng, H., Shi, K., Ma, X., Yan, Y., Ye, L. and Xia, Y. (2022), "Electrochemical degradation of antibiotic enoxacin using a novel PbO2 electrode with a graphene nanoplatelets inter-layer: Characteristics, efficiency and mechanism", Chemosphere, 307, 135833. https://doi.org/10.1016/j.chemosphere.2022.135833.
  7. Eskandar, H., Sadollah, A., Bahreininejad, A. and Hamdi, M. (2012), "Water cycle algorithm-A novel metaheuristic optimization method for solving constrained engineering optimization problems", Comput. Struct., 110, 151-166.
  8. Faramarzi, A., Heidarinejad, M., Stephens, B. and Mirjalili, S. (2020), "Equilibrium optimizer: A novel optimization algorithm", Knowledge-Based Syst., 191, 105190. https://doi.org/10.1016/j.knosys.2019.105190.
  9. Fattahi, H. (2017), "Prediction of slope stability using adaptive neuro-fuzzy inference system based on clustering methods", J. Min. Environ., 8(2), 163-177. https://doi.org/10.22044/jme.2016.637.
  10. Fattahi, H. and Ilghani, N.Z. (2020), "Slope stability analysis using bayesian markov chain Monte Carlo method", Geotech. Geol. Eng., 1-10. https://doi.org/10.1007/s10706-019-01172-w.
  11. Foong, L.K. and Moayedi, H. (2021), "Slope stability evaluation using neural network optimized by equilibrium optimization and vortex search algorithm", Eng. Comput., 1-15. https://doi.org/10.1007/s00366-021-01282-1.
  12. Foong, L.K., Moayedi, H. and Lyu, Z. (2020), "Computational modification of neural systems using a novel stochastic search scheme, namely evaporation rate-based water cycle algorithm: an application in geotechnical issues", Eng. Comput., 1-12.
  13. Foong, L.K., Zhao, Y., Bai, C. and Xu, C. (2021), "Efficient metaheuristic-retrofitted techniques for concrete slump simulation", Smart Struct. Syst., 27(5), 745-759. https://doi.org/10.12989/sss.2021.27.5.745.
  14. Gao, J., Amar, M.N., Motahari, M.R., Hasanipanah, M. and Armaghani, D.J. (2020), "Two novel combined systems for predicting the peak shear strength using RBFNN and meta-heuristic computing paradigms", Eng. Comput., 38, 129-140. https://doi.org/10.1007/s00366-020-01059-y.
  15. Gao, W., Raftari, M., Rashid, A.S.A., Mu'azu, M.A. and Jusoh, W.A.W. (2020), "A predictive model based on an optimized ANN combined with ICA for predicting the stability of slopes", Eng. Comput., 36(1), 325-344. https://doi.org/10.1007/s00366-019-00702-7
  16. Ghasemi, M., Zhang, C., Khorshidi, H. and Sun, L. (2022), "Seismic performance assessment of steel frames with slack cable bracing systems", Eng. Struct., 250, 113437. https://doi.org/10.1016/j.engstruct.2021.113437
  17. Ghatte, H.F. (2021), "A hybrid of firefly and biogeography-based optimization algorithms for optimal design of steel frames", Arabian J. Sci. Eng., 46(5), 4703-4717. https://doi.org/10.1007/s13369-020-05118-w.
  18. Gu, M., Mo, H., Qiu, J., Yuan, J. and Xia, Q. (2022), "Behavior of floating stone columns reinforced with geogrid encasement in model tests", Front. Mater., 9, 980851. https://doi.org/10.3389/fmats.2022.980851.
  19. Guo, Y., Yang, Y., Kong, Z. and He, J. (2022), "Development of similar materials for liquid-solid coupling and its application in water outburst and mud outburst model test of deep tunnel", Geofluids, 2022 https://doi.org/10.1155/2022/8784398.
  20. Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M. and Chen, H. (2019), "Harris hawks optimization: Algorithm and applications", Future Generation Comput. Syst., 97, 849-872. https://doi.org/10.1016/j.future.2019.02.028
  21. Himanshu, N., Kumar, V., Burman, A., Maity, D. and Gordan, B. (2020), "Grey wolf optimization approach for searching critical failure surface in soil slopes", Eng. Comput., 1-14. https://doi.org/10.1007/s00366-019-00927-6.
  22. Huang, H., Huang, M., Zhang, W. and Yang, S. (2021a), "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.
  23. Huang, S., Huang, M. and Lyu, Y. (2021b), "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.
  24. Issa, M. and Samn, A. (2022), "Passive vehicle suspension system optimization using Harris Hawk Optimization algorithm", Math. Comput. Simul., 191, 328-345. https://doi.org/10.1016/j.matcom.2021.08.016.
  25. Issac, K., Bharanidharan, N. and Rajaguru, H. (2022), Advanced computational paradigms and hybrid intelligent computing, 515-522. https://doi.org/10.1007/978-981-16-4369-9_50
  26. Jang, J.S. (1993), "ANFIS: adaptive-network-based fuzzy inference system", IEEE T. Syst. Man Cy., 23(3), 665-685. https://doi.org/10.1109/21.256541
  27. Jawad, F.K., Mahmood, M., Wang, D., Osama, A.A. and Anas, A.J. (2021), "Heuristic dragonfly algorithm for optimal design of truss structures with discrete variables", Structures, 843-862. https://doi.org/10.1016/j.istruc.2020.11.071.
  28. Ji, J., Zhang, C., Gui, Y., Lu, Q. and Kodikara, J. (2017), "New observations on the application of LS-SVM in slope system reliability analysis", J. Comput. Civil Eng., 31(2), 06016002. http://orcid.org/0000-0002-7616-2685. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000620
  29. Jiang, S., Zuo, Y., Yang, M. and Feng, R. (2021), "Reconstruction of the Cenozoic tectono-thermal history of the Dongpu Depression, Bohai Bay Basin, China: Constraints from apatite fission track and vitrinite reflectance data", J. Petroleum Sci. Eng., 205, 108809. https://doi.org/10.1016/j.petrol.2021.108809
  30. Kadri, N. and Koudil, M. (2022), "Multi-objective biogeography-based optimization and reinforcement learning hybridization for network-on chip reliability improvement", J. Parallel Distr. Com., 161, 20-36. https://doi.org/10.1016/j.jpdc.2021.11.005.
  31. Kang, F., Xu, B., Li, J. and Zhao, S. (2017), "Slope stability evaluation using Gaussian processes with various covariance functions", Appl. Soft Comput., 60, 387-396. https://doi.org/10.1016/j.asoc.2017.07.011.
  32. Karaboga, D., Akay, B. and Ozturk, C. (2007), "Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks", International conference on modeling decisions for artificial intelligence, 318-329. https://doi.org/10.1007/978-3-540-73729-2_30.
  33. Krabbenhoft, K., Lyamin, A. and Krabbenhoft, J. (2015), "Optum Computational Engineering (Optum G2)", Available on:< www.optumce.com.
  34. Li, B., Li, D., Zhang, Z., Yang, S. and Wang, F. (2015), "Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine", Appl. Math. Model. 39(17), 5253-5264. https://doi.org/10.1016/j.apm.2015.03.032.
  35. Li, J., Cheng, F., Lin, G. and Wu, C. (2022a), "Improved hybrid method for the generation of ground motions compatible with the multi-damping design spectra", J. Earthq. Eng., 1-27. https://doi.org/10.1080/13632469.2022.2095059.
  36. Li, Q., Song, D., Yuan, C. and Nie, W. (2022b), "An image recognition method for the deformation area of open-pit rock slopes under variable rainfall", Measurement, 188, 110544. https://doi.org/10.1016/j.measurement.2021.110544.
  37. Li, Y., Che, P., Liu, C., Wu, D. and Du, Y. (2021), "Cross-scene pavement distress detection by a novel transfer learning framework", Comput.-Aided Civil Infrastruct. Eng., 36(11), 1398-1415. https://doi.org/10.1111/mice.12674.
  38. Lin, Y., Zhou, K. and Li, J. (2018), "Prediction of slope stability using four supervised learning methods", IEEE Access, 6, 31169-31179. https://doi.org/10.1109/ACCESS.2018.2843787
  39. Liu, E., Chen, S., Yan, D., Deng, Y., Wang, H., Jing, Z. and Pan, S. (2022), "Detrital zircon geochronology and heavy mineral composition constraints on provenance evolution in the western Pearl River Mouth basin, northern south China sea: A source to sink approach", Mar. Petroleum Geol., 105884. https://doi.org/10.1016/j.marpetgeo.2022.105884.
  40. Liu, Y., Zhang, Z., Liu, X., Wang, L. and Xia, X. (2021), "Ore image classification based on small deep learning model: Evaluation and optimization of model depth, model structure and data size", Miner. Eng., 172 107020. https://doi.org/10.1016/j.mineng.2021.107020.
  41. Lohar, G., Sharma, S., Saha, A.K. and Ghosh, S. (2021), Applications of Internet of Things, 223-231. https://doi.org/10.1007/978-981-15-6198-6_21.
  42. Luo, Z., Bui, X.N., Nguyen, H. and Moayedi, H. (2019), "A novel artificial intelligence technique for analyzing slope stability using PSO-CA model", Eng. Comput., 1-12. https://doi.org/10.1007/s00366-019-00839-5.
  43. Mayank, M., Basson, M.S., Ramana, G.V. and Vassallo, R. (2020), "Ant colony optimization for slope stability analysis applied to an embankment failure in eastern India", Int. J. Geoeng., 11(1), https://doi.org/10.1186/s40703-020-00110-7.
  44. Mehrabi, M. (2021), "Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy", Nat. Hazards, 1-37. https://doi.org/10.1007/s11069-021-05083-z.
  45. Mehrabi, M. and Moayedi, H. (2021), "Landslide susceptibility mapping using artificial neural network tuned by metaheuristic algorithms", Environ. Earth Sci., 80(24), 1-20. https://doi.org/10.1007/s12665-021-10098-7.
  46. Mehrabi, M., Pradhan, B., Moayedi, H. and Alamri, A. (2020), "Optimizing an adaptive neuro-fuzzy inference system for spatial prediction of landslide susceptibility using four state-of-the-art metaheuristic techniques", Sensors, 20(6), 1723. https://doi.org/10.3390/s20061723,
  47. 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.
  48. Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014), "Grey wolf optimizer", Adv Eng. Softw., 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007.
  49. Mishra, M., Gunturi, V.R. and Maity, D. (2020a), "Teaching-learning-based optimisation algorithm and its application in capturing critical slip surface in slope stability analysis", Soft Comput., 24(4), 2969-2982. https://doi.org/10.1007/s00500-019-04075-3
  50. Mishra, M., Ramana, G.V. and Maity, D. (2020b), "Multiverse optimisation algorithm for capturing the critical slip surface in slope stability analysis", Geotech. Geol. Eng., 38(1), 459-474. https://doi.org/10.1007/s10706-019-01037-2.
  51. Moayedi, H., Mehrabi, M., Bui, D.T., Pradhan, B. and Foong, L.K. (2020), "Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility", J. Environ. Management, 260, 109867. https://doi.org/10.1016/j.jenvman.2019.109867.
  52. Moayedi, H., Mehrabi, M., Kalantar, B., Abdullahi Mu'azu, M.A., Rashid, A.S., Foong, L.K. and Nguyen, H. (2019a), "Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide", Geomatics, Natural Hazards Risk, 10(1), 1879-1911. https://doi.org/10.1080/19475705.2019.1650126.
  53. Moayedi, H., Mehrabi, M., Mosallanezhad, M., Rashid, A.S.A. and Pradhan, B. (2019b), "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.
  54. Moayedi, H., Nguyen, H. and Rashid, A.S.A. (2019c), "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., 1-11. https://doi.org/10.1007/s00366-019-00834-w.
  55. Moayedi, H., Osouli, A., Nguyen, H. and Rashid, A.S.A. (2019d), "A novel Harris hawks' optimization and k-fold cross-validation predicting slope stability", Eng. Comput., 1-11. https://doi.org/10.1007/s00366-019-00828-8.
  56. Muther, T., Syed, F.I., Dahaghi, A.K. and Negahban, S. (2022), "Socio-inspired multi-cohort intelligence and teaching-learning-based optimization for hydraulic fracturing parameters design in tight formations", J. Energ. Resour. Tech., 144(7), https://doi.org/10.1115/1.4052182.
  57. Nguyen, H., Mehrabi, M., Kalantar, B., Moayedi, H. and Abdullahi, M.A.M. (2019), "Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping", Geomatics, Natural Hazard. Risk, 10(1), 1667-1693. https://doi.org/10.1080/19475705.2019.1607782.
  58. Rao, R.V., Savsani, V.J. and Vakharia, D. (2011), "Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems", Comput.-Aided Design, 43(3), 303-315. https://doi.org/10.1016/j.cad.2010.12.015
  59. Roeva, O., Angelova, M., Zoteva, D. and Pencheva, T. (2020), "Water cycle algorithm for modelling of fermentation processes", Processes 8(8), 920. https://doi.org/10.3390/pr8080920,
  60. Roy, S.S. (2007), "An application of the adaptive neuro-fuzzy inference system for prediction of surface roughness in turning", Int. J. Comput. Appl. Tech., 28(4), 281-288. https://doi.org/10.1504/IJCAT.2007.014561.
  61. Rukhaiyar, S., Alam, M. and Samadhiya, N. (2018), "A PSO-ANN hybrid model for predicting factor of safety of slope", Int. J. Geotech. Eng., 12(6), 556-566. https://doi.org/10.1080/19386362.2017.1305652.
  62. Sadollah, A., Eskandar, H., Lee, H.M. and Kim, J.H. (2016), " Water cycle algorithm: a detailed standard code", SoftwareX 5, 37-43. https://doi.org/10.1016/j.softx.2016.03.001
  63. Sari, P.A., Suhatril, M., Osman, N., Mu'azu, M., Dehghani, H., Sedghi, Y., Safa, M., Hasanipanah, M., Wakil, K. and Khorami, M. (2019a), "An intelligent based-model role to simulate the factor of safe slope by support vector regression", Eng. Comput., 35(4), 1521-1531. https://doi.org/10.1007/s00366-018-0677-4.
  64. Sari, P.A., Suhatril, M., Osman, N., Mu'azu, M., Katebi, J., Abavisani, A., Ghaffari, N., Chahnasir, E.S., Wakil, K. and Khorami, M. (2019b), "Developing a hybrid adoptive neuro-fuzzy inference system in predicting safety of factors of slopes subjected to surface eco-protection techniques", Eng. Comput., 1-8. https://doi.org/10.1007/s00366-019-00768-3.
  65. Simon, D. (2008), "Biogeography-based optimization", IEEE T. Evolut. Comput., 12(6), 702-713. https://doi.org/10.1109/TEVC.2008.919004.
  66. Singh, J. and Banka, H. (2020), Machine learning algorithms for industrial applications, 195-207. https://doi.org/10.1007/978-3-030-50641-4_12.
  67. Singh, J., Kumar, R. and Banka, H. (2020), Machine learning algorithms for industrial applications, 301-315. https://doi.org/10.1007/978-3-030-50641-4_17.
  68. Sun, Y., Pan, J.S., Hu, P. and Chu, S.C. (2022), "Enhanced Equilibrium Optimizer algorithm applied in job shop scheduling problem", J. Intell. Manuf., 1-27. https://doi.org/10.1007/s10845-021-01899-5.
  69. Too, J. and Mirjalili, S. (2021), "A hyper learning binary dragonfly algorithm for feature selection: A COVID-19 case study", Knowledge-Based Syst., 212, 106553. https://doi.org/10.1016/j.knosys.2020.106553.
  70. Wang, J., Xu, Y.P., She, C., Xu, P. and Bagal, H.A. (2022), "Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm", Energy, 240, 122800. https://doi.org/10.1016/j.energy.2021.122800.
  71. Wang, S., Zhang, K., Chao, L., Li, D., Tian, X., Bao, H., Chen, G. and Xia, Y. (2021), "Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards", J. Hydrol., 603, 126964. https://doi.org/10.1016/j.jhydrol.2021.126964
  72. Wei, J., Xie, Z., Zhang, W., Luo, X., Yang, Y. and Chen, B. (2021), "Experimental study on circular steel tube-confined reinforced UHPC columns under axial loading", Eng. Struct., 230, 111599. https://doi.org/10.1016/j.engstruct.2020.111599.
  73. Wu, Z., Xu, J., Chen, H., Shao, L., Zhou, X. and Wang, S. (2022), "Shear strength and mesoscopic characteristics of basalt fiber-reinforced loess after dry-wet cycles", J. Mater. Civil Eng., 34(6), 04022083. https://doi.org/10.1061/ (ASCE)MT.1943-5533.0004225.
  74. Xie, C., Nguyen, H., Bui, X.N., Nguyen, V.T. and Zhou, J. (2021a), "Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms", J. Rock Mech. Geotech. Eng., 13(6), 1452-1465. https://doi.org/10.1016/j.jrmge.2021.07.005.
  75. Xie, S.J., Lin, H., Chen, Y.F. and Wang, Y.X. (2021b), "A new nonlinear empirical strength criterion for rocks under conventional triaxial compression", J. Central South Univ., 28(5), 1448-1458. https://doi.org/10.1007/s11771-021-4708-8
  76. Xie, W., Li, X., Jian, W., Yang, Y., Liu, H., Robledo, L.F. and Nie, W. (2021c), "A novel hybrid method for landslide susceptibility mapping-based geodetector and machine learning cluster: A case of Xiaojin county, China", ISPRS Int. J. Geo-Inform., 10(2), 93. https://doi.org/10.3390/ijgi10020093.
  77. Xie, W., Nie, W., Saffari, P., Robledo, L.F., Descote, P.Y. and Jian, W. (2021d), "Landslide hazard assessment based on Bayesian optimization-support vector machine in Nanping City, China", Nat. Hazards, 109(1), 931-948. https://doi.org/10.1007/s11069-021-04862-y.
  78. Xu, J., Wu, Z., Chen, H., Shao, L., Zhou, X. and Wang, S. (2022), "Influence of dry-wet cycles on the strength behavior of basalt-fiber reinforced loess", Eng. Geol., 302, 106645. https://doi.org/10.1016/j.enggeo.2022.106645.
  79. Xue, X. (2017), "Prediction of slope stability based on hybrid PSO and LSSVM", J. Comput. Civil Eng., 31(1), 04016041. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000607
  80. Yan, B., Ma, C., Zhao, Y., Hu, N. and Guo, L. (2019), "Geometrically enabled soft electroactuators via laser cutting", Adv. Eng. Mater., 21(11), 1900664. https://doi.org/10.1002/adem.201900664
  81. Yang, H., Hasanipanah, M., Tahir, M. and Bui, D.T. (2020), "Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO", Nat. Resour. Res., 29(2), 739-750. https://doi.org/10.1007/s11053-019-09515-3.
  82. Ye, R., Liu, P., Shi, K. and Yan, B. (2020a), "State damping control: a novel simple method of rotor UAV with high performance", IEEE Access, 8, 214346-214357. https://doi.org/10.1109/ACCESS.2020.3040779
  83. Ye, X., Moayedi, H., Khari, M. and Foong, K.L. (2020b), "Metaheuristic-hybridized multilayer perceptron in slope stability analysis", Smart Struct. Syst., 26, https://doi.org/10.12989/sss.2020.26.3.263.
  84. Yin, L., Wang, L., Keim, B.D., Konsoer, K. and Zheng, W. (2022), "Wavelet analysis of dam injection and discharge in three gorges dam and reservoir with precipitation and river discharge", Water, 14(4), 567. https://doi.org/10.3390/w14040567
  85. Yuan, J., Lei, D., Shan, Y., Tong, H., Fang, X. and Zhao, J. (2022), "Direct shear creep characteristics of sand treated with microbial-induced calcite precipitation", Int. J. Civil Eng., 1-15. https://doi.org/10.1007/s40999-021-00696-8.
  86. Zhan, C., Dai, Z., Soltanian, M.R. and Zhang, X. (2022), "Stage-wise stochastic deep learning inversion framework for subsurface sedimentary structure identification", Geophys. Res. Lett., 49(1), e2021GL095823. https://doi.org/10.1029/2021GL095823
  87. Zhang, K., Wang, S., Bao, H. and Zhao, X. (2019), "Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China", Nat. Hazards Earth Syst. Sci., 19(1), 93-105. https://doi.org/10.5194/nhess-19-93-2019
  88. Zhang, P., Wu, H.N., Chen, R.P. and Chan, T.H. (2020a), "Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study", Tunn. Undergr. Sp. Tech., 99, 103383. https://doi.org/10.1016/j.tust.2020.103383.
  89. Zhang, X. and Lin, Q. (2022), "Information-utilization strengthened equilibrium optimizer", Artif. Intel. Rev., 1-34. https://doi.org/10.1007/s10462-021-10105-0.
  90. Zhang, Z., Luo, C. and Zhao, Z. (2020b), "Application of probabilistic method in maximum tsunami height prediction considering stochastic seabed topography", Nat.Hazards, 104(3), 2511-2530. https://doi.org/10.1007/s11069-020-04283-3
  91. Zhao, Y. and Foong, L.K. (2022), "Predicting electrical power output of combined cycle power plants using a novel artificial neural network optimized by electrostatic discharge algorithm", Measurement, 111405. https://doi.org/10.1016/j.measurement.2022.111405.
  92. Zhao, Y., Hu, H., Bai, L., Tang, M., Chen, H. and Su, D. (2021a), "Fragility analyses of bridge structures using the logarithmic piecewise function-based probabilistic seismic demand model", . Sustainability, 13(14), 7814. https://doi.org/10.3390/su13147814.
  93. Zhao, Y., Hu, H., Song, C. and Wang, Z. (2022), "Predicting compressive strength of manufactured-sand concrete using conventional and metaheuristic-tuned artificial neural network", Measurement, 194 110993. https://doi.org/10.1016/j.measurement.2022.110993.
  94. Zhao, Y., Joseph, A.J.J..M., Zhang, Z., Ma, C., Gul, D., Schellenberg, A. and Hu, N. (2020a), "Deterministic snap-through buckling and energy trapping in axially-loaded notched strips for compliant building blocks", Smart Mater. Struct., 29(2), 02LT03. https://doi.org/10.1088/1361-665X/ab6486.
  95. Zhao, Y., Moayedi, H., Bahiraei, M. and Foong, L.K. (2020b), "Employing TLBO and SCE for optimal prediction of the compressive strength of concrete", Smart Struct. Syst., 26(6), 753-763. https://doi.org/10.12989/sss.2020.26.6.753.
  96. Zhao, Y. and Wang, Z. (2022), "Subset simulation with adaptable intermediate failure probability for robust reliability analysis: an unsupervised learning-based approach", Struct. Multidiscip. O., 65(6), 1-22. https://doi.org/10.1007/s00158-022-03260-7.
  97. Zhao, Y., Yan, Q., Yang, Z., Yu, X. and Jia, B. (2020c), "A novel artificial bee colony algorithm for structural damage detection", Adv. Civil Eng., 2020, https://doi.org/10.1155/2020/3743089.
  98. Zhao, Y, Zhong, X. and Foong, L.K. (2021b), "Predicting the splitting tensile strength of concrete using an equilibrium optimization model", Steel Compos. Struct., 39(1), 81-93. https://doi.org/10.12989/scs.2021.39.1.081.
  99. Zheng, W., Liu, X. and Yin, L. (2021), "Research on image classification method based on improved multi-scale relational network", PeerJ Comput. Sci., 7, e613. https://doi.org/10.7717/peerj-cs.613
  100. Zhou, G., Long, S., Xu, J., Zhou, X., Song, B., Deng, R. and Wang, C. (2021a), "Comparison analysis of five waveform decomposition algorithms for the airborne LiDAR echo signal", IEEE J. Selected Topics in Applied Earth Observations and Remote Sens., 14, 7869-7880. https://doi.org/10.1109/JSTARS.2021.3096197.
  101. Zhou, G., Zhang, R. and Huang, S. (2021b), "Generalized buffering algorithm", IEEE Access, 9, 27140-27157. https://doi.org/10.1109/ACCESS.2021.3057719.
  102. Zhou, J., Li, E., Yang, S., Wang, M., Shi, X., Yao, S. and Mitri, H.S. (2019), "Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories", Saf. Sci., 118, 505-518. https://doi.org/10.1016/j.ssci.2019.05.046.
  103. Zhu, Z., Yunlong, W. and Liang, Z. (2022), "Mining-induced stress and ground pressure behavior characteristics in mining a thick coal seam with hard roofs", Front. Earth Sci., 157. https://doi.org/10.3389/feart.2022.843191