Steel and Composite Structures

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ANN-Incorporated satin bowerbird optimizer for predicting uniaxial compressive strength of concrete

  • Wu, Dizi (School of Architecture, Changsha University of Science and Technology) ;
  • LI, Shuhua (China Construction Second Engineering Bureau LTD.) ;
  • Moayedi, Hossein (Institute of Research and Development, Duy Tan University) ;
  • CIFCI, Mehmet Akif (Department of Computer Engineering, Bandirma Onyedi Eylul University) ;
  • Le, Binh Nguyen (Institute of Research and Development, Duy Tan University)
  • Received : 2021.06.17
  • Accepted : 2022.10.25
  • Published : 2022.10.25
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Abstract

Surmounting complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology. This study integrates a novel metaheuristic technique, namely satin bowerbird optimizer (SBO) with artificial neural network (ANN) for predicting uniaxial compressive strength (UCS) of concrete. For this purpose, the created hybrid is trained and tested using a relatively large dataset collected from the published literature. Three other new algorithms, namely Henry gas solubility optimization (HGSO), sunflower optimization (SFO), and vortex search algorithm (VSA) are also used as benchmarks. After attaining a proper population size for all algorithms, the Utilizing various accuracy indicators, it was shown that the proposed ANN-SBO not only can excellently analyze the UCS behavior, but also outperforms all three benchmark hybrids (i.e., ANN-HGSO, ANN-SFO, and ANN-VSA). In the prediction phase, the correlation indices of 0.87394, 0.87936, 0.95329, and 0.95663, as well as mean absolute percentage errors of 15.9719, 15.3845, 9.4970, and 8.0629%, calculated for the ANN-HGSO, ANN-SFO, ANN-VSA, and ANN-SBO, respectively, manifested the best prediction performance for the proposed model. Also, the ANN-VSA achieved reliable results as well. In short, the ANN-SBO can be used by engineers as an efficient non-destructive method for predicting the UCS of concrete.

Keywords

References

  1. Ahmadi, M., Naderpour, H. and Kheyroddin, A. (2017), "ANN model for predicting the compressive strength of circular steelconfined concrete", Int. J. Civil Eng., 15(2), 213-221. https://doi.org/10.1007/s40999-016-0096-0
  2. Alshammari, B.M. and Guesmi, T. (2020), "New chaotic sunflower optimization algorithm for optimal tuning of power system stabilizers", J. Electric. Eng. Technol., 1-13. https://doi.org/10.1007/s42835-020-00470-1.
  3. Altintasi, C., Aydin, O., Taplamacioglu, M.C. and Salor, O. (2020), "Power system harmonic and interharmonic estimation using Vortex Search Algorithm", Electric Power Syst. Res., 182, 106187. https://doi.org/10.1016/j.epsr.2019.106187.
  4. Aslani, F., Uy, B., Tao, Z. and Mashiri, F. (2015), "Predicting the axial load capacity of high-strength concrete filled steel tubular columns", Steel Compos. Struct/. 19(4), 967-993. https://doi.org/10.12989/scs.2015.19.4.967.
  5. Bui, D.T., Ghareh, S., Moayedi, H. and Nguyen, H. (2019), "Finetuning of neural computing using whale optimization algorithm for predicting compressive strength of concrete", Eng. Comput., 1-12. https://doi.org/10.1007/s00366-019-00850-w.
  6. Chang, W. and Zheng, W. (2019), "Estimation of compressive strength of stirrup-confined circular columns using artificial neural networks", Struct. Concrete, 20(4), 1328-1339. https://doi.org/10.1002/suco.201800259.
  7. Chen, F.X., Zhong, Y.C., Gao, X.Y., Jin, Z.Q., Wang, E.D., Zhu, F.P, Shao, X.X. and He, X.Y. (2021a), "Non-uniform model of relationship between surface strain and rust expansion force of reinforced concrete", Sci. Reports, 11(1), 1-9. https://doi.org/10.1038/s41598-021-88146-2.
  8. Chen, F., Jin, Z., Wang, E., Wang, L., Jiang, Y., Guo, P., Gao, X. and He, X. (2021b), "Relationship model between surface strain of concrete and expansion force of reinforcement rust", Sci. Reports, 11(1), 1-11. https://doi.org/10.1038/s41598-021-83376-wa.
  9. Chen, W., Chen, X., Peng, J., Panahi, M. and Lee, S. (2020), "Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer". Geosci. Front., 12(1), 93-107. https://doi.org/10.1016/j.gsf.2020.07.012.
  10. Cheng, H., Sun, L., Wang, Y. and Chen, X. (2021), "Effects of actual loading waveforms on the fatigue behaviours of asphalt mixtures", Int. J. Fatigue, 151, 106386. https://doi.org/10.1016/j.ijfatigue.2021.106386.
  11. Chintam, J.R. and Daniel, M. (2018), "Real-power rescheduling of generators for congestion management using a novel satin bowerbird optimization algorithm", Energies, 11(1), 183. https://doi.org/10.3390/en11010183
  12. Dogan, B. and O lmez, T. (2015), "A new metaheuristic for numerical function optimization: Vortex Search algorithm", Inform. Sci., 293, 125-145. https://doi.org/10.1016/j.ins.2014.08.053.
  13. Dutta, S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput. Concrete, 21(4), 463-470. https://doi.org/10.12989/cac.2018.21.4.463.
  14. 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.
  15. Gomes, G.F., da Cunha, S.S. and Ancelotti, A.C. (2019), "A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates", Eng. Comput., 35(2), 619-626. https://doi.org/10.1007/s00366-018-0620-8.
  16. Gomes, G.F. and Giovani, R.S. (2020), "An efficient two-step damage identification method using sunflower optimization algorithm and mode shape curvature (MSDBI-SFO)", Eng. Comput., 1-20. https://doi.org/10.1007/s00366-020-01128-2.
  17. 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.
  18. Han, Q., Gui, C., Xu, J. and Lacidogna, G. (2019), "A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm", Construct. Build. Mater., 226, 734-742. https://doi.org/10.1016/j.conbuildmat.2019.07.315.
  19. Hao, R.B., Lu, Z.Q., Ding, H. and Chen, L.Q. (2022), "A nonlinear vibration isolator supported on a flexible plate: analysis and experiment", Nonlinear Dyn., 108(2), 941-958. https://doi.org/10.1007/s11071-022-07243-7.
  20. Hashim, F.A., Houssein, E.H., Hussain, K., Mabrouk, M.S., AlAtabany, W. (2020), "A modified Henry gas solubility optimization for solving motif discovery problem", Neural Comput. Appl., 32(14), 10759-10771. https://doi.org/10.1007/s00521-019-04611-0.
  21. Hashim, F.A., Houssein, E.H., Mabrouk, M.S., Al-Atabany, W. and Mirjalili, S. (2019), "Henry gas solubility optimization: A novel physics-based algorithm", Future Generation Compu. Syst., 101, 646-667. https://doi.org/10.1016/j.future.2019.07.015.
  22. Hu, Z., Shi, T., Cen, M., Wang, J., Zhao, X., Zeng, C., Zhou, Y., Fan, Y., Liu, Y. and Zhao, Z. (2022), "Research progress on lunar and Martian concrete", Construct. Build. Mater., 343, 128117. https://doi.org/10.1016/j.conbuildmat.2022.128117a.
  23. Huang, H., Guo, M., Zhang, W. and Huang, M. (2022), "Seismic Behavior of Strengthened RC Columns under Combined Loadings", J. Bridge Eng., 27(6), 05022005. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001871.
  24. Huang, H., 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.
  25. Huang, S., Lyu, Y., Sha, H. and Xiu, L. (2021b), "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
  26. Huang, Y., Zhang, J., Ann, F.T. and Ma, G. (2020), "Intelligent mixture design of steel fibre reinforced concrete using a support vector regression and firefly algorithm based multi-objective optimization model", Construct. Build. Mater., 260, 120457. https://doi.org/10.1016/j.conbuildmat.2020.120457.
  27. Jalal, M., Grasley, Z., Gurganus, C. and Bullard, J.W. (2020), "Experimental investigation and comparative machine-learning prediction of strength behavior of optimized recycled rubber concrete", Construct. Build. Mater., 256, 119478. https://doi.org/10.1016/j.conbuildmat.2020.119478.
  28. Lan, Y., Zheng, B., Shi, T., Ma, C., Liu, Y. and Zhao, Z. (2022), "Crack resistance properties of carbon nanotube-modified concrete", Mag. Concrete Res., 1-11. https://doi.org/10.1680/jmacr.21.00227.
  29. Liang. S., Foong, L.K. and Lyu, Z. (2020), "Determination of the friction capacity of driven piles using three sophisticated search schemes", Eng. Comput., 1-13. https://doi.org/10.1007/s00366-020-01118-4.
  30. Ma, X., Foong, L.K., Morasaei, A., Ghabussi, A. and Lyu, Z. (2020), "Swarm-based hybridizations of neural network for predicting the concrete strength", Smart Struct. Syst., 26(2), 241-251. https://doi.org/10.12989/sss.2020.26.2.241.
  31. Mehrabi, M. (2021), "Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy", Nat. Haz., 1-37. https://doi.org/10.1007/s11069-021-05083-z.
  32. Moayedi, H., Kalantar, B., Foong, L.K., Tien Bui, D. and Motevalli, A. (2019a), "Application of three metaheuristic techniques in simulation of concrete slump", Appl. Sci., 9(20), 4340. https://doi.org/10.3390/app9204340.
  33. Moayedi, H., Mehrabi, M., Kalantar, B., Abdullahi Mu'azu, M.A., Rashid, A.S., Foong, L.K. and Nguyen, H. (2019b), "Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide", Geomatic. Nat. Haz. Risk, 10(1), 1879-1911. https://doi.org/10.1080/19475705.2019.1650126.
  34. Moayedi, H., Mehrabi, M., Mosallanezhad, M., Rashid, A.S.A. and Pradhan, B. (2019c), "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.
  35. Moosavi, S.H.S. and Bardsiri, V.K. (2017), "Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation", Eng. Appl. Artif. Intell., 60, 1-15. https://doi.org/10.1016/j.engappai.2017.01.006
  36. Mostafa, M.A., Abdou, A.F., Abd El-Gawad, A.F. and El-Kholy, E. (2018), "SBO-based selective harmonic elimination for nine levels asymmetrical cascaded H-bridge multilevel inverter", Australian J. Electric. Electron. Eng., 15(3), 131-143. https://doi.org/10.1080/1448837X.2018.1528732
  37. Naderpour, H., Kheyroddin, A. and Amiri, G.G. (2010), "Prediction of FRP-confined compressive strength of concrete using artificial neural networks", Compos. Struct., 92(12), 2817-2829. https://doi.org/10.1016/j.compstruct.2010.04.008.
  38. Nazari, A. and Sanjayan, J.G. (2015), "Modelling of compressive strength of geopolymer paste, mortar and concrete by optimized support vector machine", Ceramics Int., 41(9), 12164-12177. https://doi.org/10.1016/j.ceramint.2015.06.037.
  39. Nguyen-Sy, T., Wakimm, J., To, Q.D., Vu, M.N., Nguyen, T.D. and Nguyen, T.T. (2020), "Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method", Construct. Build. Mater., 260, 119757. https://doi.org/10.1016/j.conbuildmat.2020.119757.
  40. Nguyen, H., Mehrabi, M., Kalantar, B., Moayedi, H., Abdullahi, M.AM. (2019a), "Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping", Geom. Nat. Haz. Risk, 10(1), 1667-1693. https://doi.org/10.1080/19475705.2019.1607782.
  41. Nguyen, M.S.T., Thai, D.K. and Kim, S.E. (2020), "Predicting the axial compressive capacity of circular concrete filled steel tube columns using an artificial neural network", Steel Compos. Struct., 35(3), 415-437. https://doi.org/10.12989/scs.2020.35.3.415.
  42. Nguyen, T., Kashani, A., Ngo, T. and Bordas, S. (2019b), "Deep neural network with high-order neuron for the prediction of foamed concrete strength", Comput. Aided Civil Infrastruct. Eng., 34(4), 316-332. https://doi.org/10.1111/mice.12422.
  43. Oreta, A.W. and Kawashima, K. (2003), "Neural network modeling of confined compressive strength and strain of circular concrete columns", J. Struct. Eng., 129(4), 554-561. https://doi.org/10.1061/~ASCE!0733-9445~2003!129:4~554!.
  44. Ozcan, G., Kocak, Y. and Gulbandilar, E. (2017), "Estimation of compressive strength of BFS and WTRP blended cement mortars with machine learning models", Comput. Concrete, 19(3), 275-282. https://doi.org/10.12989/cac.2017.19.3.275
  45. Sabbag, N. and Uyanik, O. (2018), "Determination of the reinforced concrete strength by apparent resistivity depending on the curing conditions", J. Appl. Geophy., 155, 13-25. https://doi.org/10.1016/j.jappgeo.2018.03.007.
  46. Sadowski, L., Nikoo, M., Shariq, M., Joker, E. and Czarnecki, S. (2019), "The nature-inspired metaheuristic method for predicting the creep strain of green concrete containing ground granulated blast furnace slag", Materials, 12(2), 293. https://doi.org/10.3390/ma12020293.
  47. Sadrossadat, E. and Basarir, H. (2019), "An evolutionary-based prediction model of the 28-day compressive strength of highperformance concrete containing cementitious materials", Adv. Civil Eng. Mater., 8(3), 484-497. https://doi.org/10.1520/ACEM20190016.
  48. Shaheen, M.A., Hasanien, H.M., Mekhamer, S. and Talaat, H.E. (2019), "Optimal power flow of power systems including distributed generation units using sunflower optimization algorithm", IEEE Access, 7, 109289-109300. https://doi.org/10.1109/ACCESS.2019.2933489.
  49. Shamshirband, S., Tavakkoli, A., Roy, C.B., Motamedi, S., Song, K.I., Hashim, R. and Islam, S.M. (2015), "Hybrid intelligent model for approximating unconfined compressive strength of cement-based bricks with odd-valued array of peat content (0-29%)", Powder Technol., 284, 560-570. https://doi.org/10.1016/j.powtec.2015.07.026.
  50. Shan, Y., Zhao, J., Tong, H., Yuan, J., Lei, D. and Li, Y. (2022), "Effects of activated carbon on liquefaction resistance of calcareous sand treated with microbially induced calcium carbonate precipitation", Soil Dyn. Earthq. Eng., 161, 107419. https://doi.org/10.1016/j.soildyn.2022.107419.
  51. Shi, T., Lan, Y., Hu, Z., Wang, H., Xu, J. and Zheng, B. (2022a), "Tensile and fracture properties of silicon carbide whiskermodified cement-based materials", Int. J. Concrete Struct. Mater., 16 (1), 1-13. https://doi.org/10.1186/s40069-021-00495-4.
  52. Shi, T., Liu, Y., Zhang, Y., Lan, Y., Zhao, Q., Zhao, Y. and Wang, H. (2022b), "Calcined attapulgite clay as supplementary cementing material: thermal treatment, hydration activity and mechanical properties", Int. J. Concrete Struct. Mater., 16(1), 1-10. https://doi.org/10.1186/s40069-021-00488-3
  53. Staudinger, J. and Roberts, P.V. (1996), "A critical review of Henry's law constants for environmental applications", Critical Rev. Environ. Sci. Technol., 26(3), 205-297. https://doi.org/10.1080/10643389609388492.
  54. Sun, J., Zhang, J., Gu, Y., Huang, Y., Sun, Y. and Ma, G. (2019), "Prediction of permeability and unconfined compressive strength of pervious concrete using evolved support vector regression", Construct. Build. Mater., 207, 440-449. https://doi.org/10.1016/j.conbuildmat.2019.02.117.
  55. Tinoco, J., Correia, A.G., Cortez, P. (2014), "Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns", Comput. Geotech., 55, 132-140. https://doi.org/10.1016/j.compgeo.2013.08.010.
  56. Toz, M. (2020), "Chaos-based Vortex Search algorithm for solving inverse kinematics problem of serial robot manipulators with offset wrist", Appl. Soft Comput., 89, 106074. https://doi.org/10.1016/j.asoc.2020.106074
  57. Wang, J., Meng, Q., Zou, Y., Qi, Q., Tan, K., Santamouris, M. and He, B.J. (2022a), "Performance synergism of pervious pavement on stormwater management and urban heat island mitigation: A review of its benefits, key parameters, and cobenefits approach". Water Res., 118755. https://doi.org/10.1016/j.watres.2022.118755.
  58. Wang, X., Yang, Y., Yang, R. and Liu, P. (2022b), "Experimental analysis of bearing capacity of basalt fiber reinforced concrete short columns under axial compression", Coatings, 12(5), 654. https://doi.org/10.3390/coatings12050654.
  59. 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.
  60. Wu, D., Foong, L.K. and Lyu, Z. (2020), "Two neuralmetaheuristic techniques based on vortex search and backtracking search algorithms for predicting the heating load of residential buildings", Eng. Comput., 1-14. https://doi.org/10.1007/s00366-020-01074-z.
  61. Wu, P., Liu, A., Fu, J., Ye, X. and Zhao, Y. (2022a), "Autonomous surface crack identification of concrete structures based on an improved one-stage object detection algorithm", Eng. Struct., 272, 114962. https://doi.org/10.1016/j.engstruct.2022.114962.
  62. Wu, Z., Xu, J., Chen, H., Shao, L., Zhou, X. and Wang, S. (2022b), "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.
  63. Wu, Z., Xu, J., Li, Y. and Wang, S. (2022c), "Disturbed state concept-based model for the uniaxial strain-softening behavior of fiber-reinforced soil", Int. J. Geomech., 22(7), https://doi.org/10.1061/(ASCE)GM.1943-5622.0002415.
  64. Xie, W., Xing, C., Wang, J., Guo, S., Guo, M.W. and Zhu, L.F. (2020), "Hybrid Henry Gas solubility optimization algorithm based on the Harris Hawk Optimization", IEEE Access, 8, 144665-144692. https://doi.org/10.1109/ACCESS.2020.3014309.
  65. 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.
  66. Yaseen, Z.M., Tran, M.T., Kim, S., Bakhshpoori, T. and Deo, R.C. (2018), "Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: A new approach", Eng. Struct., 177, 244-255. https://doi.org/10.1016/j.engstruct.2018.09.074.
  67. Ye, X., Moayedi, H., Khari, M. and Foong, K.L. (2020), "Metaheuristic-hybridized multilayer perceptron in slope stability analysis", Smart Struct. Syst., 26, https://doi.org/10.12989/sss.2020.26.3.263.
  68. Yeh, I.C. (1998), "Modeling of strength of high-performance concrete using artificial neural networks", Cement Concrete Res., 28(12), 1797-1808. https://doi.org/10.1016/S0008-8846(98)00165-3.
  69. 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.
  70. Zhang, C. and Ali, A. (2021), "The advancement of seismic isolation and energy dissipation mechanisms based on friction", Soil Dyn. Earthq. Eng., 146, 106746. https://doi.org/10.1016/j.soildyn.2021.106746.
  71. Zhang, J., Li, D. and Wang, Y. (2020a), "Toward intelligent construction: Prediction of mechanical properties of manufactured-sand concrete using tree-based models", J. Cleaner Product., 258, 120665. https://doi.org/10.1016/j.jclepro.2020.120665.
  72. Zhang, J., Ma, G., Huang, Y., Aslani, F. and Nener, B. (2019), "Modelling uniaxial compressive strength of lightweight selfcompacting concrete using random forest regression", Construct. Build. Mater., 210, 713-719. https://doi.org/10.1016/j.conbuildmat.2019.03.189.
  73. Zhang, J., Sun, Y., Li, G., Wang, Y., Sun, J. and Li, J. (2020b), "Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups", Eng. Comput., 1-15. https://doi.org/10.1007/s00366-020-01076-x.
  74. Zhang, J. and Wang, Y. (2020), "Evaluating the bond strength of FRP-to-concrete composite joints using metaheuristic-optimized least-squares support vector regression", Neur. Comput. Appl., 1-15. https://doi.org/10.1007/s00521-020-05191-0.
  75. Zhao, S., Hu, F., Ding, X., Zhao, M., Li, C. and Pei, S. (2017), "Dataset of tensile strength development of concrete with manufactured sand", Data Brief, 11, 469-472. https://doi.org/10.1016/j.dib.2017.02.043.
  76. 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.
  77. 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.
  78. 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.
  79. Zhao, Y., Joseph, A.J.J.M., Zhang, Z., Ma, C., Gul, D., Schellenberg, A. and Hu, N. (2020a), "Deterministic snapthrough 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.
  80. 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.
  81. Zhao, Y. and Wang, Z. (2022), "Subset simulation with adaptable intermediate failure probability for robust reliability analysis: an unsupervised learning-based approach", Struct. Multidiscipl. Optimiz., 65(6), 1-22. https://doi.org/10.1007/s00158-022-03260-7.
  82. 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.
  83. Zhao, Y., Zhong, X., 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.
  84. 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