DOI QR코드

DOI QR Code

Optimized ANNs for predicting compressive strength of high-performance concrete

  • Moayedi, Hossein (Institute of Research and Development, Duy Tan University) ;
  • Eghtesad, Amirali (Department of Engineering, Islamic Azad University Science and Research Branch) ;
  • Khajehzadeh, Mohammad (Department of Civil Engineering, Anar Branch, Islamic Azad University) ;
  • Keawsawasvong, Suraparb (Department of Civil Engineering, Thammasat School of Engineering, Thammasat University) ;
  • Al-Amidi, Mohammed M. (Information Technology Unit, Al-Mustaqbal University College) ;
  • Van, Bao Le (Institute of Research and Development, Duy Tan University)
  • 투고 : 2021.09.20
  • 심사 : 2022.09.05
  • 발행 : 2022.09.25

초록

Predicting the compressive strength of concrete (CSoC) is of high significance in civil engineering. The CSoC is a highly dependent and non-linear parameter that requires powerful models for its simulation. In this work, two novel optimization techniques, namely evaporation rate-based water cycle algorithm (ER-WCA) and equilibrium optimizer (EO) are employed for optimally finding the parameters of a multi-layer perceptron (MLP) neural processor. The efficiency of these techniques is examined by comparing the results of the ensembles to a conventionally trained MLP. It was observed that the ER-WCA and EO optimizers can enhance the training accuracy of the MLP by 11.18 and 3.12% (in terms of reducing the root mean square error), respectively. Also, the correlation of the testing results climbed from 78.80% to 82.59 and 80.71%. From there, it can be deduced that both ER-WCA-MLP and EO-MLP can be promising alternatives to the traditional approaches. Moreover, although the ER-WCA enjoys a larger accuracy, the EO was more efficient in terms of complexity, and consequently, time-effectiveness.

키워드

참고문헌

  1. Aamir M, Tolouei-Rad M, Vafadar A, Raja MNA, Giasin, K. (2020), "Performance analysis of multi-spindle drilling of Al2024 with TiN and TiCN coated drills using experimental and artificial neural networks technique", Appl. Sci., 10(23), 8633. https://doi.org/10.3390/app10238633.
  2. Abdel-Basset, M, Chang, V. and Mohamed, R. (2020), "A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems", Neural Comput. Appl., 1-34. https://doi.org/10.1007/s00521-020-04820-y.
  3. Ahmadi, M., Kheyroddin, A., Dalvand, A. and Kioumarsi, M. (2020), "New empirical approach for determining nominal shear capacity of steel fiber reinforced concrete beams", Construct. Build. Mater., 234, 117293. https://doi.org/10.1016/j.conbuildmat.2019.117293.
  4. Aitcin, P.C. (1995), "Developments in the application of highperformance concretes", Construct. Build. Mater., 9(1), 13-17. https://doi.org/10.1016/0950-0618(95)92855-B.
  5. Alweshah, M., Al-Sendah, M., Dorgham, O.M., Al-Momani, A. and Tedmori, S. (2020), "Improved water cycle algorithm with probabilistic neural network to solve classification problems", Cluster Comput., 1-16. https://doi.org/10.1007/s10586-019-03038-5.
  6. Amlashi ,AT., Abdollahi, S.M., Goodarzi, S. and Ghanizadeh, A.R. (2019), "Soft computing based formulations for slump, compressive strength, and elastic modulus of bentonite plastic concrete", J. Cleaner Product., 230, 1197-1216. https://doi.org/10.1016/j.jclepro.2019.05.168.
  7. Asteris, P.G. and Mokos, V.G. (2019), "Concrete compressive strength using artificial neural networks", Neural Comput. Appli., 1-20. https://doi.org/10.1007/s00521-019-04663-2.
  8. Bai, B., Nie, Q., Wu, H. and Hou, J. (2021), "The attachmentdetachment mechanism of ionic/nanoscale/microscale substances on quartz sand in water", Powder Technol., 394, 1158-1168. https://doi.org/10.1016/j.powtec.2021.09.051.
  9. Ban, Y., Liu, M., Wu, P., Yang, B., Liu, S., Yin, L. and Zheng, W. (2022), "Depth estimation method for monocular camera defocus images in microscopic scenes", Electronics, 11(13), https://doi.org/10.3390/electronics11132012.
  10. Behnood, A. and Golafshani, E.M. (2018), "Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves", J. Cleaner Product., 202, 54-64. https://doi.org/10.1016/j.jclepro.2018.08.065.
  11. Behnood, A. and Golafshani, E.M. (2020), "Machine learning study of the mechanical properties of concretes containing waste foundry sand", Construct. Build. Mater., 243, 118152. https://doi.org/10.1016/j.conbuildmat.2020.118152.
  12. 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.
  13. Cao, B., Zhao, J., Liu, X., Arabas, J., Tanveer, M., Singh, A.K. and Lv, Z. (2022), "Multiobjective evolution of the explainable fuzzy rough neural network with gene expression programming", IEEE Transactions on Fuzzy Systems, https://doi.org/10.1109/TFUZZ.2022.3141761.
  14. Cao, Y., Zandi, Y., Agdas, A.S., Wang, Q., Qian, X., Fu, L., Wakil, K., Selmi, A., Issakhov, A. and Roco-Videla, A. (2021), "A review study of application of artificial intelligence in construction management and composite beams", Steel Compos. Struct., 39(6), 685-700. https://doi.org/10.12989/scs.2021.39.6.685.
  15. Chen, F.X, Zhong, Y.C, Gao, X.Y., Jin, Z.Q., Wang, E.D., Zhu, F.P., Shao, X.S, 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.
  16. 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-w.
  17. Cheng, H., Liu, L. and Sun, L. (2022), "Bridging the gap between laboratory and field moduli of asphalt layer for pavement design and assessment: A comprehensive loading frequency-based approach", Front. Struct. Civil Eng., 1-14. https://doi.org/10.1007/s11709-022-0811-7.
  18. 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.
  19. Chou, J.S. and Pham, A.D. (2013), "Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength", Construct. Build. Mater., 49, 554-563. https://doi.org/10.1016/j.conbuildmat.2013.08.078.
  20. Concha, N. and Oreta, A.W. (2020), "An improved prediction model for bond strength of deformed bars in rc using upv test and artificial neural network", GEOMATE J., 18(65), 179-184. https://doi.org/10.21660/2020.65.9139.
  21. Concha, N.C. (2022), "Neural network model for bond strength of FRP bars in concrete", Structures, 306-317. https://doi.org/10.1016/j.istruc.2022.04.088.
  22. Concha, N.C. and Oreta, A.W.C. (2019), "Investigation of the effects of corrosion on bond strength of steel in concrete using neural network", Adv. Struct. Eng. Mech., Jeju, September.
  23. David, S. (1993), "The Water Cycle (John Yates, Illus)", Thomson Learning, New York.
  24. Deng, F.M., He, Y.G., Zhou, S.X., Yu, Y., Cheng, H.G. and Wu, X. (2018), "Compressive strength prediction of recycled concrete based on deep learning", Connstruct. Build. Mater., 175, 562-569. https://doi.org/10.1016/j.conbuildmat.2018.04.169.
  25. Du, Y., Qin, B., Zhao, C., Zhu, Y., Cao, J. and Ji, Y. (2021), "A novel spatio-temporal synchronization method of roadside asynchronous MMW radar-camera for sensor fusion", IEEE Transact. Intell. Transport. Syst., https://doi.org/10.1109/TITS.2021.3119079.
  26. Eskandar, H., Sadollah, A. and Bahreininejad, A. (2013), "Weight optimization of truss structures using water cycle algorithm", Iran Univ. Sci. Technol., 3(1), 115-129.
  27. 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. https://doi.org/10.1016/j.compstruc.2012.07.010.
  28. 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.
  29. 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. https://doi.org/10.1007/s00366-020-01000-3.
  30. 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.
  31. Gholampour, A., Mansouri, I., Kisi, O. and Ozbakkaloglu, T. (2020), "Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models", Neural Comput. Appl., 32(1), 295-308. https://doi.org/10.1007/s00521-018-3630-y.
  32. Golafshani, E.M. and Pazouki, G. (2018), "Predicting the compressive strength of self-compacting concrete containing fly ash using a hybrid artificial intelligence method", Comput. Concr. 22(4), 419-437. https://doi.org/10.12989/cac.2018.22.4.419.
  33. 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", Geofluid,s 2022, https://doi.org/10.1155/2022/8784398.
  34. Gupta, R., Kewalramani, M.A. and Goel, A. (2006), "Prediction of concrete strength using neural-expert system", J. Mater. Civil Eng. 18(3), 462-466. https://doi.org/10.1061/(ASCE)0899- 1561(2006)18:3(462).
  35. 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.
  36. Hasthi, V., Raja, M.N.A., Hegde, A. and Shukla, S.K. (2022), "Experimental and intelligent modelling for predicting the amplitude of footing resting on geocell-reinforced soil bed under vibratory load", Transport. Geotech., 100783. https://doi.org/10.1016/j.trgeo.2022.100783.
  37. 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.2022.128117.
  38. 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://orcid.org/0000-0001-8020-4190. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001871
  39. Huang, H., Huang, M., Zhang, W., Pospisil, S. and Wu, T. (2020), "Experimental investigation on rehabilitation of corroded RC columns with BSP and HPFL under combined loadings", J. Struct. Eng., 146(8), 04020157. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002725.
  40. 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.
  41. 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.
  42. Huang, S. and Liu, C. (2022), "A computational framework for fluid-structure interaction with applications on stability evaluation of breakwater under combined tsunami-earthquake activity", Comput. Aided Civil Infrastruct. Eng., https://doi.org/10.1111/mice.12880.
  43. Khan, M.U.A., Shukla, S.K. and Raja, M.N.A. (2021), "Soil-conduit interaction: an artificial intelligence application for reinforced concrete and corrugated steel conduits", Neural Comput. Appl., 33(21), 14861-14885. https://doi.org/10.1007/s00521-021-06125-0.
  44. Khan, M.U.A., Shukla, S.K. and Raja, M.N.A. (2022), "Loadsettlement response of a footing over buried conduit in a sloping terrain: A numerical experiment-based artificial intelligent approach". Soft Comput. 1-18. https://doi.org/10.1007/s00500-021-06628-x.
  45. Khashman, A. and Akpinar, P. (2017), "Non-destructive prediction of concrete compressive strength using neural networks", Procedia Comput. Sci., 108, 2358-2362. https://doi.org/10.1016/j.procs.2017.05.039.
  46. 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.
  47. Lei, W., Hui, Z., Xiang, L., Zelin, Z., Xu-Hui, X. and Evans, S. (2021), "Optimal remanufacturing service resource allocation for generalized growth of retired mechanical products: maximizing matching efficiency", IEEE Aaccess 9, 89655-89674. https://doi.org/10.1109/ACCESS.2021.3089896.
  48. Li, J., Cheng, F., Lin, G. and Wu, C. (2022), "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.
  49. Li, J. and Wu, Y. (2022), "Improved sparrow search algorithm with the extreme learning machine and its application for prediction", Neural Processing Lett., 1-21. https://doi.org/10.1007/s11063-022-10804-x.
  50. Li, Y., Che, P., Liu, C., Wu, D. and Du, Y. (2021a), "Cross-scene pavement distress detection by a novel transfer learning framework", Comput. Aided Civil Infrast. Eng., 36(11), 1398-1415. https://doi.org/10.1111/mice.12674.
  51. Li, Y., Zeng, X., Zhou, J., Shi, Y., Umar, H.A., Long, G. and Xie, Y. (2021b), "Development of an eco-friendly ultra-high performance concrete based on waste basalt powder for Sichuan-Tibet Railway", J. Cleaner Product., 312, 127775. https://doi.org/10.1016/j.jclepro.2021.127775.
  52. Liu, C., He, X., Deng, X., Wu, Y., Zheng, Z., Liu, J. and Hui, D. (2020), "Application of nanomaterials in ultra-high performance concrete: a review", Nanotechnol. Rev., 9(1), 1427-1444. https://doi.org/10.1515/ntrev-2020-0107.
  53. Liu, C., Wu, D., Li, Y. and Du, Y. (2021a), "Large-scale pavement roughness measurements with vehicle crowdsourced data using semi-supervised learning", Transport. Res. Part C: Emerging Technol., 125, 103048. https://doi.org/10.1016/j.trc.2021.103048.
  54. Liu, G.H. and Zheng, J. (2019), "Prediction model of compressive strength development in concrete containing four kinds of gelled materials with the artificial intelligence method", Appl. Sci.-Basel, 9(6), https://doi.org/10.3390/app9061039.
  55. Liu, Y., Zhang, Z., Liu, X., Wang, L. and Xia, X. (2021b), "Efficient image segmentation based on deep learning for mineral image classification", Adv. Powder Technol., 32(10), 3885-3903. https://doi.org/10.1016/j.apt.2021.08.038.
  56. Liu, Y., Zhang, Z., Liu, X., Wang, L. and Xia, X. (2021c), "Ore image classification based on small deep learning model: Evaluation and optimization of model depth, model structure and data size", Minerals Eng., 172, 107020. https://doi.org/10.1016/j.mineng.2021.107020.
  57. Lu, S., Ban, Y., Zhang, X., Yang, B., Liu, S., Yin, L. and Zheng, W. (2022), "Adaptive control of time delay teleoperation system with uncertain dynamics", Front. Neurorobotic., 16, 928863-928863. https://doi.org/10.3389/fnbot.2022.928863.
  58. 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.
  59. 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/s11069-021-5083-z.
  60. 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.
  61. Moayedi, H., Mehrabi, M., Bui, D.T., Pradhan, B., Foong, L.K. (2020), "Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility", J. Environ. Manage., 260, 109867. https://doi.org/10.1016/j.jenvman.2019.109867.
  62. 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", Geom., Nat. Haz. Risk, 10(1), 1879-1911. https://doi.org/10.1080/19475705.2019.1650126.
  63. 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.
  64. Naik, N.M., Mukherjee, A., Khamaru, A., Ojha, S., Kulkarni, G.S., Prakash, K. (2019), "Compressive Strength Prediction of Silica Fume mixed Concrete Soaked in Used Engine Oil with a Mathematical Model", Int. J. Eng. Manufact., 9(1), 64. https://doi.org/10.5815/ijem.2019.01.06.
  65. Neeraja, D. and Swaroop, G. (2017), "Prediction of compressive strength of concrete using artificial neural networks", Res. J. Pharmacy Technol., 10(1), 35-40. https://doi.org/10.5958/0974-360X.2017.00009.9.
  66. Nguyen, H., Mehrabi, M., Kalantar, B., Moayedi, H. and Abdullahi, M.A.M. (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.
  67. 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.
  68. Oskouei, A.V., Nazari, R. and Khaneghahi, M.H. (2020), "Laboratory and in situ investigation of the compressive strength of CFRD concrete", Construct. Build. Mater., 242, 118166. https://doi.org/10.1016/j.conbuildmat.2020.118166.
  69. Pham, A.D., Ngo, N.T., Nguyen, Q.T. and Truong, N.S. (2020), "Hybrid machine learning for predicting strength of sustainable concrete", Soft Comput., 1-16. https://doi.org/10.1007/s00500-020-04848-1.
  70. Prayogo, D., Tjong, W.F. and Tjandra, D. (2018), "Prediction of high-performance concrete strength uising a hybrid artificial intelligence approach", MATEC Web of Conferences, 1-12.
  71. Raja, M.N.A., Jaffar, S.T.A., Bardhan, A. and Shukla, S.K. (2022), "Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling", J. Rock Mech. Geotech. Eng., https://doi.org/10.1016/j.jrmge.2022.04.012
  72. Raja, M.N.A. and Shukla, S.K. (2021a), "Multivariate adaptive regression splines model for reinforced soil foundations", Geosynthetic. Int., 28(4), 368-390. https://doi.org/10.1680/jgein.20.00049.
  73. Raja, M.N.A. and Shukla, S.K. (2021b), "Predicting the settlement of geosynthetic-reinforced soil foundations using evolutionary artificial intelligence technique", Geotext. Geomemb., 49(5), 1280-1293. https://doi.org/10.1016/j.geotexmem.2021.04.007.
  74. Raja, M.N.A., Shukla, S.K. and Khan, M.U.A. (2021), "An intelligent approach for predicting the strength of geosyntheticreinforced subgrade soil", Int. J. Pavement Eng., 1-17. https://doi.org/10.1080/10298436.2021.1904237.
  75. Rajabi, A.M. and Moaf, F.O. (2017), "Simple empirical formula to estimate the main geomechanical parameters of preplaced aggregate concrete and conventional concrete", Construct. Build. Mater., 146, 485-492. https://doi.org/10.1016/j.conbuildmat.2017.04.089.
  76. Reddy, B.S.K. and Wanjari, S. (2018), "Core strength of concrete using newly developed in situ compressive testing machine", Mag. Concrete Res., 70(22), 1149-1156. https://doi.org/10.1680/jmacr.17.00315.
  77. Revanna, H. and Kumar, G. (2019), Reliability of Predicting the Compressive Strength of Concrete Based on Mathematical Models by Comparison to Experimental Results, Ph.D. Dissertation, Instytut Inzynierii Budowlanej.
  78. Sadollah, A., Eskandar, H., Bahreininejad, A. and Kim, J.H. (2015), "Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems", Appl. Soft Comput., 30, 58-71. https://doi.org/10.1016/j.asoc.2015.01.050.
  79. 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.
  80. Sadowski, L. and Nikoo, M. (2018), "Concrete compressive strength prediction using the imperialist competitive algorithm", Comput. Concr. 22(4), 355-363. https://doi.org/10.12989/cac.2018.22.4.355.
  81. 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.
  82. Shi, L., Xiao, X., Wang, X., Liang, H. and Wang, D. (2022), "Mesostructural characteristics and evaluation of asphalt mixture contact chain complex networks", Construct. Build. Mater., 340, 127753. https://doi.org/10.1016/j.conbuildmat.2022.127753.
  83. Sun, W., Lv, X. and Qiu, M. (2020), "Distributed estimation for stochastic Hamiltonian systems with fading wireless channels", IEEE Transactions Cybernetics, https://doi.org/10.1109/TCYB.2020.3023547.
  84. Taylor, K.E. (2001), "Summarizing multiple aspects of model performance in a single diagram", J. Geophys. Res.: Atmos., 106 (D7), 7183-7192. https://doi.org/10.1029/2000JD900719.
  85. 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.
  86. 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.
  87. Wang, J., Yang, M., Liang, F., Feng, K., Zhang, K. and Wang, Q. (2022b), "An algorithm for painting large objects based on a nine-axis UR5 robotic manipulator", Appl. Sci., 12(14), 7219. https://doi.org/10.3390/app12147219.
  88. Wang, X., Yang, Y., Yang, R. and Liu, P. (2022c), "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.
  89. 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.
  90. Xu, H., Wang, X.Y., Liu, C.N., Chen, J.N. and Zhang, C. (2021a), "A 3D root system morphological and mechanical model based on L-Systems and its application to estimate the shear strength of root-soil composites", Soil Tillage Res., 212, 105074. https://doi.org/10.1016/j.still.2021.105074.
  91. Xu, J., Wu, Z., Chen, H., Shao, L., Zhou, X. and Wang, S. (2021b), "Study on strength behavior of basalt fiber-reinforced loess by digital image technology (DIT) and scanning electron microscope (SEM)", Arab. J. Sci. Eng., 46(11), 11319-11338. https://doi.org/10.1007/s13369-021-05787-1.
  92. Xu, J., Zhou, L., Hu, K., Li, Y., Zhou, X. and Wang, S. (2022a), "Influence of wet-dry cycles on uniaxial compression behavior of fissured loess disturbed by vibratory loads", KSCE J. Civil Eng., 26(5), 2139-2152. https://doi.org/10.1007/s12205-022-1593-0.
  93. Xu, J., Zhou, L., Li, Y., Ding, J., Wang, S. and Cheng, W.C. (2022b), "Experimental study on uniaxial compression behavior of fissured loess before and after vibration", Int. J. Geomech., 22(2), 04021277. https://doi.org/10.1061/(ASCE)GM.1943-5622.0002259.
  94. Xu, L., Liu, X., Tong, D., Liu, Z., Yin, L. and Zheng, W. (2022c), "Forecasting urban land use change based on cellular automata and the PLUS model", Land, 11(5), 652. https://doi.org/10.3390/land11050652.
  95. 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.
  96. Yan, J., Jiao, H., Pu, W., Shi, C. Dai, J. and Liu, H. (2022), "Radar Sensor Network Resource Allocation for Fused Target Tracking: A Brief Review", Inform. Fusion https://doi.org/10.1016/j.inffus.2022.06.009.
  97. Yaseen, Z.M., Deo, R.C., Hilal, A., Abd, A.M., Bueno, L.C., Salcedo-Sanz, S., Nehdi, M.L. (2018), "Predicting compressive strength of lightweight foamed concrete using extreme learning machine model", Adv. Eng. Softw. 115, 112-125. https://doi.org/10.1016/j.advengsoft.2017.09.004.
  98. 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.
  99. Yin, H., Liu, S., Lu, S., Nie, W, and Jia, B. (2021), "Prediction of the compressive and tensile strength of HPC concrete with fly ash and micro-silica using hybrid algorithms", Adv. Concrete Construct., 12(4), 339-354. https://doi.org/10.12989/acc.2021.12.4.339.
  100. Yu, Y., Li, W., Li, J. and Nguyen, T.N. (2018), "A novel optimised self-learning method for compressive strength prediction of high performance concrete", Construct. Build. Mater., 184, 229-247. https://doi.org/10.1016/j.conbuildmat.2018.06.219.
  101. 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.
  102. Zhang, C., Mousavi, A.A., Masri, S.F., Gholipour, G., Yan, K., Li, X. (2022), "Vibration feature extraction using signal processing techniques for structural health monitoring: A review", Mech. Syst. Sig. Processing, 177, 109175. https://doi.org/10.1016/j.ymssp.2022.109175.
  103. Zhao, C., Liao, F., Li, X. and Du, Y. (2021a), "Macroscopic modeling and dynamic control of on-street cruising-for-parking of autonomous vehicles in a multi-region urban road network", Transport. Res. Part C: Emerg. Technol., 128, 103176. https://doi.org/10.1016/j.trc.2021.103176.
  104. Zhao, Y., 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.
  105. Zhao, Y., Hu, H., Bai, L., Tang, M., Chen, H. and Su, D. (2021b), "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.
  106. 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.
  107. Zhao, Y., Joseph, A.J.J.M., Zhang, Z., Ma, C., Gul, D. and 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.
  108. 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.
  109. 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.
  110. 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.
  111. Zhao, Y., Zhong, X., Foong, L.K. (2021c), "Predicting the splitting tensile strength of concrete using an equilibrium optimization model", Steel Compos. Struct., 39(1), 81-93. http://dx.doi.org/10.12989/scs.2021.39.1.081.
  112. Zhu, P., Brunner, S., Zhao, S., Griffa, M., Leemann, A., Toropovs, N., Malekos, A., Koebel, M.M. and Lura, P. (2019), "Study of physical properties and microstructure of aerogel-cement mortars for improving the fire safety of high-performance concrete linings in tunnels", Cement Concrete Compos., 104, 103414. https://doi.org/10.1016/j.cemconcomp.2019.103414.
  113. 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.