Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

An insight into the prediction of mechanical properties of concrete using machine learning techniques

  • Neeraj Kumar Shukla (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Aman Garg (Department of Multidisciplinary Engineering, The NorthCap University) ;
  • Javed Bhutto (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Mona Aggarwal (Department of Multidisciplinary Engineering, The NorthCap University) ;
  • M.Ramkumar Raja (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • Hany S. Hussein (Electrical Engineering Department, College of Engineering, King Khalid University) ;
  • T.M. Yunus Khan (Mechanical Engineering Department, College of Engineering, King Khalid University) ;
  • Pooja Sabherwal (Department of Multidisciplinary Engineering, The NorthCap University)
  • Received : 2023.02.22
  • Accepted : 2023.05.15
  • Published : 2023.09.25
⟨ Previous Next ⟩

Abstract

Experimenting with concrete to determine its compressive and tensile strengths is a laborious and time-consuming operation that requires a lot of attention to detail. Researchers from all around the world have spent the better part of the last several decades attempting to use machine learning algorithms to make accurate predictions about the technical qualities of various kinds of concrete. The research that is currently available on estimating the strength of concrete draws attention to the applicability and precision of the various machine learning techniques. This article provides a summary of the research that has previously been conducted on estimating the strength of concrete by making use of a variety of different machine learning methods. In this work, a classification of the existing body of research literature is presented, with the classification being based on the machine learning technique used by the researchers. The present review work will open the horizon for the researchers working on the machine learning based prediction of the compressive strength of concrete by providing the recommendations and benefits and drawbacks associated with each model as determining the compressive strength of concrete practically is a laborious and time-consuming task.

Keywords

Acknowledgement

The authors gratefully acknowledge their respective organizations for their help and support. The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University (KKU), Kingdom of Saudi Arabia for funding this work through General Research Project under the grant number "RGP1/70/44".

References

  1. Abuodeh, O.R., Abdalla, J.A. and Hawileh, R.A. (2020), "Assessment of compressive strength of ultra-high performance concrete using deep machine learning techniques", Appl. Soft Comput., 95, 106552. https://doi.org/10.1016/j.asoc.2020.10655.
  2. Ahmad, A., Ostrowski, K.A., Maslak, M., Farooq, F., Mehmood, I. and Nafees, A. (2021), "Comparative study of supervised machine learning algorithms for predicting the compressive strength of concrete at high temperature", Mater. (Basel), 14, 4222. https://doi.org/10.3390/ma14154222.
  3. Ahmad, W., Ahmad, A., Ostrowski, K.A., Aslam, F., Joyklad, P. and Zajdel, P. (2021), "Application of advanced machine learning approaches to predict the compressive strength of concrete containing supplementary cementitious materials", Mater. (Basel), 14, 5762. https://doi.org/10.3390/ma14195762.
  4. Al-Gburi, S.N.A., Akpinar, P. and Helwan, A. (2022), "Machine learning in concrete's strength prediction", Comput. Concrete, 29(6), 433-444. https://doi.org/10.12989/cac.2022.29.6.433.
  5. Al-Shamiri, A.K., Kim, J.H., Yuan, T.F. and Yoon, Y.S. (2019), "Modeling the compressive strength of high-strength concrete: An extreme learning approach", Constr. Build. Mater., 208, 204-219. https://doi.org/10.1016/j.conbuildmat.2019.02.165.
  6. Al-Shamiri, A.K., Yuan, T.F. and Kim, J.H. (2020), "Non-tuned machine learning approach for predicting the compressive strength of high-performance concrete", Mater. (Basel), 13, 1023. https://doi.org/10.3390/ma13051023.
  7. Amin, M.N., Iqtidar, A., Khan, K., Javed, M.F., Shalabi, F.I. and Qadir, M.G. (2021), "Comparison of machine learning approaches with traditional methods for predicting the compressive strength of rice husk ash concrete", Crystals, 11, 779. https://doi.org/10.3390/cryst11070779.
  8. Anyaoha, U., Zaji, A. and Liu, Z. (2020), "Soft computing in estimating the compressive strength for high-performance concrete via concrete composition appraisal", Constr. Build. Mater., 257, 119472. https://doi.org/10.1016/j.conbuildmat.2020.119472.
  9. Arora, S., Singh, B. and Bhardwaj, B. (2019) "Strength performance of recycled aggregate concretes containing mineral admixtures and their performance prediction through various modeling techniques", J. Build. Eng., 24, 100741. https://doi.org/10.1016/j.jobe.2019.100741.
  10. Ashrafian, A., Shokri, F., Taheri Amiri, M.J., Yaseen, Z.M. and Rezaie-Balf, M. (2020), "Compressive strength of foamed cellular lightweight concrete simulation: New development of hybrid artificial intelligence model", Constr. Build. Mater., 230, 117048. https://doi.org/10.1016/j.conbuildmat.2019.117048.
  11. Ashrafian, A., Taheri Amiri, M.J., Rezaie-Balf, M., Ozbakkaloglu, T. and Lotfi-Omran, O. (2018), "Prediction of compressive strength and ultrasonic pulse velocity of fiber reinforced concrete incorporating nano silica using heuristic regression methods", Constr. Build. Mater., 190, 479-494. https://doi.org/10.1016/j.conbuildmat.2018.09.047.
  12. Asri, Y. El, Aicha, M. Ben, Zaher, M. and Alaoui, A.H. (2022), "Prediction of compressive strength of self-compacting concrete using four machine learning technics", Mater. Today Proc., 57, 859-866. https://doi.org/10.1016/j.matpr.2022.02.487.
  13. Asteris, P.G., Skentou, A.D., Bardhan, A., Samui, P. and Pilakoutas, K. (2021), "Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models", Cement Concrete Res., 145, 106449. https://doi.org/10.1016/j.cemconres.2021.106449.
  14. Ayaz, Y., Kocamaz, A.F. and Karakoc, M.B. (2015), "Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers", Constr. Build. Mater., 94, 235-240. https://doi.org/10.1016/j.conbuildmat.2015.06.029.
  15. 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.
  16. Babanajad, S.K., Gandomi, A.H. and Alavi, A.H. (2017), "New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach", Adv. Eng. Softw., 110, 55-68. https://doi.org/10.1016/j.advengsoft.2017.03.011.
  17. Babanajad, S.K., Gandomi, A.H., Mohammadzadeh S.D. and Alavi, A.H. (2013), "Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming", Autom. Constr., 36, 136-144. https://doi.org/10.1016/j.autcon.2013.08.016.
  18. Behnood, A., Behnood, V., Modiri Gharehveran, M. and Alyamac, K.E. (2017), "Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm", Constr. Build. Mater., 142, 199-207. https://doi.org/10.1016/j.conbuildmat.2017.03.061.
  19. Behnood, A. and Golafshani, E.M. (2020), "Machine learning study of the mechanical properties of concretes containing waste foundry sand", Constr. Build. Mater., 243, 118152. https://doi.org/10.1016/j.conbuildmat.2020.118152.
  20. Behnood, A., Olek, J. and Glinicki, M.A. (2015), "Predicting modulus elasticity of recycled aggregate concrete using M5' model tree algorithm", Constr. Build. Mater., 94, 137-147. https://doi.org/10.1016/j.conbuildmat.2015.06.055.
  21. 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.
  22. Chaabene, W.B., Flah, M. and Nehdi, M.L. (2020), "Machine learning prediction of mechanical properties of concrete: Critical review", Constr. Build. Mater., 260, 119889. https://doi.org/10.1016/j.conbuildmat.2020.119889.
  23. Candelaria, M.D.E., Kee, S.H. and Lee, K.S. (2022), "Prediction of compressive strength of partially saturated concrete using machine learning methods", Mater. (Basel), 15, 1662. https://doi.org/10.3390/ma15051662.
  24. Castelli, M., Vanneschi, L. and Silva, S. (2013), "Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators", Expert Syst. Appl., 40, 6856-6862. https://doi.org/10.1016/j.eswa.2013.06.037.
  25. Chakraborty, D., Awolusi, I. and Gutierrez, L. (2021), "An explainable machine learning model to predict and elucidate the compressive behavior of high-performance concrete", Results Eng., 11, 100245. https://doi.org/10.1016/j.rineng.2021.100245.
  26. Chen, L., Kou, C.H. and Ma, S.W. (2014), "Prediction of slump flow of high-performance concrete via parallel hyper-cubic gene-expression programming", Eng. Appl. Artif. Intell., 34, 66-74. https://doi.org/10.1016/j.engappai.2014.05.005.
  27. Chen, Y., Lu, C., Fan, W., Feng, J. and Sareh, P. (2023), "Data-driven design and morphological analysis of conical six-fold origami structures", Thin Wall. Struct., 185, 110626. https://doi.org/10.1016/j.tws.2023.110626.
  28. Chithra, S., Kumar, S.R.R.S., Chinnaraju, K. and Alfin Ashmita, F. (2016), "A comparative study on the compressive strength prediction models for high performance concrete containing nano silica and copper slag using regression analysis and artificial neural networks", Constr. Build. Mater., 114, 528-535. https://doi.org/10.1016/j.conbuildmat.2016.03.214.
  29. Chopra, P., Sharma, R.K., Kumar, M. and Chopra, T. (2018), "Comparison of machine learning techniques for the prediction of compressive strength of concrete", Adv. Civil Eng., 2018, 1-9. https://doi.org/10.1155/2018/5481705.
  30. Chou, J.S. and Pham, A.D. (2013), "Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength", Constr. Build. Mater., 49, 554-563. https://doi.org/10.1016/j.conbuildmat.2013.08.078.
  31. Chou, J.S., Tsai, C.F., Pham, A.D. and Lu, Y.H. (2014), "Machine learning in concrete strength simulations: Multi-nation data analytics", Constr. Build. Mater., 73, 771-780. https://doi.org/10.1016/j.conbuildmat.2014.09.054.
  32. Cook, R., Lapeyre, J., Ma, H. and Kumar, A. (2019), "Prediction of compressive strength of concrete: Critical comparison of performance of a hybrid machine learning model with standalone models", J. Mater. Civil Eng., 31, 4019255. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002902.
  33. Dabiri, H., Kioumarsi, M., Kheyroddin, A., Kandiri, A. and Sartipi, F. (2022), "Compressive strength of concrete with recycled aggregate; A machine learning-based evaluation", Clean. Mater., 3, 100044. https://doi.org/10.1016/j.clema.2022.100044.
  34. Dai, L., Wu, X., Zhou, M., Ahmad, W., Ali, M., Sabri, M.M.S., Salmi, A. and Ewais, D.Y.Z. (2022), "Using machine learning algorithms to estimate the compressive property of high strength fiber reinforced concrete", Mater. (Basel)., 15, 4450. https://doi.org/10.3390/ma15134450.
  35. de-Prado-Gil, J., Palencia, C., Silva-Monteiro, N. and MartinezGarcia, R. (2022), "To predict the compressive strength of self compacting concrete with recycled aggregates utilizing ensemble machine learning models", Case Stud. Constr. Mater., 16, e01046. https://doi.org/10.1016/j.cscm.2022.e01046.
  36. Deng, F., He, Y., Zhou, S., Yu, Y., Cheng, H. and Wu, X. (2018), "Compressive strength prediction of recycled concrete based on deep learning", Constr. Build. Mater., 175, 562-569. https://doi.org/10.1016/j.conbuildmat.2018.04.169.
  37. DeRousseau, M.A., Laftchiev, E., Kasprzyk, J.R., Rajagopalan, B. and Srubar, W.V. (2019), "A comparison of machine learning methods for predicting the compressive strength of field-placed concrete", Constr. Build. Mater., 228, 116661. https://doi.org/10.1016/j.conbuildmat.2019.08.042.
  38. Duan, Z.H., Kou, S.C. and Poon, C.S. (2013), "Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete", Constr. Build. Mater., 44, 524-532. https://doi.org/10.1016/j.conbuildmat.2013.02.064.
  39. Dutta, S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput. Concrete, 21(4), 463-470. https://doi.org/10.12989/cac.2018.21.4.463.
  40. Erdal, H.I. (2013), "Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction", Eng. Appl. Artif. Intell., 26, 1689-1697. https://doi.org/10.1016/j.engappai.2013.03.014.
  41. Eskandari-Naddaf, H. and Kazemi, R. (2017) "ANN prediction of cement mortar compressive strength, influence of cement strength class", Constr. Build. Mater., 138, 1-11. https://doi.org/10.1016/j.conbuildmat.2017.01.132.
  42. Fan, W., Chen, Y., Li, J., Sun, Y., Feng, J., Hassanin, H. and Sareh, P. (2021), "Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications", Struct., 33, 3954-3963. https://doi.org/10.1016/j.istruc.2021.06.110.
  43. Falah, M.W., Hussein, S.H., Saad, M.A., Ali, Z.H., Tran, T.H., Ghoniem, R.M. and Ewees, A.A. (2022), "Compressive strength prediction using coupled deep learning model with extreme gradient boosting algorithm: Environmentally friendly concrete incorporating recycled aggregate", Complex., 2022, 1-22. https://doi.org/10.1155/2022/5433474.
  44. Feng, D.C., Liu, Z.T., Wang, X.D., Chen, Y., Chang, J.Q., Wei, D.F. and Jiang, Z.M. (2020), "Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach", Constr. Build. Mater., 230, 117000. https://doi.org/10.1016/j.conbuildmat.2019.117000.
  45. Feng, D.C., Xie, S.C., Deng, W.N. and Ding, Z.D. (2019), "Probabilistic failure analysis of reinforced concrete beam-column sub-assemblage under column removal scenario", Eng. Fail. Anal., 100, 381-392. https://doi.org/10.1016/j.engfailanal.2019.02.004.
  46. Garg, A., Aggarwal, P., Aggarwal, Y., Belarbi, M.O., Chalak, H.D., Tounsi, A. and Gulia, R. (2022a), "Machine learning models for predicting the compressive strength of concrete containing nano silica", Comput. Concrete, 30(1), 33-42. https://doi.org/10.12989/cac.2022.30.1.033.
  47. Garg, A., Belarbi, M., Tounsi, A., Li, L., Singh, A. and Mukhopadhyay, T. (2022b), "Predicting elemental stiffness matrix of FG nanoplates using Gaussian process regression based surrogate model in framework of layerwise model", Eng. Anal. Bound. Elem., 143, 779-795. https://doi.org/10.1016/j.enganabound.2022.08.001.
  48. Garg, A., Mukhopadhyay, T., Belarbi, M.O., Chalak, H.D., Singh, A. and Zenkour, A.M. (2023a), "On accurately capturing the through-thickness variation of transverse shear and normal stresses for composite beams using FSDT coupled with GPR", Compos. Struct., 305, 116551. https://doi.org/10.1016/j.compstruct.2022.116551.
  49. Garg, A., Mukhopadhyay, T., Belarbi, M.O. and Li, L. (2023b), "Random forest-based surrogates for transforming the behavioral predictions of laminated composite plates and shells from FSDT to elasticity solutions", Compos. Struct., 309, 116756. https://doi.org/10.1016/j.compstruct.2023.116756.
  50. Getahun, M.A., Shitote, S.M. and Gariy, Z.C.A. (2018), "Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes", Constr. Build. Mater., 190, 517-525. https://doi.org/10.1016/j.conbuildmat.2018.09.097.
  51. Ghanizadeh, A.R., Abbaslou, H., Amlashi, A.T. and Alidoust, P. (2019), "Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine", Front. Struct. Civil Eng., 13, 215-239. https://doi.org/10.1007/s11709-018-0489-z.
  52. Gholampour, A., Gandomi, A.H. and Ozbakkaloglu, T. (2017), "New formulations for mechanical properties of recycled aggregate concrete using gene expression programming", Constr. Build. Mater., 130, 122-145. https://doi.org/10.1016/j.conbuildmat.2016.10.114.
  53. Gholampour, A., Gandomi, A.H., Ozbakkaloglu, T. and Xie, T. (2021), "Corrigendum to "New formulations for mechanical properties of recycled aggregate concrete using gene expression programming" [Constr. Build. Mater., 130(2017), 122-145]", Constr. Build. Mater., 278, 122930. https://doi.org/10.1016/j.conbuildmat.2021.122930.
  54. Golafshani, E.M. and Ashour, A. (2016), "Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques", Autom. Constr., 64, 7-19. https://doi.org/10.1016/j.autcon.2015.12.026.
  55. Golafshani, E.M. and Behnood, A. (2018), "Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete", Appl. Soft Comput., 64, 377-400. https://doi.org/10.1016/j.asoc.2017.12.030.
  56. 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.
  57. Hadzima-Nyarko, M., Nyarko, E.K., Lu, H. and Zhu, S. (2020) "Machine learning approaches for estimation of compressive strength of concrete", Eur. Phys. J. Plus, 135, 682. https://doi.org/10.1140/epjp/s13360-020-00703-2.
  58. Hameed, M.M., Abed, M.A., Al-Ansari, N. and Alomar, M.K. (2022), "Predicting compressive strength of concrete containing industrial waste materials: Novel and hybrid machine learning model", Adv. Civil Eng., 2022, 1-19. https://doi.org/10.1155/2022/5586737.
  59. 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.
  60. Han, B., Wu, Y. and Liu, L. (2022), "Prediction and uncertainty quantification of compressive strength of high-strength concrete using optimized machine learning algorithms", Struct. Concrete, 23, 3772-3785. https://doi.org/10.1002/suco.202100732.
  61. Han, Q., Gui, C., Xu, J. and Lacidogna, G. (2019), "A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm", Constr. Build. Mater., 226, 734-742. https://doi.org/10.1016/j.conbuildmat.2019.07.315.
  62. Han, T., Siddique, A., Khayat, K., Huang, J. and Kumar, A. (2020), "An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete", Constr. Build. Mater., 244, 118271. https://doi.org/10.1016/j.conbuildmat.2020.118271.
  63. Iqtidar, A., Bahadur Khan, N., Kashif-ur-Rehman, S., Faisal Javed, M., Aslam, F., Alyousef, R., Alabduljabbar, H. and Mosavi, A. (2021), "Prediction of compressive strength of rice husk ash concrete through different machine learning processes", Crystals, 11, 352. https://doi.org/10.3390/cryst11040352.
  64. 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.2017.01.120.
  65. Jia, H., Qiao, G. and Han, P. (2022), "Machine learning algorithms in the environmental corrosion evaluation of reinforced concrete structures - A review", Cement Concrete Compos., 133, 104725. https://doi.org/10.1016/j.cemconcomp.2022.104725.
  66. Kaloop, M.R., Kumar, D., Samui, P., Hu, J.W. and Kim, D. (2020), "Compressive strength prediction of high-performance concrete using gradient tree boosting machine", Constr. Build. Mater., 264, 120198. https://doi.org/10.1016/j.conbuildmat.2020.120198.
  67. Kandiri, A., Mohammadi Golafshani, E. and Behnood, A. (2020), "Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm", Constr. Build. Mater., 248, 118676. https://doi.org/10.1016/j.conbuildmat.2020.118676.
  68. 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, 485. https://doi.org/10.3390/app11020485.
  69. Kang, M.C., Yoo, D.Y. and Gupta, R. (2021), "Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete", Constr. Build. Mater., 266, 121117. https://doi.org/10.1016/j.conbuildmat.2020.121117.
  70. Ke, X. and Duan, Y. (2021), "A Bayesian machine learning approach for inverse prediction of high-performance concrete ingredients with targeted performance", Constr. Build. Mater., 270, 121424. https://doi.org/10.1016/j.conbuildmat.2020.121424.
  71. Khan, K., Ahmad, W., Amin, M.N. and Ahmad, A. (2022a), "A systematic review of the research development on the application of machine learning for concrete", Mater. (Basel), 15, 4512. https://doi.org/10.3390/ma15134512.
  72. Khan, K., Ahmad, W., Amin, M.N., Aslam, F., Ahmad, A. and Al-Faiad, M.A. (2022b), "Comparison of prediction models based on machine learning for the compressive strength estimation of recycled aggregate concrete", Mater. (Basel), 15, 3430. https://doi.org/10.3390/ma15103430.
  73. Kim, H., Ahn, E., Shin, M. and Sim, S.H. (2019), "Crack and noncrack classification from concrete surface images using machine learning", Struct. Heal. Monit., 18, 725-738. https://doi.org/10.1177/1475921718768747.
  74. Kovacevic, M., Lozancic, S., Nyarko, E.K. and Hadzima-Nyarko, M. (2021), "Modeling of compressive strength of self-compacting rubberized concrete using machine learning", Mater. (Basel), 14, 4346. https://doi.org/10.3390/ma14154346.
  75. Koya, B.P., Aneja, S., Gupta, R., Valeo, C. (2022), "Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete", Mech. Adv. Mater. Struct., 29, 4032-4043. https://doi.org/10.1080/15376494.2021.1917021.
  76. Kumar, A., Arora, H.C., Kapoor, N.R., Mohammed, M.A., Kumar, K., Majumdar, A. and Thinnukool, O. (2022), "Compressive strength prediction of lightweight concrete: Machine learning models", Sustainab., 14, 2404. https://doi.org/10.3390/su14042404.
  77. Li, Z. and Yan, G. (2022), "Machine learning for structural stability: Predicting dynamics responses using physics-informed neural networks", Comput. Concrete, 29(6), 419-432. https://doi.org/10.12989/cac.2022.29.6.419.
  78. Li, Y., Zhang, Q., Kaminski, P., Deifalla, A.F., Sufian, M., Dyczko, A., Kahla, N.B. and Atig, M. (2022), "Compressive strength of steel fiber-reinforced concrete employing supervised machine learning techniques", Mater. (Basel), 15, 4209. https://doi.org/10.3390/ma15124209.
  79. Liang, C., Qian, C., Chen, H. and Kang, W. (2018), "Prediction of compressive strength of concrete in wet-dry environment by bp artificial neural networks", Adv. Mater. Sci. Eng., 2018, 1-11. https://doi.org/10.1155/2018/6204942.
  80. Liang, M., Chang, Z., Wan, Z., Gan, Y., Schlangen, E. and Savija, B. (2022), "Interpretable ensemble-machine-learning models for predicting creep behavior of concrete", Cement Concrete Compos., 125, 104295. https://doi.org/10.1016/j.cemconcomp.2021.104295.
  81. Lim, D.K., Mustapha, K.B. and Pagwiwoko, C.P. (2021), "Delamination detection in composite plates using random forests", Compos. Struct., 278, 114676. https://doi.org/10.1016/j.compstruct.2021.114676.
  82. Ling, H., Qian, C., Kang, W., Liang, C. and Chen, H. (2019), "Combination of support vector machine and K-fold cross validation to predict compressive strength of concrete in marine environment", Constr. Build. Mater., 206, 355-363. https://doi.org/10.1016/j.conbuildmat.2019.02.071.
  83. Liu, Y. (2022), "High-performance concrete strength prediction based on machine learning", Comput. Intell. Neurosci., 2022, 1-7. https://doi.org/10.1155/2022/5802217.
  84. Loh, W.Y. (2011), "Classification and regression trees", WIREs Data Min. Knowl. Discov., 1, 14-23. https://doi.org/10.1002/widm.8.
  85. Lyngdoh, G.A., Zaki, M., Krishnan, N.M.A. and Das, S. (2022), "Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning", Cement Concrete Compos., 128, 104414. https://doi.org/10.1016/j.cemconcomp.2022.104414.
  86. Miladirad, K., Golafshani, E.M., Safehian, M. and Sarkar, A. (2021), "Modeling the mechanical properties of rubberized concrete using machine learning methods", Comput. Concrete, 28(6), 567-583. https://doi.org/10.12989/cac.2021.28.6.567.
  87. Mirzahosseini, M., Jiao, P., Barri, K., Riding, K.A. and Alavi, A.H. (2019), "New machine learning prediction models for compressive strength of concrete modified with glass cullet", Eng. Comput., 36, 876-898. https://doi.org/10.1108/EC-08-2018-0348.
  88. Mohammed, A., Rafiq, S., Sihag, P., Kurda, R., Mahmood, W., Ghafor, K. and Sarwar, W. (2020), "ANN, M5P-tree and nonlinear regression approaches with statistical evaluations to predict the compressive strength of cement-based mortar modified with fly ash", J. Mater. Res. Technol., 9, 12416-12427. https://doi.org/10.1016/j.jmrt.2020.08.083.
  89. Mohtasham Moein, M., Saradar, A., Rahmati, K., Ghasemzadeh Mousavinejad, S.H., Bristow, J., Aramali, V. and Karakouzian, M. (2023), "Predictive models for concrete properties using machine learning and deep learning approaches: A review", J. Build. Eng., 63, 105444. https://doi.org/10.1016/j.jobe.2022.105444.
  90. Naderpour, H., Rafiean, A.H. and Fakharian, P. (2018), "Compressive strength prediction of environmentally friendly concrete using artificial neural networks", J. Build. Eng., 16, 213-219. https://doi.org/10.1016/j.jobe.2018.01.007.
  91. Nguyen-Sy, T., Wakim, J., To, Q.D., Vu, M.N., Nguyen, T.D. and Nguyen, T.T. (2020), "Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method", Constr. Build. Mater., 260, 119757. https://doi.org/10.1016/j.conbuildmat.2020.119757.
  92. Nguyen, H., Vu, T., Vo, T.P. and Thai, H.T. (2021), "Efficient machine learning models for prediction of concrete strengths", Constr. Build. Mater., 266, 120950. https://doi.org/10.1016/j.conbuildmat.2020.120950.
  93. Oliver, J., Huespe, A.E., Samaniego, E. and Chaves, E.W.V. (2004), "Continuum approach to the numerical simulation of material failure in concrete", Int. J. Numer. Anal. Method. Geomech., 28, 609-632. https://doi.org/10.1002/nag.365.
  94. Ozcan, G., Kocak, Y. and Gulbandilar E. (2017), "Estimation of compressive strength of BFS and WTRP blended cement mortars with machine learning models", Comput. Concrete, 19(3), 275-282. https://doi.org/10.12989/cac.2017.19.3.275.
  95. Penido, R.E.K., da Paixao, R.C.F., Costa, L.C.B., Peixoto, R.A.F., Cury, A.A. and Mendes, J.C. (2022), "Predicting the compressive strength of steelmaking slag concrete with machine learning - Considerations on developing a mix design tool", Constr. Build. Mater., 341, 127896. https://doi.org/10.1016/j.conbuildmat.2022.127896.
  96. 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., 24, 14965-14980. https://doi.org/10.1007/s00500-020-04848-1.
  97. Probst, P., Wright, M.N. and Boulesteix, A.L. (2019), "Hyperparameters and tuning strategies for random forest", WIREs Data Min. Knowl. Discov., 9, e1301. https://doi.org/https://doi.org/10.1002/widm.1301.
  98. Quinlan, J.R. (1992), "Learning with continuous classes", 5th Australian Joint Conference on Artificial Intelligence, Tasmania, Austailia, November.
  99. Rajakarunakaran, S.A., Lourdu, A.R., Muthusamy, S., Panchal, H., Jawad Alrubaie, A., Musa Jaber, M., Ali, M.H., Tlili, I., Maseleno, A., Majdi, A. and Ali, S.H.M. (2022), "Prediction of strength and analysis in self-compacting concrete using machine learning based regression techniques", Adv. Eng. Softw., 173, 103267. https://doi.org/10.1016/j.advengsoft.2022.103267.
  100. Rao, Z., Tung, P.Y., Xie, R., Wei, Y., Zhang, H., Ferrari, A., Fritz Kormann, T.P.C.K., Sukumar, P.T., Silva, A.K.D., Chen, Y., Li, Z., Ponge, D., Neugebauer, J., Gutfleisch, O., Bauer, S. and Raabe, D. (2022), "Machine learning-enabled high-entropy alloy discovery", Sci., 378, 78-85. https://doi.org/10.1126/science.abo4940.
  101. Salami, B.A., Iqbal, M., Abdulraheem, A., Jalal, F.E., Alimi, W., Jamal, A., Tafsirojjaman, T., Liu, Y. and Bardhan, A. (2022), "Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches", Cement Concrete Compos., 133, 104721. https://doi.org/10.1016/j.cemconcomp.2022.104721.
  102. 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.
  103. Shah, M.I., Javed, M.F., Aslam, F. and Alabduljabbar, H. (2022), "Machine learning modeling integrating experimental analysis for predicting the properties of sugarcane bagasse ash concrete", Constr. Build. Mater., 314, 125634. https://doi.org/10.1016/j.conbuildmat.2021.125634.
  104. Shamsabadi, E., Roshan, N., Hadigheh, S.A., Nehdi, M.L., Khodabakhshian, A. and Ghalehnovi, M. (2022), "Machine learning-based compressive strength modelling of concrete incorporating waste marble powder", Constr. Build. Mater., 324, 126592. https://doi.org/10.1016/j.conbuildmat.2022.126592.
  105. Shang, M., Li, H., Ahmad, A., Ahmad, W., Ostrowski, K.A., Aslam, F., Joyklad, P., Majka, T.M. (2022), "Predicting the mechanical properties of RCA-based concrete using supervised machine learning algorithms", Mater. (Basel), 15, 647. https://doi.org/10.3390/ma15020647.
  106. Sharma, N., Thakur, M.S., Sihag, P., Malik, M.A., Kumar, R., Abbas, M. and Saleel, C.A. (2022), "Machine learning techniques for evaluating concrete strength with waste marble powder", Mater. (Basel), 15, 5811. https://doi.org/10.3390/ma15175811.
  107. Shen, Z., Deifalla, A.F., Kaminski, P. and Dyczko, A. (2022), "Compressive strength evaluation of ultra-high-strength concrete by machine learning", Mater. (Basel), 15, 3523. https://doi.org/10.3390/ma15103523.
  108. Shiuly, A., Hazra, T., Sau, D. and Maji, D. (2022), "Performance and optimisation study of waste plastic aggregate based sustainable concrete - A machine learning approach", Clean. Waste Syst., 2, 100014. https://doi.org/10.1016/j.clwas.2022.100014.
  109. Song, H., Ahmad, A., Farooq, F., Ostrowski, K.A., Maslak, M., Czarnecki, S. and Aslam, F. (2021) "Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms", Constr. Build. Mater., 308, 125021. https://doi.org/10.1016/j.conbuildmat.2021.125021.
  110. Song, Y., Zhao, J., Ostrowski, K.A., Javed, M.F., Ahmad, A., Khan, M.I., Aslam, F. and Kinasz, R. (2021), "Prediction of compressive strength of fly-ash-based concrete using ensemble and non-ensemble supervised machine-learning approaches", Appl. Sci., 12, 361. https://doi.org/10.3390/app12010361.
  111. Tenza-Abril, A.J., Villacampa, Y., Solak, A.M. and Baeza-Brotons, F. (2018), "Prediction and sensitivity analysis of compressive strength in segregated lightweight concrete based on artificial neural network using ultrasonic pulse velocity", Constr. Build. Mater., 189, 1173-1183. https://doi.org/10.1016/j.conbuildmat.2018.09.096.
  112. Cihan, M.T. (2019), "Prediction of concrete compressive strength and slump by machine learning methods", Adv. Civil Eng., 2019, 1-11. https://doi.org/10.1155/2019/3069046.
  113. Tran, V.Q., Quoc Dang, V. and Si Ho, L. (2022), "Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach", Constr. Build. Mater., 323, 126578. https://doi.org/10.1016/j.conbuildmat.2022.126578.
  114. Ullah, H.S., Khushnood, R.A., Ahmad, J. and Farooq, F. (2022a), "Predictive modelling of sustainable lightweight foamed concrete using machine learning novel approach", J. Build. Eng., 56, 104746. https://doi.org/10.1016/j.jobe.2022.104746.
  115. Ullah, H.S., Khushnood, R.A., Farooq, F., Ahmad, J., Vatin, N.I. and Ewais, D.Y.Z. (2022b), "Prediction of compressive strength of sustainable foam concrete using individual and ensemble machine learning approaches", Mater. (Basel), 15, 3166. https://doi.org/10.3390/ma15093166.
  116. Unlu, R. (2020), "An assessment of machine learning models for slump flow and examining redundant features", Comput. Concrete, 25(6), 565-574. https://doi.org/10.12989/cac.2020.25.6.565.
  117. Wan, Z., Xu, Y. and Savija, B. (2021), "On the use of machine learning models for prediction of compressive strength of concrete: influence of dimensionality reduction on the model performance", Mater. (Basel), 14, 713. https://doi.org/10.3390/ma14040713.
  118. Wangler, T., Roussel, N., Bos, F.P., Salet, T.A.M. and Flatt, R.J. (2019), "Digital concrete: A review", Cement Concrete Res., 123, 105780. https://doi.org/10.1016/j.cemconres.2019.105780.
  119. Xu, J., Zhao, X., Yu, Y., Xie, T., Yang, G. and Xue, J. (2019), "Parametric sensitivity analysis and modelling of mechanical properties of normal- and high-strength recycled aggregate concrete using grey theory, multiple nonlinear regression and artificial neural networks", Constr. Build. Mater., 211, 479-491. https://doi.org/10.1016/j.conbuildmat.2019.03.234.
  120. Xu, Y., Ahmad, W., Ahmad, A., Ostrowski, K.A., Dudek, M., Aslam, F. and Joyklad, P. (2021), "Computation of high-performance concrete compressive strength using standalone and ensembled machine learning techniques", Mater. (Basel), 14, 7034. https://doi.org/10.3390/ma14227034.
  121. Yaseen, Z.M., Deo, R.C., Hilal, A., Abd, A.M., Bueno, L.C., Salcedo-Sanz, S. and 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.
  122. Young, B.A., Hall, A., Pilon, L., Gupta, P. and Sant, G. (2019), "Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods", Cement Concrete Res., 115, 379-388. https://doi.org/10.1016/j.cemconres.2018.09.006.
  123. 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", Constr. Build. Mater., 184, 229-247. https://doi.org/10.1016/j.conbuildmat.2018.06.219.
  124. Yuan, X., Tian, Y., Ahmad, W., Ahmad, A., Usanova, K.I., Mohamed, A.M. and Khallaf, R. (2022), "Machine learning prediction models to evaluate the strength of recycled aggregate concrete", Mater. (Basel), 15, 2823. https://doi.org/10.3390/ma15082823.
  125. Yuan, Z., Wang, L.N. and Ji, X. (2014), "Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS", Adv. Eng. Softw., 67, 156-163. https://doi.org/10.1016/j.advengsoft.2013.09.004.
  126. 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.
  127. Zhang, J., Ma, G., Huang, Y., Sun, J., Aslani, F. and Nener, B. (2019), "Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression", Constr. Build. Mater., 210, 713-719. https://doi.org/10.1016/j.conbuildmat.2019.03.189.
  128. Zhang, P., Fan, W., Chen, Y., Feng, J. and Sareh, P. (2022), "Structural symmetry recognition in planar structures using convolutional neural networks", Eng. Struct., 260, 114227. https://doi.org/10.1016/j.engstruct.2022.114227.
  129. Zhou, Q., Wang, F. and Zhu, F. (2016), "Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neuro-fuzzy inference systems", Constr. Build. Mater., 125, 417-426. https://doi.org/10.1016/j.conbuildmat.2016.08.064.
  130. Ziolkowski, P. and Niedostatkiewicz, M. (2019), "Machine learning techniques in concrete mix design", Mater. (Basel), 12, 1256. https://doi.org/10.3390/ma12081256.