Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Efficient metaheuristic-retrofitted techniques for concrete slump simulation

  • Zhao, Yinghao (Guangzhou Institute of Building Science Co., Ltd.) ;
  • Bai, Chengzong (School of Civil Engineering and Transportation, South China University of Technology) ;
  • Xu, Chengyong (Institute of Research and Development, Duy Tan University) ;
  • Foong, Loke Kok (Institute of Research and Development, Duy Tan University)
  • Received : 2020.08.28
  • Accepted : 2021.01.02
  • Published : 2021.05.25
⟨ Previous Next ⟩

Abstract

Due to the benefits of the early prediction of concrete slump, introducing an efficient model for this purpose is of great importance. Considering this motivation, four strong metaheuristic algorithms, namely electromagnetic field optimization (EFO), water cycle algorithm (WCA), teaching-learning-based optimization (TLBO), and multi-tracker optimization algorithm (MTOA) are used to supervise a neural predictive system in analyzing the slump pattern. This supervision protects the network against computational issues like pre-mature convergence. The overall results (e.g., Pearson correlation indicator larger than 0.839 and 0.807 for the training and testing data, respectively) revealed the competency of the proposed models. However, investigating the rankings of the models pointed out the superiority of the WCA (MAEtrain = 3.3080 vs. 3.7821, 3.5782, and 3.6851; and MAEtest = 3.8443 vs. 4.0326, 4.1417, and 4.0871 obtained for the EFO, TLBO, and MTOA, respectively). Moreover, the high efficiency of the EFO in terms of model complexity and convergence rate, as well as the adequate accuracy of prediction, demonstrated the suitability of the corresponding ensemble. Therefore, the neural systems trained by these two algorithms (i.e., the WCA and EFO) are efficient slump evaluative models and can give an optimal design of the concrete mixture for any desirable slump.

Keywords

References

  1. Abedini, M. and Zhang, C. (2020), "Performance assessment of concrete and steel material models in ls-dyna for enhanced numerical simulation, a state of the art review", Arch. Computat. Methods Eng., 1-22. https://doi.org/10.1007/s11831-020-09483-5
  2. Abedini, M., Mutalib, A.A., Zhang, C., Mehrmashhadi, J., Raman, S.N., Alipour, R., Momeni, T. and Mussa, M.H. (2020), "Large deflection behavior effect in reinforced concrete columns exposed to extreme dynamic loads", Frontiers Struct. Civil Eng., 14(2), 532-553. https://doi.org/10.1007/s11709-020-0604-9
  3. Abedinpourshotorban, H., Shamsuddin, S.M., Beheshti, Z. and Jawawi, D.N. (2016), "Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm", Swarm Evolution. Computat., 26, 8-22. https://doi.org/10.1016/j.swevo.2015.07.002
  4. Abellan Garcia, J., Fernandez Gomez, J. and Torres Castellanos, N. (2020), "Properties prediction of environmentally friendly ultra-high-performance concrete using artificial neural networks", Eur. J. Environ. Civil Eng., 1-25. https://doi.org/10.1080/19648189.2020.1762749
  5. Adeli, H. (2001), "Neural networks in civil engineering: 1989- 2000", Comput.-Aided Civil Infrastruct. Eng., 16(2), 126-142. https://doi.org/10.1111/0885-9507.00219
  6. Amlashi, A.T., 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. Anderson, D. and McNeill, G. (1992), "Artificial neural networks technology", Kaman Sci. Corp., 258(6), 1-83.
  8. Aydogmus, H.Y., Erdal, H.I., Karakurt, O., Namli, E., Turkan, Y.S. and Erdal, H. (2015), "A comparative assessment of bagging ensemble models for modeling concrete slump flow", Comput. Concrete, Int. J., 16(5), 741-757. https://doi.org/10.12989/cac.2015.16.5.741
  9. Azimi-Pour, M., Eskandari-Naddaf, H. and Pakzad, A. (2020), "Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete", Constr. Build. Mater., 230, 117021. https://doi.org/10.1016/j.conbuildmat.2019.117021
  10. Bao, J., Li, S., Zhang, P., Ding, X., Xue, S., Cui, Y. and Zhao, T. (2020), "Influence of the incorporation of recycled coarse aggregate on water absorption and chloride penetration into concrete", Constr. Build. Mater., 239, 117845. https://doi.org/10.1016/j.conbuildmat.2019.117845
  11. Behforouz, B., Memarzadeh, P., Eftekhar, M. and Fathi, F. (2020), "Regression and ANN models for durability and mechanical characteristics of waste ceramic powder high performance sustainable concrete", Comput. Concrete, Int. J., 25(2), 119-132. https://doi.org/10.12989/cac.2020.25.2.119
  12. Bui, D.K., Nguyen, T., Chou, J.S., Nguyen-Xuan, H. and Ngo, T.D. (2018), "A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete", Constr. Build. Mater., 180, 320-333. https://doi.org/10.1016/j.conbuildmat.2018.05.201
  13. Bui, D.T., Ghareh, S., Moayedi, H. and Nguyen, H. (2019a), "Fine-tuning 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
  14. Bui, D.T., Moayedi, H., Kalantar, B., Osouli, A., Pradhan, B., Nguyen, H. and Rashid, A.S.A. (2019b), "A novel swarm intelligence-Harris hawks optimization for spatial assessment of landslide susceptibility", Sensors, 19(16), 3590. https://doi.org/10.3390/s19163590
  15. Cao, B., Zhao, J., Lv, Z., Gu, Y., Yang, P. and Halgamuge, S.K. (2020a), "Multiobjective evolution of fuzzy rough neural network via distributed parallelism for stock prediction", IEEE Transact. Fuzzy Syst., 28(5), 939-952. https://doi.org/10.1109/TFUZZ.2020.2972207
  16. Cao, Y., Li, Y., Zhang, G., Jermsittiparsert, K. and Nasseri, M. (2020b), "An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm", Energy Reports, 6, 530-542. https://doi.org/10.1016/j.egyr.2020.02.035
  17. Chandwani, V., Agrawal, V. and Nagar, R. (2015), "Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks", Expert Syst. Applicat., 42(2), 885-893. https://doi.org/10.1016/j.eswa.2014.08.048
  18. Chao, L., Zhang, K., Li, Z., Zhu, Y., Wang, J. and Yu, Z. (2018), "Geographically weighted regression based methods for merging satellite and gauge precipitation", J. Hydrol., 558, 275-289. https://doi.org/10.1016/j.jhydrol.2018.01.042
  19. Chao, M., Kai, C. and Zhiwei, Z. (2020), "Research on tobacco foreign body detection device based on machine vision", Transact. Inst. Measure. Control, 42(15), 2857-2871. https://doi.org/10.1177/0142331220929816
  20. Chen, H.L., Wang, G., Ma, C., Cai, Z.N., Liu, W.B. and Wang, S.J. (2016), "An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease", Neurocomputing, 184, 131-144. https://doi.org/10.1016/j.neucom.2015.07.138
  21. Chen, Y., He, L., Guan, Y., Lu, H. and Li, J. (2017), "Life cycle assessment of greenhouse gas emissions and water-energy optimization for shale gas supply chain planning based on multi-level approach: Case study in Barnett, Marcellus, Fayetteville, and Haynesville shales", Energy Convers. Manage., 134, 382-398. https://doi.org/10.1016/j.enconman.2016.12.019
  22. Chen, Y., He, L., Li, J. and Zhang, S. (2018), "Multi-criteria design of shale-gas-water supply chains and production systems towards optimal life cycle economics and greenhouse gas emissions under uncertainty", Comput. Chem. Eng., 109, 216-235. https://doi.org/10.1016/j.compchemeng.2017.11.014
  23. Chen, H., Chen, A., Xu, L., Xie, H., Qiao, H., Lin, Q. and Cai, K. (2020a), "A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources", Agricult. Water Manage., 240, 106303. https://doi.org/10.1016/j.agwat.2020.106303
  24. Chen, H., Fan, D.L., Fang, L., Huang, W., Huang, J., Cao, C., Yang, L., He, Y. and Zeng, L. (2020b), "Particle swarm optimization algorithm with mutation operator for particle filter noise reduction in mechanical fault diagnosis", Int. J. Pattern Recogn. Artif. Intell., 34(10), 2058012. https://doi.org/10.1142/S0218001420580124
  25. Chen, H., Heidari, A.A., Chen, H., Wang, M., Pan, Z. and Gandomi, A.H. (2020c), "Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies", Future Generat. Comput. Syst., 111, 175-198. https://doi.org/10.1016/j.future.2020.04.008
  26. Chen, Y., Li, J., Lu, H. and Yan, P. (2021), "Coupling system dynamics analysis and risk aversion programming for optimizing the mixed noise-driven shale gas-water supply chains", J. Cleaner Product., 278, 123209. https://doi.org/10.1016/j.jclepro.2020.123209
  27. Cheng, X., He, L., Lu, H., Chen, Y. and Ren, L. (2016), "Optimal water resources management and system benefit for the Marcellus shale-gas reservoir in Pennsylvania and West Virginia", J. Hydrol., 540, 412-422. https://doi.org/10.1016/j.jhydrol.2016.06.041
  28. Dao, D.V., Trinh, S.H., Ly, H.B. and Pham, B.T. (2019), "Prediction of compressive strength of geopolymer concrete using entirely steel slag aggregates: Novel hybrid artificial intelligence approaches", Appl. Sci., 9(6), 1113. https://doi.org/10.3390/app9061113
  29. David, S. (1993), The Water Cycle, (John Yates, Illus), Thomson Learning, New York, USA.
  30. Deng, Y., Zhang, T., Sharma, B.K. and Nie, H. (2019), "Optimization and mechanism studies on cell disruption and phosphorus recovery from microalgae with magnesium modified hydrochar in assisted hydrothermal system", Sci. Total Environ., 646, 1140-1154. https://doi.org/10.1016/j.scitotenv.2018.07.369
  31. Douma, O.B., Boukhatem, B., Ghrici, M. and Tagnit-Hamou, A. (2017), "Prediction of properties of self-compacting concrete containing fly ash using artificial neural network", Neural Comput. Applicat., 28(1), 707-718. https://doi.org/10.1007/s00521-016-2368-7
  32. 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
  33. Feng, S., Lu, H., Tian, P., Xue, Y., Lu, J., Tang, M. and Feng, W. (2020a), "Analysis of microplastics in a remote region of the Tibetan Plateau: Implications for natural environmental response to human activities", Sci. Total Environ., 739, 140087. https://doi.org/10.1016/j.scitotenv.2020.140087
  34. Feng, W., Lu, H., Yao, T. and Yu, Q. (2020b), "Drought characteristics and its elevation dependence in the Qinghai-Tibet plateau during the last half-century", Scientific Reports, 10(1), 14323. https://doi.org/10.1038/s41598-020-71295-1
  35. 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
  36. Fu, X., Fortino, G., Pace, P., Aloi, G. and Li, W. (2020a), "Environment-fusion multipath routing protocol for wireless sensor networks", Inform. Fusion, 53, 4-19. https://doi.org/10.1016/j.inffus.2019.06.001
  37. Fu, X., Pace, P., Aloi, G., Yang, L. and Fortino, G. (2020b), "Topology optimization against cascading failures on wireless sensor networks using a memetic algorithm", Comput. Networks, 177, 107327. https://doi.org/10.1016/j.comnet.2020.107327
  38. Ghaemi, A., Rezaie-Balf, M., Adamowski, J., Kisi, O. and Quilty, J. (2019), "On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction", Agricult. Forest Meteorol., 278, 107647. https://doi.org/10.1016/j.agrformet.2019.107647
  39. Gholipour, G., Zhang, C. and Mousavi, A.A. (2020), "Numerical analysis of axially loaded RC columns subjected to the combination of impact and blast loads", Eng. Struct., 219, 110924. https://doi.org/10.1016/j.engstruct.2020.110924
  40. Hammoudi, A., Moussaceb, K., Belebchouche, C. and Dahmoune, F. (2019), "Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates", Constr. Build. Mater., 209, 425-436. https://doi.org/10.1016/j.conbuildmat.2019.03.119
  41. Han, X., Zhang, D., Yan, J., Zhao, S. and Liu, J. (2020), "Process development of flue gas desulphurization wastewater treatment in coal-fired power plants towards zero liquid discharge: Energetic, economic and environmental analyses", J. Cleaner Product., 261, 121144. https://doi.org/10.1016/j.jclepro.2020.121144
  42. He, L., Chen, Y. and Li, J. (2018a), "A three-level framework for balancing the tradeoffs among the energy, water, and air-emission implications within the life-cycle shale gas supply chains", Resourc. Conserv. Recycl., 133, 206-228. https://doi.org/10.1016/j.resconrec.2018.02.015
  43. He, L., Chen, Y., Zhao, H., Tian, P., Xue, Y. and Chen, L. (2018b), "Game-based analysis of energy-water nexus for identifying environmental impacts during Shale gas operations under stochastic input", Sci. Total Environ., 627, 1585-1601. https://doi.org/10.1016/j.scitotenv.2018.02.004
  44. He, L., Shen, J. and Zhang, Y. (2018c), "Ecological vulnerability assessment for ecological conservation and environmental management", J. Environ. Manage., 206, 1115-1125. https://doi.org/10.1016/j.jenvman.2017.11.059
  45. He, L., Shao, F. and Ren, L. (2020), "Sustainability appraisal of desired contaminated groundwater remediation strategies: an information-entropy-based stochastic multi-criteria preference model", Environ. Develop. Sustain., 23(2), 1759-1779. https://doi.org/10.1007/s10668-020-00650-z
  46. Hecht-Nielsen, R. (1992), Neural Networks for Perception, Elsevier, pp. 65-93.
  47. Heidari, A.A., Abbaspour, R.A. and Jordehi, A.R. (2017), "An efficient chaotic water cycle algorithm for optimization tasks", Neural Computing and Applications, 28(1), 57-85. https://doi.org/10.1007/s00521-015-2037-2
  48. Hornik, K., Stinchcombe, M. and White, H. (1989), "Multilayer feedforward networks are universal approximators", Neural Networks, 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  49. Hu, L., Hong, G., Ma, J., Wang, X. and Chen, H. (2015), "An efficient machine learning approach for diagnosis of paraquat-poisoned patients", Comput. Biol. Med., 59, 116-124. https://doi.org/10.1016/j.compbiomed.2015.02.003
  50. Hu, X., Chong, H.Y. and Wang, X. (2019), "Sustainability perceptions of off-site manufacturing stakeholders in Australia", J. Cleaner Product., 227, 346-354. https://doi.org/10.1016/j.jclepro.2019.03.258
  51. Jia, L., Liu, B., Zhao, Y., Chen, W., Mou, D., Fu, J., Wang, Y., Xin, W. and Zhao, L. (2020), "Structure design of MoS2@Mo2C on nitrogen-doped carbon for enhanced alkaline hydrogen evolution reaction", J. Mater. Sci., 55(34), 16197-16210. https://doi.org/10.1007/s10853-020-05107-2
  52. Keshtegar, B., Heddam, S., Sebbar, A., Zhu, S.P. and Trung, N.T. (2019), "SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporation", Environ. Sci. Pollut. Res., 26(35), 35807-35826. https://doi.org/10.1007/s11356-019-06596-8
  53. Kisi, O. and Heddam, S. (2019), "Evaporation modelling by heuristic regression approaches using only temperature data", Hydrol. Sci. J., 64(6), 653-672. https://doi.org/10.1080/02626667.2019.1599487
  54. Lei, Z., Hao, S., Yang, J. and Dan, X. (2020), "Study on solid waste pyrolysis coke catalyst for catalytic cracking of coal tar", Int. J. Hydrog. Energy, 45(38), 19280-19290. https://doi.org/10.1016/j.ijhydene.2020.05.075
  55. Li, C., Hou, L., Sharma, B.Y., Li, H., Chen, C., Li, Y., Zhao, X., Huang, H., Cai, Z. and Chen, H. (2018), "Developing a new intelligent system for the diagnosis of tuberculous pleural effusion", Comput. Methods Programs Biomed., 153, 211-225. https://doi.org/10.1016/j.cmpb.2017.10.022
  56. Li, T., Xu, M., Zhu, C., Yang, R., Wang, Z. and Guan, Z. (2019), "A deep learning approach for multi-frame in-loop filter of HEVC", IEEE Transact. Image Process., 28(11), 5663-5678. https://doi.org/10.1109/TIP.2019.2921877
  57. Li, X., Zhang, R., Zhang, X., Zhu, P. and Yao, T. (2020a), "Silver-Catalyzed Decarboxylative Allylation of Difluoroarylacetic Acids with Allyl Sulfones in Water", Chemistry - An Asian J., 15(7), 1175-1179. https://doi.org/10.1002/asia.202000059
  58. Li, Z., Zhou, H., Hu, D. and Zhang, C. (2020b), "Yield criterion for rocklike geomaterials based on strain energy and CMP model", Int. J. Geomech., 20(3), 04020013. https://doi.org/10.1061/(ASCE)GM.1943-5622.0001593
  59. Liu, Z., Shao, J., Xu, W., Chen, H. and Zhang, Y. (2014), "An extreme learning machine approach for slope stability evaluation and prediction", Natural Hazards, 73(2), 787-804. https://doi.org/10.1007/s11069-014-1106-7
  60. Liu, J., Wu, C., Wu, G. and Wang, X. (2015), "A novel differential search algorithm and applications for structure design", Appl. Mathe. Computat., 268, 246-269. https://doi.org/10.1016/j.amc.2015.06.036
  61. Liu, D., Wang, S., Huang, D., Deng, G., Zeng, F. and Chen, H. (2016a), "Medical image classification using spatial adjacent histogram based on adaptive local binary patterns", Comput. Biol. Med., 72, 185-200. https://doi.org/10.1016/j.compbiomed.2016.03.010
  62. Liu, S., Chan, F.T. and Ran, W. (2016b), "Decision making for the selection of cloud vendor: An improved approach under group decision-making with integrated weights and objective/subjective attributes", Expert Syst. Applicat., 55, 37-47. https://doi.org/10.1016/j.eswa.2016.01.059
  63. Liu, L., Li, J., Yue, F., Yan, X., Wang, F., Bloszies, S. and Wang, Y. (2018), "Effects of arbuscular mycorrhizal inoculation and biochar amendment on maize growth, cadmium uptake and soil cadmium speciation in Cd-contaminated soil", Chemosphere, 194, 495-503. https://doi.org/10.1016/j.chemosphere.2017.12.025
  64. Liu, E., Lv, L., Yi, Y. and Xie, P. (2019), "Research on the steady operation optimization model of natural gas pipeline considering the combined operation of air coolers and compressors", IEEE Access, 7, 83251-83265. https://doi.org/10.1109/ACCESS.2019.2924515
  65. Liu, C., Huang, X., Wu, Y.Y., Deng, X., Liu, J., Zheng, Z. and Hui, D. (2020a), "Review on the research progress of cement-based and geopolymer materials modified by graphene and graphene oxide", Nanotechnol. Rev., 9(1), 155-169. https://doi.org/10.1515/ntrev-2020-0014
  66. Liu, J., Liu, Y. and Wang, X. (2020b), "An environmental assessment model of construction and demolition waste based on system dynamics: a case study in Guangzhou", Environ. Sci. Pollut. Res., 27(30), 37237-37259. https://doi.org/10.1007/s11356-019-07107-5
  67. Liu, Y., Yang, C. and Sun, Q. (2020c), "Thresholds based image extraction schemes in big data environment in intelligent traffic management", IEEE Transact. Intell. Transport. Syst., 1-9. https://doi.org/10.1109/TITS.2020.2994386
  68. Long, Q., Wu, C. and Wang, X. (2015), "A system of nonsmooth equations solver based upon subgradient method", Appl. Mathe. Computat., 251, 284-299. https://doi.org/10.1016/j.amc.2014.11.064
  69. Lu, H., Tian, P. and He, L. (2019), "Evaluating the global potential of aquifer thermal energy storage and determining the potential worldwide hotspots driven by socio-economic, geo-hydrologic and climatic conditions", Renew. Sustain. Energy Rev., 112, 788-796. https://doi.org/10.1016/j.rser.2019.06.013
  70. Luo, Q., Wen, C., Qiao, S. and Zhou, Y. (2016), "Dual-system water cycle algorithm for constrained engineering optimization problems", Proceedings of International Conference on Intelligent Computing, pp. 730-741.
  71. Lv, Z. and Qiao, L. (2020), "Deep belief network and linear perceptron based cognitive computing for collaborative robots", Appl. Soft Comput., 92, 106300. https://doi.org/10.1016/j.asoc.2020.106300
  72. Lyu, Z., Chai, J., Xu, Z., Qin, Y. and Cao, J. (2019), "A comprehensive review on reasons for tailings dam failures based on case history", Adv. Civil Eng., 4159306. https://doi.org/10.1155/2019/4159306
  73. 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., Int. J., 26(2), 241-251. https://doi.org/10.12989/sss.2020.26.2.241
  74. McCulloch, W.S. and Pitts, W. (1943), "A logical calculus of the ideas immanent in nervous activity", Bull. Mathe. Biophys., 5(4), 115-133. https://doi.org/10.1007/BF02478259
  75. Meesaraganda, L.P., Sarkar, N. and Tarafder, N. (2020), Soft Computing for Problem Solving, Springer, pp. 119-134.
  76. Mirjalili, S. (2015), "The ant lion optimizer", Adv. Eng. Software, 83, 80-98. https://doi.org/10.1016/j.advengsoft.2015.01.010
  77. 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
  78. 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
  79. More, J.J. (1978), Numerical Analysis, Springer, pp. 105-116.
  80. Mou, B., Li, X., Bai, Y. and Wang, L. (2019a), "Shear behavior of panel zones in steel beam-to-column connections with unequal depth of outer annular stiffener", J. Struct. Eng., 145(2), 04018247. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002256
  81. Mou, B., Li, X., Qiao, Q., He, B. and Wu, M. (2019b), "Seismic behaviour of the corner joints of a frame under biaxial cyclic loading", Eng. Struct., 196, 109316. https://doi.org/10.1016/j.engstruct.2019.109316
  82. Mou, B., Zhao, F., Qiao, Q., Wang, L., Li, H., He, B. and Hao, Z. (2019c), "Flexural behavior of beam to column joints with or without an overlying concrete slab", Eng. Struct, 199, 109616. https://doi.org/10.1016/j.engstruct.2019.109616
  83. Nguyen, C.H., Tran, L.H. and Ho, K.N. (2020), CIGOS 2019, Innovation for Sustainable Infrastructure, Springer, pp. 1161-1166.
  84. Pang, R., Xu, B., Kong, X. and Zou, D. (2018), "Seismic fragility for high CFRDs based on deformation and damage index through incremental dynamic analysis", Soil Dyn. Earthq. Eng., 104, 432-436. https://doi.org/10.1016/j.soildyn.2017.11.017
  85. Pang, R., Xu, B., Zhou, Y., Zhang, X. and Wang, X. (2020), "Fragility analysis of high CFRDs subjected to mainshock-aftershock sequences based on plastic failure", Eng. Struct., 206, 110152. https://doi.org/10.1016/j.engstruct.2019.110152
  86. Pham, A.D., Hoang, N.D. and Nguyen, Q.T. (2016), "Predicting compressive strength of high-performance concrete using metaheuristic-optimized least squares support vector regression", J. Comput. Civil Eng., 30(3), 06015002. https://doi.org/10.1061/%28ASCE%29CP.1943-5487.0000506
  87. Piotrowski, A.P., Osuch, M., Napiorkowski, M.J., Rowinski, P.M. and Napiorkowski, J.J. (2014), "Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river", Comput. Geosci., 64, 136-151. https://doi.org/10.1016/j.cageo.2013.12.013
  88. Qian, J., Feng, S., Li, Y., Tao, T., Han, J., Chen, Q. and Zuo, C. (2020a), "Single-shot absolute 3D shape measurement with deep-learning-based color fringe projection profilometry", Optics Letters, 45(7), 1842-1845. https://doi.org/10.1364/OL.388994
  89. Qian, J., Feng, S., Tao, T., Hu, Y., Li, Y., Chen, Q. and Zuo, C. (2020b), "Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement", APL Photonics, 5(4), 046105. https://doi.org/10.1063/5.0003217
  90. Qiu, T., Shi, X., Wang, J., Li, Y., Qu, S., Cheng, Q., Cui, T. and Sui, S. (2019), "Deep learning: a rapid and efficient route to automatic metasurface design", Adv. Sci., 6(12), 1900128. https://doi.org/10.1002/advs.201900128
  91. Quan, Q., Hao, Z., Xifeng, H. and Jingchun, L. (2020), "Research on water temperature prediction based on improved support vector regression", Neural Comput. Applicat., 1-10. https://doi.org/10.1007/s00521-020-04836-4
  92. Rao, R.V., Savsani, V.J. and Vakharia, D.P. (2011), "Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems", Comput.-Aided Des., 43(3), 303-315. https://doi.org/10.1016/j.cad.2010.12.015
  93. Rao, C.S., Pavan, K.K. and Rao, A.A. (2013), "An automatic medical image segmentation using teaching learning based optimization", Proceedings of International Conference on Advances in Computer Science.
  94. Roy, D.K., Barzegar, R., Quilty, J. and Adamowski, J. (2020), "Using ensembles of adaptive neuro-fuzzy inference system and optimization algorithms to predict reference evapotranspiration in subtropical climatic zones", J. Hydrol., 591, 125509. https://doi.org/10.1016/j.jhydrol.2020.125509
  95. Sadowski, L., Nikoo, M. and Nikoo, M. (2018), "Concrete compressive strength prediction using the imperialist competitive algorithm", Comput. Concrete, Int. J., 22(4), 355-363. https://doi.org/10.12989/cac.2018.22.4.355
  96. 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
  97. Saha, S. and Rajasekaran, C. (2016), "Mechanical properties of recycled aggregate concrete produced with Portland Pozzolana Cement", Adv. Concrete Constr., Int. J., 4(1), 27-35. https://doi.org/10.12989/acc.2016.4.1.027
  98. Saremi, S., Mirjalili, S. and Lewis, A. (2017), "Grasshopper optimisation algorithm: theory and application", Adv. Eng. Software, 105, 30-47. https://doi.org/10.1016/j.advengsoft.2017.01.004
  99. Shahmansouri, A.A., Yazdani, M., Ghanbari, S., Bengar, H.A., Jafari, A. and Ghatte, H.F. (2020), "Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite", J. Cleaner Product., 279, 123697. https://doi.org/10.1016/j.jclepro.2020.123697
  100. Shen, L., Chen, H., Yu, Z., Kang, W., Zhang, B., Li, H., Yang, B. and Liu, D. (2016), "Evolving support vector machines using fruit fly optimization for medical data classification", Knowledge-Based Syst., 96, 61-75. https://doi.org/10.1016/j.knosys.2016.01.002
  101. Shi, K., Wang, J., Tang, Y. and Zhong, S. (2020a), "Reliable asynchronous sampled-data filtering of T-S fuzzy uncertain delayed neural networks with stochastic switched topologies", Fuzzy Sets Syst., 381, 1-25. https://doi.org/10.1016/j.fss.2018.11.017
  102. Shi, K., Wang, J., Zhong, S., Tang, Y. and Cheng, J. (2020b), "Non-fragile memory filtering of T-S fuzzy delayed neural networks based on switched fuzzy sampled-data control", Fuzzy Sets Syst., 394, 40-64. https://doi.org/10.1016/j.fss.2019.09.001
  103. Shukla, A.K., Singh, P. and Vardhan, M. (2020), "An adaptive inertia weight teaching-learning-based optimization algorithm and its applications", Appl. Mathe. Modell., 77, 309-326. https://doi.org/10.1016/j.apm.2019.07.046
  104. Simon, D. (2008), "Biogeography-based optimization", IEEE Transact. Evolution. Computat., 12(6), 702-713. https://doi.org/10.1109/TEVC.2008.919004
  105. Singh, V., Gu, N. and Wang, X. (2011), "A theoretical framework of a BIM-based multi-disciplinary collaboration platform", Automat. Constr., 20(2), 134-144. https://doi.org/10.1016/j.autcon.2010.09.011
  106. Su, Z., Liu, E., Xu, Y., Xie, P., Shang, C. and Zhu, Q. (2019), "Flow field and noise characteristics of manifold in natural gas transportation station", Oil & Gas Science and Technology-Revue d'IFP Energies Nouvelles, 74, 70. https://doi.org/10.2516/ogst/2019038
  107. Sun, Y., Wang, J., Wu, J., Shi, W., Ji, D., Wang, X. and Zhao, X. (2020), "Constraints hindering the development of high-rise modular buildings", Appl. Sci., 10(20), 7159. https://doi.org/10.3390/app10207159
  108. Talebi, B. and Dehkordi, M.N. (2018), "Sensitive association rules hiding using electromagnetic field optimization algorithm", Expert Syst. Applicat., 114, 155-172. https://doi.org/10.1016/j.eswa.2018.07.031
  109. Tian, P., Lu, H., Feng, W., Guan, Y. and Xue, Y. (2020), "Large decrease in streamflow and sediment load of Qinghai-Tibetan Plateau driven by future climate change: A case study in Lhasa River Basin", Catena, 187, 104340. https://doi.org/10.1016/j.catena.2019.104340
  110. Unlu, R. (2020), "An assessment of machine learning models for slump flow and examining redundant features", Comput. Concrete, Int. J., 25(6), 565-574. https://doi.org/10.12989/cac.2020.25.6.565
  111. Vakhshouri, B. and Nejadi, S. (2018), "Prediction of compressive strength of self-compacting concrete by ANFIS models", Neurocomputing, 280, 13-22. https://doi.org/10.1016/j.neucom.2017.09.099
  112. Wang, M. and Chen, H. (2020), "Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis", Appl. Soft Comput. J., 88, 105946. https://doi.org/10.1016/j.asoc.2019.105946
  113. Wang, S.J., Chen, H.L., Yan, W.J., Chen, Y.H. and Fu, X. (2014), "Face recognition and micro-expression recognition based on discriminant tensor subspace analysis plus extreme learning machine", Neural Processing Letters, 39(1), 25-43. https://doi.org/10.1007/s11063-013-9288-7
  114. Wang, M., Chen, H., Yang, B., Zhao, X., Hu, L., Cai, Z., Huang, H. and Tong, C. (2017), "Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses", Neurocomputing, 267, 69-84. https://doi.org/10.1016/j.neucom.2017.04.060
  115. Wang, S., Zhang, K., van Beek, L.P., Tian, X. and Bogaard, T.A. (2020a), "Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment", Environ. Modell. Software, 124, 104607 https://doi.org/10.1016/j.envsoft.2019.104607
  116. Wang, Y., Yao, M., Ma, R., Yuan, Q., Yang, D., Cui, B., Ma, C., Liu, M. and Hu, D. (2020b), "Design strategy of barium titanate/polyvinylidene fluoride-based nanocomposite films for high energy storage", J. Mater. Chem. A, 8(3), 884-917. https://doi.org/10.1039/C9TA11527G
  117. Wu, C., Wang, X., Chen, M. and Kim, M.J. (2019), "Differential received signal strength based RFID positioning for construction equipment tracking", Adv. Eng. Inform., 42, 100960. https://doi.org/10.1016/j.aei.2019.100960
  118. Wu, T., Xiong, L., Cheng, J. and Xie, X. (2020), "New results on stabilization analysis for fuzzy semi-Markov jump chaotic systems with state quantized sampled-data controller", Inform. Sci., 521, 231-250. https://doi.org/10.1016/j.ins.2020.02.051
  119. Xia, J., Chen, H., Li, Q., Zhou, M., Chen, L., Cai, Z., Fang, Y. and Zhou, H. (2017), "Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach", Comput. Methods Programs Biomed., 147, 37-49. https://doi.org/10.1016/j.cmpb.2017.06.005
  120. Xiong, Q., Zhang, X., Wang, W.F. and Gu, Y. (2020), "A parallel algorithm framework for feature extraction of EEG signals on MPI", Computat. Mathe. Methods Med., 2020. https://doi.org/10.1155/2020/9812019
  121. Xu, X. and Chen, H.L. (2014), "Adaptive computational chemotaxis based on field in bacterial foraging optimization", Soft Comput., 18(4), 797-807. https://doi.org/10.1007/s00500-013-1089-4
  122. Xu, M., Li, T., Wang, Z., Deng, X., Yang, R. and Guan, Z. (2018), "Reducing complexity of HEVC: A deep learning approach", IEEE Transact. Image Process., 27(10), 5044-5059. https://doi.org/10.1109/TIP.2018.2847035
  123. Xu, Y., Chen, H., Luo, J., Zhang, Q., Jiao, S. and Zhang, X. (2019), "Enhanced Moth-flame optimizer with mutation strategy for global optimization", Inform. Sci., 492, 181-203. https://doi.org/10.1016/j.ins.2019.04.022
  124. Xu, B., Pang, R. and Zhou, Y. (2020a), "Verification of stochastic seismic analysis method and seismic performance evaluation based on multi-indices for high CFRDs", Eng. Geol, 264, 105412. https://doi.org/10.1016/j.enggeo.2019.105412
  125. Xu, M., Li, C., Zhang, S. and Le Callet, P. (2020b), "State-of-the-art in 360 video/image processing: Perception, assessment and compression", IEEE J. Select. Topics Signal Process., 14(1), 5-26. https://doi.org/10.1109/JSTSP.2020.2966864
  126. Yaman, M.A., Abd Elaty, M. and Taman, M. (2017), "Predicting the ingredients of self compacting concrete using artificial neural network", Alexandria Eng. J., 56(4), 523-532. https://doi.org/10.1016/j.aej.2017.04.007
  127. Yan, J., Pu, W., Zhou, S., Liu, H. and Bao, Z. (2020), "Collaborative detection and power allocation framework for target tracking in multiple radar system", Inform. Fusion, 55, 173-183. https://doi.org/10.1016/j.inffus.2019.08.010
  128. Yang, X.S. (2008), "Firefly algorithm", Nature-Inspired Metaheuristic Algorithms, 20, 79-90.
  129. Yang, L. and Chen, H. (2019), "Fault diagnosis of gearbox based on RBF-PF and particle swarm optimization wavelet neural network", Neural Comput. Applicat., 31(9), 4463-4478. https://doi.org/10.1007/s00521-018-3525-y
  130. Yang, M. and Sowmya, A. (2015), "An underwater color image quality evaluation metric", IEEE Transact. Image Process., 24(12), 6062-6071. https://doi.org/10.1109/TIP.2015.2491020
  131. Yang, S., Deng, B., Wang, J., Li, H., Lu, M., Che, Y., Wei, X. and Loparo, K.A. (2020a), "Scalable digital neuromorphic architecture for large-scale biophysically meaningful neural network with multi-compartment neurons", IEEE Transact. Neural Networks Learning Syst., 31(1), 148-162. https://doi.org/10.1109/TNNLS.2019.2899936
  132. Yang, W., Pudasainee, D., Gupta, R., Li, W., Wang, B. and Sun, L. (2020b), "An overview of inorganic particulate matter emission from coal/biomass/MSW combustion: Sampling and measurement, formation, distribution, inorganic composition and influencing factors", Fuel Process. Technol., 106657. https://doi.org/10.1016/j.fuproc.2020.106657
  133. Yang, W., Zhao, Y., Wang, D., Wu, H., Lin, A. and He, L. (2020c), "Using principal components analysis and IDW interpolation to determine spatial and temporal changes of surface water quality of Xin'anjiang river in Huangshan, China", Int. J. Environ. Res. Public Health, 17(8), 2942. https://doi.org/10.3390/ijerph17082942
  134. Yang, Y., Liu, J., Yao, J., Kou, J., Li, Z., Wu, T., Zhang, K., Zhang, L. and Sun, H (2020d), "Adsorption behaviors of shale oil in kerogen slit by molecular simulation", Chem. Eng. J., 387, 124054. https://doi.org/10.1016/j.cej.2020.124054
  135. Yeh, I.C. (2007), "Modeling slump flow of concrete using second-order regressions and artificial neural networks", Cement Concrete Compos., 29(6), 474-480. https://doi.org/10.1016/j.cemconcomp.2007.02.001
  136. Yeh, I.C. (2008), "Modeling slump of concrete with fly ash and superplasticizer", Comput. Concrete, Int. J., 5(6), 559-572. https://doi.org/10.12989/cac.2008.5.6.559
  137. Yeh, I.C. (2009), "Simulation of concrete slump using neural networks", Proceedings of the Institution of Civil Engineers-Construction Materials, 162(1), 11-18. https://doi.org/10.1680/coma.2009.162.1.11
  138. Yu, H., Shen, S., Qian, G. and Gong, X. (2020), "Packing theory and volumetrics-based aggregate gradation design method", J. Mater. Civil Eng., 32(6), 04020110. https://doi.org/10.1061/(ASCE)MT.1943-5533.0003192
  139. Yue, H., Wang, H., Chen, H., Cai, K. and Jin, Y. (2020), "Automatic detection of feather defects using Lie group and fuzzy Fisher criterion for shuttlecock production", Mech. Syst. Signal Process., 141, 106690. https://doi.org/10.1016/j.ymssp.2020.106690
  140. Yurtkuran, A. and Kucukoglu, I. (2018), "Comparative study of physics-inspired meta-heuristic algorithms for the solar cell parameter identification problem", Proceedings of the 16th International Conference on Clean Energy (ICCE- 2018).
  141. Zakeri, E., Moezi, S.A., Bazargan-Lari, Y. and Zare, A. (2017), "Multi-tracker optimization algorithm: a general algorithm for solving engineering optimization problems", Iran. J. Sci. Technol. Transact. Mech. Eng., 41(4), 315-341. https://doi.org/10.1007/s40997-016-0066-9
  142. Zakeri, E., Moezi, S.A. and Eghtesad, M. (2019), "Optimal interval type-2 fuzzy fractional order super twisting algorithm: A second order sliding mode controller for fully-actuated and under-actuated nonlinear systems", ISA Transact., 85, 13-32. https://doi.org/10.1016/j.isatra.2018.10.013
  143. Zenggang, X., Zhiwen, T., Xiaowen, C., Xue-min, Z., Kaibin, Z. and Conghuan, Y. (2019), "Research on image retrieval algorithm based on combination of color and shape features", J. Signal Process. Syst., 1-8. https://doi.org/10.1007/s11265-019-01508-y
  144. Zhang, W. (2020), "Parameter adjustment strategy and experimental development of hydraulic system for wave energy power generation", Symmetry, 12(5), 711. https://doi.org/10.3390/sym12050711
  145. Zhang, J. and Liu, B. (2019), "A review on the recent developments of sequence-based protein feature extraction methods", Current Bioinform., 14(3), 190-199. https://doi.org/10.2174/1574893614666181212102749
  146. Zhang, C. and Wang, H. (2020), "Swing vibration control of suspended structures using the Active Rotary Inertia Driver system: Theoretical modeling and experimental verification", Struct. Control Health Monitor., 27(6), e2543. https://doi.org/10.1002/stc.2543
  147. Zhang, C.W., Ou, J.P. and Zhang, J.Q. (2006), "Parameter optimization and analysis of a vehicle suspension system controlled by magnetorheological fluid dampers", Struct. Control Health Monitor., 13(5), 885-896. https://doi.org/10.1002/stc.63
  148. Zhang, B., Xu, D., Liu, Y., Li, F., Cai, J. and Du, L. (2016), "Multi-scale evapotranspiration of summer maize and the controlling meteorological factors in north China", Agricul. Forest Meteorol., 216, 1-12. https://doi.org/10.1016/j.agrformet.2015.09.015
  149. Zhang, T., Wu, X., Fan, X., Tsang, D.C., Li, G. and Shen, Y. (2019), "Corn waste valorization to generate activated hydrochar to recover ammonium nitrogen from compost leachate by hydrothermal assisted pretreatment", J. Environ. Manage., 236, 108-117. https://doi.org/10.1016/j.jenvman.2019.01.018
  150. Zhang, C., Abedini, M. and Mehrmashhadi, J. (2020a), "Development of pressure-impulse models and residual capacity assessment of RC columns using high fidelity Arbitrary Lagrangian-Eulerian simulation", Eng. Struct., 224, 111219. https://doi.org/10.1016/j.engstruct.2020.111219
  151. Zhang, K., Ruben, G.B., Li, X., Li, Z., Yu, Z., Xia, J. and Dong, Z. (2020b), "A comprehensive assessment framework for quantifying climatic and anthropogenic contributions to streamflow changes: A case study in a typical semi-arid North China basin", Environ. Modell. Software, 128, 104704. https://doi.org/10.1016/j.envsoft.2020.104704
  152. Zhang, X., Jiang, R., Wang, T. and Wang, J. (2020c), "Recursive neural network for video deblurring", IEEE Transact. Circuits Syst. Video Technol., 1-1. https://doi.org/10.1109/TCSVT.2020.3035722
  153. Zhang, X., Wang, T., Wang, J., Tang, G. and Zhao, L. (2020d), "Pyramid Channel-based Feature Attention Network for image dehazing", Comput. Vision Image Understand., 197, 103003. https://doi.org/10.1016/j.cviu.2020.103003
  154. Zhang, Y., Liu, R., Wang, X., Chen, H. and Li, C. (2020e), "Boosted binary Harris hawks optimizer and feature selection", Eng. Comput. https://doi.org/10.1007/s00366-020-01028-5
  155. Zhao, X., Li, D., Yang, B., Ma, C., Zhu, Y. and Chen, H. (2014), "Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton", Appl. Soft Comput., 24, 585-596. https://doi.org/10.1016/j.asoc.2014.07.024
  156. Zhao, X., Li, D., Yang, B., Chen, H., Yang, X., Yu, C. and Liu, S. (2015), "A two-stage feature selection method with its application", Comput. Electric. Eng., 47, 114-125. https://doi.org/10.1016/j.compeleceng.2015.08.011
  157. Zhao, X., Zhang, X., Cai, Z., Tian, X., Wang, X., Huang, Y., Chen, H. and Hu, L. (2019), "Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients", Computat. Biol. Chem., 78, 481-490. https://doi.org/10.1016/j.compbiolchem.2018.11.017
  158. Zhou, G., Moayedi, H. and Foong, L.K. (2020), "Teaching-learning-based metaheuristic scheme for modifying neural computing in appraising energy performance of building", Eng. Comput., 1-12. https://doi.org/10.1007/s00366-020-00981-5
  159. Zhu, Q. (2020), "Research on road traffic situation awareness system based on image big data", IEEE Intell. Syst., 35(1), 18-26. https://doi.org/10.1109/MIS.2019.2942836.
  160. Zhu, J., Shi, Q., Wu, P., Sheng, Z. and Wang, X. (2018), "Complexity analysis of prefabrication contractors' dynamic price competition in mega projects with different competition strategies", Complexity, 2018. https://doi.org/10.1155/2018/5928235
  161. Zhu, G., Wang, S., Sun, L., Ge, W. and Zhang, X. (2020a), "Output feedback adaptive dynamic surface sliding-mode control for quadrotor UAVs with tracking error constraints", Complexity, 2020. https://doi.org/10.1155/2020/8537198
  162. Zhu, L., Kong, L. and Zhang, C. (2020b), "Numerical study on hysteretic behaviour of horizontal-connection and energy-dissipation structures developed for prefabricated shear walls", Appl. Sci., 10(4), 1240. https://doi.org/10.3390/app10041240