Smart Structures and Systems

  • 제28권4호
  • /
  • Pages.535-551
  • /
  • 2021
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Machine learning and RSM models for prediction of compressive strength of smart bio-concrete

  • Algaifi, Hassan Amer (Faculty of Civil and Environmental Engineering, Universiti Tun Hussein Onn Malaysia) ;
  • Bakar, Suhaimi Abu (School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia) ;
  • Alyousef, Rayed (Department of Civil Engineering, College of Engineering, Prince Sattam bin Abdulaziz University) ;
  • Sam, Abdul Rahman Mohd. (School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia) ;
  • Alqarni, Ali S. (Department of Civil Engineering, College of Engineering, King Saud University) ;
  • Ibrahim, M.H. Wan (Faculty of Civil and Environmental Engineering, Universiti Tun Hussein Onn Malaysia) ;
  • Shahidan, Shahiron (Faculty of Civil and Environmental Engineering, Universiti Tun Hussein Onn Malaysia) ;
  • Ibrahim, Mohammed (Center for Engineering Research, Research Institute, King Fahd University of Petroleum and Minerals) ;
  • Salami, Babatunde Abiodun (Center for Engineering Research, Research Institute, King Fahd University of Petroleum and Minerals)
  • 투고 : 2021.01.14
  • 심사 : 2021.06.01
  • 발행 : 2021.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

In recent years, bacteria-based self-healing concrete has been widely exploited to improve the compressive strength of concrete using different bacterial species. However, both the identification of the optimal involved reaction parameters and theoretical framework information are still limited. In the present study, both experimentally and numerical modelling using machine learning (ANN and ANFIS) and response surface methodology (RSM) were implemented to evaluate and optimse the evolution of bacterial concrete strength. Therefore, a total of 58 compressive strength tests of the concrete incorporating new bacterial species were designed using different concentrations of urea, cells concentration, calcium, nutrient and time. Based on the results, the compressive strength of the bacterial concrete improved by 16% due to the decrement of the pore percentage in the concrete skin; specifically, 5 mm from the concrete surface, compared to that of the control concrete. In the same context, both machine the learning and RSM models indicated that the optimal range of urea, calcium, nutrient and bacterial cells were (18-23 g/L), (150-350 mM), (1-3 g/L) and 2×107 cells/mL, respectively. Based on the statistical analysis, RMSE, R2, MPE, RAE and RRSE were (0.793, 0.785), (0.985, 0.986), (1.508, 1.1), (0.11, 0.09) and (0.121, 0.12) from both the ANN and ANFIS models, respectively, while; the following values (0.839, 0.972, 1.678, 0.131 and 0.165) was obtained from RSM model, respectively. As such, it can be concluded that a high correlation and minimum error were obtained, however, machine learning models provided more accurate results compared to that of the RSM model.

키워드

과제정보

The authors acknowledge full gratitude to Universiti Tun Hussein Onn Malaysia (UTHM) and Ministry of Higher Education through fundamental research grant scheme VOT. NO. FRGS /1/2018/TK01/UTHM/02/3. In addition, this research activity was also supported and funded by Malaysian Technical University Network (MTUN) Grant Vot K122 and Industry Grant PLUS BERHAD (M007). Indeed, the authors would like to thank their support and cooperation in this research. Moreover, the authors extend their thanks to Shimadzu Corporation, Alex Corporation (M) Sdn Bhd (KS Wong) and Volume Graphic Software for their assistance and collaboration during the analysis of the X-Ray CT imaging using Shimadzu Inspection System.

참고문헌

  1. Abubakar, A.U., Akcaoglu, T. and Marar, K. (2018), "P-value significance level test for high-performance steel fiber concrete (HPSFC)", Comput. Concrete, Int. J., 21(5), 485-493. https://doi.org/10.12989/cac.2018.21.5.485
  2. Al-Mughanam, T., Aldhyani, T.H., AlSubari, B. and Al-Yaari, M. (2020), "Modeling of Compressive Strength of Sustainable SelfCompacting Concrete Incorporating Treated Palm Oil Fuel Ash Using Artificial Neural Network", Sustainability, 12(22), 9322. https://doi.org/10.3390/su12229322
  3. Alabduljabbar, H., Huseien, G.F., Sam, A.R.M., Alyouef, R., Algaifi, H.A. and Alaskar, A. (2020), "Engineering Properties of Waste Sawdust-Based Lightweight Alkali-Activated Concrete: Experimental Assessment and Numerical Prediction", Materials, 13(23), 5490. https://doi.org/10.3390/ma13235490
  4. Algaifi, H.A., Bakar, S.A., Sam, A.R.M., Abidin, A.R.Z., Shahir, S. and AL-Towayti, W.A.H. (2018), "Numerical modeling for crack self-healing concrete by microbial calcium carbonate", Constr. Build. Mater., 189, 816-824. https://doi.org/10.1016/j.conbuildmat.2018.08.218
  5. Algaifi, H.A., Bakar, S.A., Sam, A.R.M., Ismail, M., Abidin, A.R.Z., Shahir, S. and Altowayti, W.A.H. (2020), "Insight into the role of microbial calcium carbonate and the factors involved in self-healing concrete", Constr. Build. Mater., 254, 119258. https://doi.org/10.1016/j.conbuildmat.2020.119258
  6. Algaifi, H.A., Bakar, S.A., Alyousef, R., Sam, A.R.M., Ibrahim, M.W., Shahidan, S., Ibrahim, M. and Salami, B.A. (2021), "Bio-inspired self-healing of concrete cracks using new B. pseudomycoides species", J. Mater. Res. Technol., 12(5-6), 967-981. https://doi.org/10.1016/j.jmrt.2021.03.037
  7. Alkroosh, I.S. and Sarker, P.K. (2019), "Prediction of the compressive strength of fly ash geopolymer concrete using gene expression programming", Comput. Concrete, Int. J., 24(4), 295-302. https://doi.org/10.12989/cac.2019.24.4.295
  8. Alshalif, A.F., Irwan, J., Othman, N., Al-Gheethi, A., Shamsudin, S. and Nasser, I.M. (2021), "Optimisation of carbon dioxide sequestration into bio-foamed concrete bricks pores using Bacillus tequilensis", J. CO2 Utiliz., 44, 101412. https://doi.org/10.1016/j.jcou.2020.101412
  9. Altowayti, W.A.H., Algaifi, H.A., Bakar, S.A. and Shahir, S. (2019), "The adsorptive removal of As (III) using biomass of arsenic resistant Bacillus thuringiensis strain WS3: characteristics and modelling studies", Ecotoxicol. Environ. Safety, 172, 176-185. https://doi.org/10.1016/j.ecoenv.2019.01.067
  10. Awolusi, T.F., Oke, O.L., Akinkurolere, O.O. and Atoyebi, O.D. (2019), "Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres", Cogent Eng., 6(1), 1649852. https://doi.org/10.1080/23311916.2019.1649852
  11. Bahiraei, M., Mazaheri, N. and Hosseini, S. (2020), "Neural network modeling of thermo-hydraulic attributes and entropy generation of an ecofriendly nanofluid flow inside tubes equipped with novel rotary coaxial double-twisted tape", Powder Technology, 369, 162-175. https://doi.org/10.1016/j.powtec.2020.05.014
  12. Balam, N.H., Mostofinejad, D. and Eftekhar, M. (2017), "Effects of bacterial remediation on compressive strength, water absorption, and chloride permeability of lightweight aggregate concrete", Constr. Build. Mater., 145, 107-116. https://doi.org/10.1016/j.conbuildmat.2017.04.003
  13. Bundur, Z.B., Kirisits, M.J. and Ferron, R.D. (2015), "Biomineralized cement-based materials: Impact of inoculating vegetative bacterial cells on hydration and strength", Cem. Concrete Res., 67, 237-245. https://doi.org/10.1016/j.cemconres.2014.10.002
  14. Bundur, Z.B., Amiri, A., Ersan, Y.C., Boon, N. and De Belie, N. (2017), "Impact of air entraining admixtures on biogenic calcium carbonate precipitation and bacterial viability", Cem. Concrete Res., 98, 44-49. https://doi.org/10.1016/j.cemconres.2017.04.005
  15. Dao, D.V., Ly, H.-B., Trinh, S.H., Le, T.-T. and Pham, B.T. (2019), "Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete", Materials, 12(6), 983. https://doi.org/10.3390/ma12060983
  16. Dutta, S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput. Concrete, Int. J., 21(4), 463-470. https://doi.org/10.12989/cac.2018.21.4.463
  17. Golafshani, E.M., Behnood, A. and Arashpour, M. (2020), "Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer", Constr. Build. Mater., 232, 117266. https://doi.org/10.1016/j.conbuildmat.2019.117266
  18. 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
  19. Huseien, G.F., Sam, A.R.M., Algaifi, H.A. and Alyouef, R. (2021), "Development of a sustainable concrete incorporated with effective microorganism and fly Ash: Characteristics and modeling studies", Constr. Build. Mater., 285, 122899. https://doi.org/10.1016/j.conbuildmat.2021.122899
  20. Jafari, S. and Mahini, S.S. (2017), "Lightweight concrete design using gene expression programing", Constr. Build. Mater., 139, 93-100. https://doi.org/10.1016/j.conbuildmat.2021.122899
  21. Jakubovskis, R., Jankute, A., Urbonavicius, J. and Gribniak, V. (2020), "Analysis of mechanical performance and durability of self-healing biological concrete", Constr. Build. Mater., 260, 119822. https://doi.org/10.1016/j.conbuildmat.2020.119822
  22. Jena, S., Basa, B., Panda, K.C. and Sahoo, N.K. (2020), "Impact of Bacillus subtilis bacterium on the properties of concrete", Mater Today Proc. https://doi.org/10.1016/j.matpr.2020.03.129
  23. Kathirvel, P. and Kaliyaperumal, S.R.M. (2017), "Probabilistic modeling of geopolymer concrete using response surface methodology", Comput. Concrete, Int. J., 19(6), 737-744. https://doi.org/10.12989/cac.2017.19.6.737
  24. Liu, Q.-f., Iqbal, M.F., Yang, J., Lu, X.-y., Zhang, P. and Rauf, M. (2021), "Prediction of chloride diffusivity in concrete using artificial neural network: Modelling and performance evaluation", Constr. Build. Mater., 268, 121082. https://doi.org/10.1016/j.conbuildmat.2020.121082
  25. 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
  26. Mahdinia, S., Eskandari-Naddaf, H. and Shadnia, R. (2019), "Effect of cement strength class on the prediction of compressive strength of cement mortar using GEP method", Constr. Build. Mater., 198, 27-41. https://doi.org/10.1016/j.conbuildmat.2018.11.265
  27. Mahmud, M.Z.H., Hassan, N.A., Hainin, M.R. and Ismail, C.R. (2017), "Microstructural investigation on air void properties of porous asphalt using virtual cut section", Constr. Build. Mater., 155, 485-494. https://doi.org/10.1016/j.conbuildmat.2017.08.103
  28. Mondal, S. and Ghosh, A.D. (2019), "Review on microbial induced calcite precipitation mechanisms leading to bacterial selection for microbial concrete", Constr. Build. Mater., 225, 67-75. https://doi.org/10.1016/j.conbuildmat.2019.07.122
  29. Mousavi, S.M., Aminian, P., Gandomi, A.H., Alavi, A.H. and Bolandi, H. (2012), "A new predictive model for compressive strength of HPC using gene expression programming", Adv. Eng. Software, 45(1), 105-114. https://doi.org/10.1016/j.advengsoft.2011.09.014
  30. Mukharjee, B.B. and Barai, S.V. (2014), "Influence of incorporation of nano-silica and recycled aggregates on compressive strength and microstructure of concrete", Constr. Build. Mater., 71, 570-578. https://doi.org/10.1016/j.conbuildmat.2014.08.040
  31. Muthuraj, M. (2019), "Prediction of compressive strength of bacteria incorporated geopolymer concrete by using ANN and MARS", Struct. Eng. Mech., Int. J., 70(6), 671-681. https://doi.org/10.12989/sem.2019.70.6.671
  32. Naderpour, H. and Mirrashid, M. (2020), "Estimating the compressive strength of eco-friendly concrete incorporating recycled coarse aggregate using neuro-fuzzy approach", J. Clean. Prod., 121886. https://doi.org/10.1016/j.jclepro.2020.121886
  33. Nain, N., Surabhi, R., Yathish, N., Krishnamurthy, V., Deepa, T. and Tharannum, S. (2019), "Enhancement in strength parameters of concrete by application of Bacillus bacteria", Constr. Build. Mater., 202, 904-908. https://doi.org/10.1016/j.conbuildmat.2019.01.059
  34. Nathaniel, O., Sam, A.R.M., Lim, N.H.A.S., Adebisi, O. and Abdulkareem, M. (2020), "Biogenic approach for concrete durability and sustainability using effective microorganisms: A review", Constr. Build. Mater., 261, 119664. https://doi.org/10.1016/j.conbuildmat.2020.119664
  35. Nematzadeh, M., Shahmansouri, A.A. and Zabihi, R. (2021), "Innovative models for predicting post-fire bond behavior of steel rebar embedded in steel fiber reinforced rubberized concrete using soft computing methods", In: Structures, Vol. 31, pp. 1141-1162. https://doi.org/10.1016/j.istruc.2021.02.015
  36. Nguyen, T.H., Ghorbel, E., Fares, H. and Cousture, A. (2019), "Bacterial self-healing of concrete and durability assessment", Cem. Concrete Compos., 104, 103340. https://doi.org/10.1016/j.cemconcomp.2019.103340
  37. Nigdeli, S.M. and Bekdas, G. (2013), "Optimum tuned mass damper design for preventing brittle fracture of RC buildings", Smart Struct. Syst., Int. J., 12(2), 137-155. https://doi.org/10.12989/sss.2013.12.2.137
  38. Nosrati, A., Zandi, Y., Shariati, M., Khademi, K., Aliabad, M.D., Marto, A., Mu'azu, M., Ghanbari, E., Mahdizadeh, M. and Shariati, A. (2018), "Portland cement structure and its major oxides and fineness", Smart Struct. Syst., Int. J., 22(4), 425-432. https://doi.org/10.12989/sss.2018.22.4.425
  39. Okwadha, G.D. and Li, J. (2010), "Optimum conditions for microbial carbonate precipitation", Chemosphere, 81(9), 1143- 1148. https://doi.org/10.1016/j.chemosphere.2010.09.066
  40. Onat, O. and Celik, E. (2017), "An integral based fuzzy approach to evaluate waste materials for concrete", Smart Struct. Syst., Int. J., 19(3), 323-333. https://doi.org/10.12989/sss.2017.19.3.323
  41. Perumal, R. and Prabakaran, V. (2020), "Estimating the compressive strength of HPFRC containing metallic fibers using statistical methods and ANNs", Adv. Concrete Constr., Int. J., 10(6), 479-488. https://doi.org/10.12989/acc.2020.10.6.479
  42. Prasad, B.R., Eskandari, H. and Reddy, B.V. (2009), "Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN", Constr. Build. Mater., 23(1), 117-128. https://doi.org/10.1016/j.conbuildmat.2008.01.014
  43. Qian, C., Wang, J., Wang, R. and Cheng, L. (2009), "Corrosion protection of cement-based building materials by surface deposition of CaCO3 by Bacillus pasteurii", Mater. Sci. Eng.: C, 29(4), 1273-1280. https://doi.org/10.1016/j.msec.2008.10.025
  44. Reddy, B.M.S. and Revathi, D. (2019), "An experimental study on effect of Bacillus sphaericus bacteria in crack filling and strength enhancement of concrete", Materials Today: Proceedings, 19(2), 803-809. https://doi.org/10.1016/j.matpr.2019.08.135
  45. 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
  46. Scrivener, K., Fullmann, T., Gallucci, E., Walenta, G. and Bermejo, E. (2004), "Quantitative study of Portland cement hydration by X-ray diffraction/Rietveld analysis and independent methods", Cem. Concrete Res., 34(9), 1541-1547. https://doi.org/10.1016/j.cemconres.2004.04.014
  47. Shaban, W.M., Yang, J., Elbaz, K., Xie, J. and Li, L. (2021), "Fuzzy-metaheuristic ensembles for predicting the compressive strength of brick aggregate concrete", Resour. Conserv. Recycl., 169, 105443. https://doi.org/10.1016/j.resconrec.2021.105443
  48. Shaheen, N., Khushnood, R.A., Khaliq, W., Murtaza, H., Iqbal, R. and Khan, M.H. (2019), "Synthesis and characterization of bioimmobilized nano/micro inert and reactive additives for feasibility investigation in self-healing concrete", Constr. Build. Mater., 226, 492-506. https://doi.org/10.1016/j.conbuildmat.2019.07.202
  49. Shahmansouri, A.A., Bengar, H.A. and Jahani, E. (2019), "Predicting compressive strength and electrical resistivity of eco-friendly concrete containing natural zeolite via GEP algorithm", Constr. Build. Mater., 229, 116883. https://doi.org/10.1016/j.conbuildmat.2019.116883
  50. 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. Clean. Prod., 279, 123697. https://doi.org/10.1016/j.jclepro.2020.123697
  51. Shahmansouri, A.A., Bengar, H.A. and AzariJafari, H. (2021a), "Life cycle assessment of eco-friendly concrete mixtures incorporating natural zeolite in sulfate-aggressive environment", Constr. Build. Mater., 268, 121136. https://doi.org/10.1016/j.conbuildmat.2020.121136
  52. Shahmansouri, A.A., Nematzadeh, M. and Behnood, A. (2021b), "Mechanical properties of GGBFS-based geopolymer concrete incorporating natural zeolite and silica fume with an optimum design using response surface method", J. Build. Eng., 36, 102138. https://doi.org/10.1016/j.jobe.2020.102138
  53. Shariati, M., Mafipour, M.S., Haido, J.H., Yousif, S.T., Toghroli, A., Trung, N.T. and Shariati, A. (2020a), "Identification of the most influencing parameters on the properties of corroded concrete beams using an Adaptive Neuro-Fuzzy Inference System (ANFIS)", Steel Compos. Struct., Int. J., 34(1), 155-170. https://doi.org/10.12989/scs.2020.34.1.155
  54. Shariati, M., Mafipour, M.S., Mehrabi, P., Ahmadi, M., Wakil, K., Trung, N.T. and Toghroli, A. (2020b), "Prediction of concrete strength in presence of furnace slag and fly ash using Hybrid ANN-GA (Artificial Neural Network-Genetic Algorithm)", Smart Struct. Syst., Int. J., 25(2), 183-195. https://doi.org/10.12989/sss.2020.25.2.183
  55. Shirkhani, A., Davarnia, D. and Azar, B.F. (2019), "Prediction of bond strength between concrete and rebar under corrosion using ANN", Comput. Concrete, Int. J., 23(4), 273-279. https://doi.org/10.12989/cac.2019.23.4.273
  56. Siddique, R., Jameel, A., Singh, M., Barnat-Hunek, D., Ait-Mokhtar, A., Belarbi, R. and Rajor, A. (2017), "Effect of bacteria on strength, permeation characteristics and microstructure of silica fume concrete", Constr. Build. Mater., 142, 92-100. https://doi.org/10.1016/j.conbuildmat.2017.03.057
  57. Snoeck, D., Dewanckele, J., Cnudde, V. and De Belie, N. (2016), "X-ray computed microtomography to study autogenous healing of cementitious materials promoted by superabsorbent polymers", Cem. Concrete Compos., 65, 83-93. https://doi.org/10.1016/j.cemconcomp.2015.10.016
  58. Sokhansefat, G., Moradian, M., Finnell, M., Behravan, A., Ley, M.T., Lucero, C. and Weiss, J. (2020), "Using X-ray computed tomography to investigate mortar subjected to freeze-thaw cycles", Cem. Concrete Compos., 108, 103520. https://doi.org/10.1016/j.cemconcomp.2020.103520
  59. Soto-Perez, L., Lopez, V. and Hwang, S.S. (2015), "Response Surface Methodology to optimize the cement paste mix design: Time-dependent contribution of fly ash and nano-iron oxide as admixtures", Mater. Des., 86, 22-29. https://doi.org/10.1016/j.matdes.2015.07.049
  60. Su, Y., Feng, J., Jin, P. and Qian, C. (2019), "Influence of bacterial self-healing agent on early age performance of cement-based materials", Constr. Build. Mater., 218, 224-234. https://doi.org/10.1016/j.conbuildmat.2019.05.077
  61. Topcu, I.B. and Saridemir, M. (2008), "Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic", Computat. Mater. Sci., 41(3), 305-311. https://doi.org/10.1016/j.commatsci.2007.04.009
  62. Valipour, M., Yekkalar, M., Shekarchi, M. and Panahi, S. (2014), "Environmental assessment of green concrete containing natural zeolite on the global warming index in marine environments", J. Clean. Prod., 65, 418-423. https://doi.org/10.1016/j.jclepro.2013.07.055
  63. Wang, J., Jonkers, H.M., Boon, N. and De Belie, N. (2017), "Bacillus sphaericus LMG 22257 is physiologically suitable for self-healing concrete", Appl. Microbiol. Biotechnol., 101(12), 5101-5114. https://doi.org/10.1007/s00253-017-8260-2
  64. Wu, M., Hu, X., Zhang, Q., Cheng, W., Xue, D. and Zhao, Y. (2020), "Application of bacterial spores coated by a green inorganic cementitious material for the self-healing of concrete cracks", Cem. Concrete Compos., 103718. https://doi.org/10.1016/j.cemconcomp.2020.103718
  65. Xu, J., Wang, B. and Zuo, J. (2017), "Modification effects of nanosilica on the interfacial transition zone in concrete: A multiscale approach", Cem. Concrete Compos., 81, 1-10. https://doi.org/10.1016/j.cemconcomp.2017.04.003
  66. Zhang, J.L., Wu, R.S., Li, Y.M., Zhong, J.Y., Deng, X., Liu, B., Han, N.X. and Xing, F. (2016), "Screening of bacteria for self-healing of concrete cracks and optimization of the microbial calcium precipitation process", Appl. Microbiol. Biotechnol., 100(15), 6661-6670. https://doi.org/10.1007/S00253-016-7382-2
  67. Zhang, J., Bian, F., Zhang, Y., Fang, Z., Fu, C. and Guo, J. (2018), "Effect of pore structures on gas permeability and chloride diffusivity of concrete", Constr. Build. Mater., 163, 402-413. https://doi.org/10.1016/j.conbuildmat.2017.12.111
  68. Zhang, J., Li, D. and Wang, Y. (2020), "Predicting uniaxial compressive strength of oil palm shell concrete using a hybrid artificial intelligence model", J. Build. Eng., 30, 101282. https://doi.org/10.1016/j.jobe.2020.101282
  69. Zhao, Y., Moayedi, H., Bahiraei, M. and Foong, L.K. (2020), "Employing TLBO and SCE for optimal prediction of the compressive strength of concrete", Smart Struct. Syst., Int. J., 26(6), 753-763. http://doi.org/10.12989/sss.2020.26.6.753
  70. Zhao, Y., Bai, C., Xu, C. and Foong, L.K. (2021a), "Efficient metaheuristic-retrofitted techniques for concrete slump simulation", Smart Struct. Syst., Int. J., 27(5), 745-759. http://doi.org/10.12989/sss.2021.27.5.745
  71. Zhao, Y., Zhong, X. and Foong, L.K. (2021b), "Predicting the splitting tensile strength of concrete using an equilibrium optimization model", Steel Compos. Struct., Int. J., 39(1), 81-93. https://doi.org/10.12989/scs.2021.39.1.081