Steel and Composite Structures

  • 제44권3호
  • /
  • Pages.389-406
  • /
  • 2022
  • /
  • 1229-9367(pISSN)
  • /
  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Estimation of splitting tensile strength of modified recycled aggregate concrete using hybrid algorithms

  • Zhu, Yirong (School of Management Engineering, Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Huang, Lihua (School of Management Engineering, Zhejiang Guangsha Vocational and Technical University of Construction) ;
  • Zhang, Zhijun (Southwest China Architectural Design and Research Institute Corp. Ltd) ;
  • Bayrami, Behzad (Department of Civil Engineering, Moghadas Ardabili Institute of Higher Education)
  • 투고 : 2021.12.13
  • 심사 : 2022.08.01
  • 발행 : 2022.08.10
⟨ 이전 논문 다음 논문 ⟩

초록

Recycling concrete construction waste is an encouraging step toward green and sustainable building. A lot of research has been done on recycled aggregate concretes (RACs), but not nearly as much has been done on concrete made with recycled aggregate. Recycled aggregate concrete, on the other hand, has been found to have a lower mechanical productivity compared to conventional one. Accurately estimating the mechanical behavior of the concrete samples is a most important scientific topic in civil, structural, and construction engineering. This may prevent the need for excess time and effort and lead to economic considerations because experimental studies are often time-consuming, costly, and troublous. This study presents a comprehensive data-mining-based model for predicting the splitting tensile strength of recycled aggregate concrete modified with glass fiber and silica fume. For this purpose, first, 168 splitting tensile strength tests under different conditions have been performed in the laboratory, then based on the different conditions of each experiment, some variables are considered as input parameters to predict the splitting tensile strength. Then, three hybrid models as GWO-RF, GWO-MLP, and GWO-SVR, were utilized for this purpose. The results showed that all developed GWO-based hybrid predicting models have good agreement with measured experimental results. Significantly, the GWO-RF model has the best accuracy based on the model performance assessment criteria for training and testing data.

키워드

참고문헌

  1. Abdollahzadeh, G., Jahani, E., Kashir, Z. (2016), "Predicting of compressive strength of recycled aggregate concrete by genetic programming", Comput Concr., 18(2), 155-163. https://doi.org/10.12989/cac.2016.18.2.155.
  2. Agar, O., Tekin, H. O., Sayyed, M. I., Korkmaz, M. E., Culfa, O. and Ertugay, C. (2019), "Experimental investigation of photon attenuation behaviors for concretes including natural perlite mineral", Results Phys., 12, 237-243. https://doi.org/10.1016/j.rinp.2018.11.053.
  3. Al-Fugara, A. K., Ahmadlou, M., Al-Shabeeb, A. R., AlAyyash, S., Al-Amoush, H. and Al-Adamat, R. (2020), "Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression", Geocarto Int., 37(1), 284-303. https://doi.org/10.1080/10106049.2020.1716396.
  4. Ali, B., Ahmed, H., Ali Qureshi, L., Kurda, R., Hafez, H., Mohammed, H., and Raza, A. (2020), "Enhancing the hardened properties of recycled concrete (RC) through synergistic incorporation of fiber reinforcement and silica fume", Materials, 13(18), 4112. https://doi.org/10.3390/ma13184112.
  5. Altun M.G. and Oltulu, M. (2020), "Effect of different types of fiber utilization on mechanical properties of recycled aggregate concrete containing silica fume", J. Green Build., 15(1), 119-136. https://doi.org/10.3992/1943-4618.15.1.119.
  6. Archer, K.J. and Kimes, R.V. (2008), "Empirical characterization of random forest variable importance measures", Comput. Stat. Data Anal., 52(4), 2249-2260. https://doi.org/10.1016/j.csda.2007.08.015.
  7. Assaggaf, R.A., Ali, M.R., Al-Dulaijan, S.U. and Maslehuddin, M. (2021), "Properties of concrete with untreated and treated crumb rubber - A review", J. Mater. Res. Technol., 11, 1753-1798. https://doi.org/10.1016/j.jmrt.2021.02.019.
  8. ASTM C-17 (2017), Standard Test Method for Splitting Tensile Strength of Cylindrical Concrete Specimens, ASTM International; West Conshohocken, PA, USA.
  9. ASTM C-18 (2018), Standard specification for concrete aggregates, ASTM International; West Conshohocken, PA, USA.
  10. ASTM C136-01 (2017), Standard Test Method for Sieve Analysis of Fine and Coarse Aggregates, ASTM International; West Conshohocken, PA, USA.
  11. Awoyera, P.O., Kirgiz, M.S., Viloria, A. and Ovallos-Gazabon, D. (2020), "Estimating strength properties of geopolymer selfcompacting concrete using machine learning techniques", J. Mater. Res. Technol., 9(4), 9016-9028. https://doi.org/10.1016/j.jmrt.2020.06.008.
  12. Bahurudeen, A., Kanraj, D., Dev, V.G. and Santhanam, M. (2015), "Performance evaluation of sugarcane bagasse ash blended cement in concrete", Cem. Concr. Compos., 59, 77-88. https://doi.org/10.1016/j.cemconcomp.2015.03.004.
  13. Benemaran, R.S. and Esmaeili-Falak, M. (2020), "Optimization of cost and mechanical properties of concrete with admixtures using MARS and PSO", Comput. Concr., 26(4), 309-316. http://dx.doi.org/10.12989/cac.2020.26.4.309.
  14. Biau, G., Devroye, L. and Lugosi, G. (2008), "Consistency of random forests and other averaging classifiers", J. Mach. Learn. Res., 9, 2015-2033.
  15. Breima, L. (2010), "Random forests", Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324.
  16. Candido, V.S., da Silva, A.C.R., Simonassi, N.T., Lima, E.S., da Luz, F.S. and Monteiro, S.N. (2018), "Mechanical and microstructural characterization of geopolymeric concrete subjected to fatigue", J. Mater. Res. Technol., 7(4), 566-570. https://doi.org/10.1016/j.jmrt.2018.07.011.
  17. Chalee, W., Cheewaket, T. and Jaturapitakkul, C. (2021), "Enhanced durability of concrete with palm oil fuel ash in a marine environment", J. Mater. Res. Technol., 13, 128-137. https://doi.org/10.1016/j.jmrt.2021.04.061.
  18. Chen, W., Wang, Y., Cao, G., Chen, G. and Gu, Q. (2014), "A random forest model based classification scheme for neonatal amplitude-integrated EEG", Biomed. Eng. Online, 13(2), 1-13. https://doi.org/10.1186/1475-925X-13-S2-S4.
  19. Cordeiro, G.C., Toledo Filho, R.D., Tavares, L.M. and Fairbairn, E.D. M.R. (2009), "Ultrafine grinding of sugar cane bagasse ash for application as pozzolanic admixture in concrete", Cem. Concr. Res., 39(2), 110-115. https://doi.org/10.1016/j.cemconres.2008.11.005.
  20. da Silva, T. R., de Azevedo, A. R. G., Cecchin, D., Marvila, M. T., Amran, M., Fediuk, R., Vatin, N., Karelina, M., Klyuev, S. and Szelag, M. (2021), "Application of plastic wastes in construction materials: a review using the concept of life-cycle assessment in the context of recent research for future perspectives", Materials, 14(13), 3549. https://doi.org/10.3390/ma14133549.
  21. Dang, X.J., Shi, L. and Zhao, N. (2021), "Prediction of utilization ratio of blast furnace gas based on parameter optimized by SVR method", J. Iron Steel Res., 33, 279-283.
  22. Desai, T., Rimpal, S., Peled, A. and Mobasher, B. (2003), "Mechanical properties of concrete reinforced with AR-glass fibers", Brittle Matrix Composites 7, Elsevier, Amsterdam, The Netherlands. 223-232.
  23. Ding, Y. and Bai, Y.L. (2018), "Fracture properties and softening curves of steel fiber-reinforced slag-based geopolymer mortar and concrete", Materials, 11(8), 1445. https://doi.org/10.3390/ma11081445.
  24. Esmaeili-Falak, M. and Hajialilue-Bonab, M. (2012), "Numerical studying the effects of gradient degree on slope stability analysis using limit equilibrium and finite element methods", Int. J. Acad. Res., 4, 216-222. https://doi.org/10.7813/2075-4124.2012/4-4/A.30.
  25. Esmaeili-Falak, M. and Sarkhani Benemaran, R. (2022), "Investigating the stress-strain behavior of frozen clay using triaxial test", J. Struct. Constr. Eng., 2022. https://doi.org/10.22065/JSCE.2022.332406.2747
  26. Esmaeili-Falak, M., Katebi, H. and Javadi, A. (2018), "Experimental study of the mechanical behavior of frozen soilsA case study of tabriz subway", Period Polytech. Civil Eng., 62(1), 117-125. https://doi.org/10.3311/PPci.10960
  27. Esmaeili-Falak, M., Katebi, H. and Javadi, A.A. (2020), "Effect of freezing on stress-strain characteristics of granular and cohesive soils", J. Cold Reg. Eng., 34, 5020001. https://doi.org/10.1061/(ASCE)CR.1943-5495.0000205.
  28. Esmaeili-Falak, M., Katebi, H., Vadiati, M. and Adamowski, J. (2019), "Predicting triaxial compressive strength and Young's modulus of frozen sand using artificial intelligence methods", J. Cold Reg. Eng., 33(3), 4019007. https://doi.org/10.1061/(ASCE)CR.1943-5495.0000188.
  29. Esmaeili Falak, M., Lotfi Eghlim, A. and Nematzadeh, S. (2019), "Improvement of mechanical parameters of concrete yielded from pozzolanic cement for irrigation and drainage projects", J. Struct. Constr. Eng., 6(Special Issue 1), 43-58. https://dx.doi.org/10.22065/jsce.2017.100834.1349.
  30. Esmaeili Falak, M., Sarkhani Benemaran, R. and Seifi, R. (2020), "Improvement of the mechanical and durability parameters of construction concrete of the Qotursuyi spa", Concr. Res., 13, 119-134. https://dx.doi.org/10.22124/jcr.2020.14518.1395.
  31. Faris, H., Aljarah, I., Al-Betar, M.A. and Mirjalili, S. (2018), "Grey wolf optimizer: a review of recent variants and applications", Neural Comput Appl., 30(2), 413-435. https://doi.org/10.1007/s00521-017-3272-5.
  32. Gar, P.S., Suresh, N. and Bindiganavile, V. (2017), "Sugar cane bagasse ash as a pozzolanic admixture in concrete for resistance to sustained elevated temperatures", Constr. Build. Mater., 153, 929-936. https://doi.org/10.1016/j.conbuildmat.2017.07.107.
  33. Ge, D.M., Zhao, L.C. and Esmaeili-Falak, M. (2022), "Estimation of rapid chloride permeability of SCC using hyperparameters optimized random forest models", J. Sustain Cem. Mater., 1-19. https://doi.org/10.1080/21650373.2022.2093291.
  34. Guo, L., Guo, X., Hong, J. and Wang, Y. (2017), "Constitutive relation of concrete containing meso-structural characteristics", Results Phys., 7, 1155-1160. https://doi.org/10.1016/j.rinp.2017.03.005.
  35. Hong, H., Pourghasemi, H.R. and Pourtaghi, Z.S. (2016), "Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models", Geomorphology, 259, 105-118. https://doi.org/10.1016/j.geomorph.2016.02.012.
  36. Hosan, A. and Shaikh, F.U.A. (2021), "Compressive strength development and durability properties of high volume slag and slag-fly ash blended concretes containing nano-CaCO3", J. Mater. Res. Technol., 10, 1310-1322. https://doi.org/10.1016/j.jmrt.2021.01.001.
  37. Hu, J., Zhou, T., Ma, S., Yang, D., Guo, M. and Huang, P. (2022), "Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine", Sci. Rep., 12, 1-20. https://doi.org/10.1038/s41598-022-05027-y.
  38. James, J., Pandian, P. K., Deepika, K., Manikanda Venkatesh, J., Manikandan, V. and Manikumaran, P. (2016), "Cement stabilized soil blocks admixed with sugarcane bagasse ash", J. Eng., 2016, 7940239. https://doi.org/10.1155/2016/7940239.
  39. Jamsawang, P., Poorahong, H., Yoobanpot, N., Songpiriyakij, S. and Jongpradist, P. (2017), "Improvement of soft clay with cement and bagasse ash waste", Constr Build Mater., 154, 61-71. https://doi.org/10.1016/j.conbuildmat.2017.07.188.
  40. Kandiri, A., Sartipi, F. and Kioumarsi, M. (2021), "Predicting compressive strength of concrete containing recycled aggregate using modified ann with different optimization algorithms", Appl. Sci., 11(2), 485. https://doi.org/10.3390/app11020485.
  41. Katare, V.D. and Madurwar, M.V. (2021), "Process standardization of sugarcane bagasse ash to develop durable high-volume ash concrete", J. Build. Eng., 39, 102151. https://doi.org/10.1016/j.jobe.2021.102151
  42. Khademi, F., Jamal, S.M., Deshpande, N. and Londhe, S. (2016), "Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression", Int. J. Sustain. Built Environ., 5(2), 355-369. https://doi.org/10.1016/j.ijsbe.2016.09.003.
  43. Khalil, M.J., Aslam, M. and Ahmad, S. (2020), "Utilization of sugarcane bagasse ash as cement replacement for the production of sustainable concrete - A review", Constr. Build. Mater., 270, 121371. https://doi.org/10.1016/j.conbuildmat.2020.121371.
  44. Kim, K.K., Urgessa, G.S. and Yeon, J.H. (2020), "Analysis and modeling of uniaxial compressive creep of MMA-modified unsaturated polyester polymer concrete", J. Mater. Res. Technol., 9, 12773-12782. https://doi.org/10.1016/j.jmrt.2020.09.039.
  45. Kumar, R.S., Vijayan, D.S., Manzoor, P.M., Subinjith, N. and Santhosh, S. (2020), "Effect of silica fume on strength of glass fiber incorporated concrete", AIP Conference Proceedings, AIP Publishing LLC, Long Island, NY, USA. 30020.
  46. Lee, S. and Shin, S. (2019), "Prediction on Compressive and Split Tensile Strengths of GGBFS/FA Based GPC", Materials, 12(24), 4198. https://doi.org/10.3390/ma12244198.
  47. Liaw, A. and Wiener, M. (2002), "Classification and regression by randomForest", R news, 2(3), 18-22.
  48. Lin, S., Zheng, H., Han, C., Han, B. and Li, W. (2021), "Evaluation and prediction of slope stability using machine learning approaches", Frontiers Struct. Civil Eng., 15(4), 821-833. https://doi.org/10.1007/s11709-021-0742-8.
  49. Lv, J., Zhou, T., Li, K. and Sun, K. (2019), "Shrinkage properties of self-compacting rubber lightweight aggregate concrete: experimental and analytical studies", Materials, 12(24), 4059. https://doi.org/10.3390/ma12244059.
  50. Martinez-Garcia, R., Jagadesh, P., Burdalo-Salcedo, G., Palencia, C., Fernandez-Raga, M. and Fraile-Fernandez, F.J. (2021), "Impact of design parameters on the ratio of compressive to split tensile strength of self-compacting concrete with recycled aggregate", Materials, 14(13), 3480. https://doi.org/10.3390/ma14133480.
  51. Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014), "Grey wolf optimizer", Adv. Eng. Softw., 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007.
  52. Moayedi, H., Mu'azu, M.A. and Kok Foong, L. (2021), "Swarmbased analysis through social behavior of grey wolf optimization and genetic programming to predict friction capacity of driven piles", Eng. Comput., 37(2), 1277-1293. https://doi.org/10.1007/s00366-019-00885-z.
  53. Mohammed, A., Kurda, R., Armaghani, D.J. and Hasanipanah, M. (2021), "Prediction of compressive strength of concrete modified with fly ash: Applications of neuro-swarm and neuroimperialism models", Comput. Concr., 27, 489-512. https://doi.org/10.12989/cac.2021.27.5.489.
  54. Murthi, P., Poongodi, K., Awoyera, P. O., Gobinath, R. and Saravanan, R. (2020), "Enhancing the strength properties of high-performance concrete using ternary blended cement: OPC, nano-silica, bagasse ash", Silicon, 12(8), 1949-1956. https://doi.org/10.1007/s12633-019-00324-0.
  55. Nhu, V.H., Hoang, N.D., Duong, V. B., Vu, H.D. and Tien Bui, D. (2020), "A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: A case study at Vinhomes Imperia project, Hai Phong city (Vietnam)", Eng. Comput., 36(2), 603-616. https://doi.org/10.1007/s00366-019-00718-z.
  56. Ogrodnik, P., Szulej, J. and Franus, W. (2018), "The wastes of sanitary ceramics as recycling aggregate to special concretes", Materials, 11(8), 1275. https://doi.org/10.3390/ma11081275.
  57. Pinkus, A. (1999), "Approximation theory of the MLP model in neural networks", Acta Numerica, 8, 143-195. https://doi.org/10.1017/S0962492900002919.
  58. Praveenkumar, S. and Sankarasubramanian, G. (2019), "Mechanical and durability properties of bagasse ash-blended high-performance concrete", SN Appl. Sci., 1(12), 1-7. https://doi.org/10.1007/s42452-019-1711-x.
  59. Qi, C., Chen, Q., Fourie, A. and Zhang, Q. (2018), "An intelligent modelling framework for mechanical properties of cemented paste backfill", Minerals Eng., 123, 16-27. https://doi.org/10.1016/j.mineng.2018.04.010.
  60. Reddy, A.V.S., Reddy, M.D. and Reddy, M.S.K. (2017), "Network reconfiguration of distribution system for loss reduction using GWO algorithm", Int. J. Electr. Comput. Eng., 7(6), 3226-3234. https://doi.org/10.11591/ijece.v7i6.pp3226-3234.
  61. Rong, C., Ma, J., Shi, Q. and Wang, Q. (2021), "The simple mix design method and confined behavior analysis for recycled aggregate concrete", Materials, 14, 3533. https://doi.org/10.3390/ma14133533.
  62. Saba, A.M., Khan, A.H., Akhtar, M.N., Khan, N.A., Koloor, S S. R., Petru, M. and Radwan, N. (2021), "Strength and flexural behavior of steel fiber and silica fume incorporated selfcompacting concrete", J. Mater. Res. Technol., 12, 1380-1390. https://doi.org/10.1016/j.jmrt.2021.03.066.
  63. Salimbahrami, S.R. and Shakeri, R. (2021), "Experimental investigation and comparative machine-learning prediction of compressive strength of recycled aggregate concrete", Soft Comput., 25, 919-932. https://doi.org/10.1007/s00500-021-05571-1.
  64. Sarkhani Benemaran, R., Esmaeili-Falak, M. and Katebi, H. (2021), "Physical and numerical modelling of pile-stabilised saturated layered slopes", Proc. Inst. Civil Eng. Geotech. Eng., 1-16. https://doi.org/10.1680/jgeen.20.00152.
  65. Sarkhani Benemaran, R., Esmaeili-Falak, M. and Javadi, A. (2022), "Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimized models", Int. J. Pavement Eng., 1-20. https://doi.org/10.1080/10298436.2022.2095385.
  66. Shah, M. I., Amin, M. N., Khan, K., Niazi, M. S. K., Aslam, F., Alyousef, R., Javed, M.F. and Mosavi, A. (2021), "Performance evaluation of soft computing for modeling the strength properties of waste substitute green concrete", Sustainability, 13(5), 2867. https://doi.org/10.3390/su13052867.
  67. Shozib, I.A., Ahmad, A., Rahaman, M.S.A., majdi Abdul-Rani, A., Alam, M.A., Beheshti, M. and Taufiqurrahman, I. (2021), "Modelling and optimization of microhardness of electroless Ni-P-TiO2 composite coating based on machine learning approaches and RSM", J. Mater. Res. Technol., 12, 1010-1025. https://doi.org/10.1016/j.jmrt.2021.03.063
  68. Singh, S., Shintre, D., Ransinchung RN, G.D. and Kumar, P. (2018), "Performance of fine RAP concrete containing flyash, silica fume, and bagasse ash", J. Mater. Civil Eng., 30(10), 4018233. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002408.
  69. Souza, A.T., Barbosa, T.F., Riccio, L.A. and dos Santos, W.J. (2020), "Effect of limestone powder substitution on mechanical properties and durability of slender precast components of structural mortar", J. Mater. Res. Technol., 9, 847-856. https://doi.org/10.1016/j.jmrt.2019.11.024.
  70. Stumpf, A. and Kerle, N. (2011), "Object-oriented mapping of landslides using Random Forests", Remote Sensing Environ., 115(10), 2564-2577. https://doi.org/10.1016/j.rse.2011.05.013.
  71. Sun, J., Mo, Y., Chen, Y., Yang, N. and Tang, Y. (2018), "Detection of moisture content of tomato leaves based on dielectric properties and IRIV-GWO-SVR algorithm", Trans. Chinese Soc. Agric. Eng., 34(14), 188-195.
  72. Trigila, A., Iadanza, C., Esposito, C. and Scarascia-Mugnozza, G. (2015), "Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)", Geomorphology, 249, 119-136. https://doi.org/10.1016/j.geomorph.2015.06.001.
  73. Ulsen, C., Tseng, E., Angulo, S.C., Landmann, M., Contessotto, R., Balbo, J.T. and Kahn, H. (2019), "Concrete aggregates properties crushed by jaw and impact secondary crushing", J. Mater. Res. Technol., 8(1), 494-502. https://doi.org/10.1016/j.jmrt.2018.04.008.
  74. Vapnik, V. (2013), The Nature of Statistical Learning Theory, Springer Science & Business Media, New York, USA.
  75. Wang, K., Ren, L. and Yang, L. (2018), "Excellent carbonation behavior of rankinite prepared by calcining the CSH: Potential recycling of waste concrete powders for prefabricated building products", Materials, 11(8), 1474. https://doi.org/10.3390/ma11081474.
  76. Wang, X., Yang, C., Qin, B. and Gui, W. (2005), "Parameter selection of support vector regression based on hybrid optimization algorithm and its application", J. Control Theory Appl., 3(4), 371-376. https://doi.org/10.1007/s11768-005-0026-1.
  77. Xu, J., Chen, Y., Xie, T., Zhao, X., Xiong, B. and Chen, Z. (2019), "Prediction of triaxial behavior of recycled aggregate concrete using multivariable regression and artificial neural network techniques", Constr. Build. Mater., 226, 534-554. https://doi.org/10.1016/j.conbuildmat.2019.07.155.
  78. Yang, C., Feng, H. and Esmaeili-Falak, M. (2022), "Predicting the compressive strength of modified recycled aggregate concrete", Struct. Concr. https://doi.org/10.1002/suco.202100681. (In press).
  79. Yoo, D.Y., Kim, S., Kim, M.J., Kim, D. and Shin, H.O. (2019), "Self-healing capability of asphalt concrete with carbon-based materials", J. Mater. Res. Technol., 8(1), 827-839. https://doi.org/10.1016/j.jmrt.2018.07.001.
  80. Yuan, J., Zhao, M. and Esmaeili-Falak, M. (2022), "A comparative study on predicting the rapid chloride permeability of self-compacting concrete using meta-heuristic algorithm and artificial intelligence techniques", Struct. Concr., 23(2), 753-774. https://doi.org/10.1002/suco.202100682.
  81. Yuvaraj, N., Karthikeyan, T. and Praghash, K. (2021), "An improved task allocation scheme in serverless computing using gray wolf Optimization (GWO) based reinforcement learning (RIL) approach", Wirel Pers Commun., 117, 2403-2421. https://doi.org/10.1007/s11277-020-07981-0.
  82. Zeyad, A.M. and Almalki, A. (2020), "Influence of mixing time and superplasticizer dosage on self-consolidating concrete properties", J. Mater. Res. Technol., 9(3), 6101-6115. https://doi.org/10.1016/j.jmrt.2020.04.013.
  83. Zhang, B., Li, K., Hu, Y., Ji, K. and Han, B. (2022), "Prediction of Backfill Strength Based on Support Vector Regression Improved by Grey Wolf Optimization", J. Shanghai Jiaotong University (Science), 1-9. https://doi.org/10.1007/s12204-022-2408-7.
  84. Zhou, J., Huang, S., Zhou, T., Armaghani, D.J. and Qiu, Y. (2022), "Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential", Artif. Intell. Rev., 1-33. https://doi.org/10.1007/s10462-022-10140-5.
  85. Zhu, L., Zhao, C. and Dai, J. (2021a), "Prediction of compressive strength of recycled aggregate concrete based on gray correlation analysis", Constr. Build. Mater., 273, 121750. https://doi.org/10.1016/j.conbuildmat.2020.121750.
  86. Zhu, W., Huang, L., Mao, L. and Esmaeili-Falak, M. (2021b), "Predicting the uniaxial compressive strength of oil palm shell lightweight aggregate concrete using artificial intelligence-based algorithms", Struct Concr., https://doi.org/10.1002/suco.202100656. (In Press).