Advances in concrete construction

  • 제20권4호
  • /
  • Pages.247-280
  • /
  • 2025
  • /
  • 2287-5301(pISSN)
  • /
  • 2287-531X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

An in-depth exploration of machine learning models for concrete compressive strength and reinforced concrete beam behaviour: A comprehensive review

  • Saurabh Dubey (Department of Civil Engineering, National Institute of Technology Arunachal Pradesh) ;
  • Deepak Gupta (Department of Computer Science & Engineering, Motilal Nehru National Institute of Technology Allahabad) ;
  • Mainak Mallik (Department of Civil Engineering, National Institute of Technology Arunachal Pradesh) ;
  • Barenya Bikash Hazarika ( Faculty of Computer Technology, Assam down town University)
  • 투고 : 2024.02.04
  • 심사 : 2025.07.20
  • 발행 : 2025.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

The mechanical characteristics of concrete, encompassing essential factors such as Compressive Strength, Deflection, Bond Strength, and Shear Strength, are of paramount significance in the design of structurally sound Reinforced Concrete elements. Notably, the assessment of beam deflection is particularly critical for serviceability concerns; however, its accurate determination often requires rigorous analysis or destructive testing. Predicting these vital parameters based on the available test data holds immense value for the design community. Traditional empirical and statistical models, including linear and non-linear regression, have long been employed for this purpose. However, they can be time consuming and prone to errors owing to the variability in factors such as reinforcement, concrete properties, design mix ratios, and curing conditions. To overcome these complexities, researchers have proposed the utilization of Machine Learning algorithms, such as Artificial Neural Networks, Support Vector Machines, Decision Trees, Random Forest, for the prediction of mechanical properties in Reinforced Concrete Beams. The input datasets employed to train these models encompass a range of critical variables, including cement, mineral admixture, Coarse Aggregate, Water/Cement Ratio, Fine Aggregate, Superplasticizer, Slump Value, concrete age, Reinforcement, Reinforcement spacing, Reinforcement, Reinforcement Tensile strength, number of Reinforcement, and Concrete Cover. This study conducts a comprehensive assessment of the applicability and performance of each model, yielding practical recommendations, identifying current knowledge gaps, and delineating potential directions for future research endeavors in this domain.

키워드

과제정보

The authors express their sincere gratitude to the National Institute of Technology, Arunachal Pradesh, for providing the necessary infrastructure and support for this study. Special thanks to the Department of Civil Engineering for their continued guidance and encouragement throughout this study. The authors also extend their appreciation to Motilal Nehru National Institute of Technology (MNNIT) Allahabad and Assam Down Town University, Guwahati, for their valuable support and collaboration in this review.

참고문헌

  1. Abd, A.M. and Abd, S.M. (2017), "Modelling the strength of lightweight foamed concrete using support vector machine (SVM)", Case Stud. Constr. Mater., 6, 8-15. https://doi.org/10.1016/j.cscm.2016.11.002
  2. Aggarwal, Y. (2007), "Modeling of reinforcement in concrete beams using machine learning tools. World Academy of Science", Eng. Technol., 32, 130-131. https://doi.org/10.5281/zenodo.1082405
  3. Ahmad, A., Ostrowski, K.A., Maślak, M., Farooq, F., Mehmood, I. and Nafees, A. (2021a), "Comparative study of supervised machine learning algorithms for predicting the compressive strength of concrete at high temperature", Materials, 14(15), p. 4222. https://doi.org/10.3390/ma14154222
  4. Ahmad, W., Ahmad, A., Ostrowski, K.A., Aslam, F., Joyklad, P. and Zajdel, P. (2021b), "Application of advanced machine learning approaches to predict the compressive strength of concrete containing supplementary cementitious materials", Materials, 14(19), p. 5762. https://doi.org/10.3390/ma14195762
  5. Aiyer, B.G., Kim, D., Karingattikkal, N., Samui, P. and Rao, P.R. (2014), "Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine", KSCE J. Civil Eng., 18(6), 1753-1758. https://doi.org/10.1007/s12205-014-0524-0
  6. Akande, K.O., Owolabi, T.O., Twaha, S. and Olatunji, S.O. (2014), "Performance comparison of SVM and ANN in predicting compressive strength of concrete", IOSR J. Comput. Eng., 16(5), 88-94. https://doi.org/10.9790/0661-16518894
  7. Akpinar, P. and Khashman, A. (2017), "Intelligent classification system for concrete compressive strength", Procedia Comput. Sci., 120, 712-718. https://doi.org/10.1016/j.procs.2017.11.300
  8. Alam, M.S., Sultana, N. and Hossain, S.Z. (2021), "Bayesian optimization algorithm-based support vector regression analysis for estimation of shear capacity of FRP reinforced concrete members", Appl. Soft Comput., 105, p. 107281. https://doi.org/10.1016/j.asoc.2021.107281
  9. Albuthbahak, O.M. and Hiswa, A.A. (2019), "Prediction of concrete compressive strength using supervised machine learning models through ultrasonic pulse velocity and mix parameters", Revista Romana de Materiale, 49(2), 232-243.
  10. 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
  11. 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", Materials, 13(5), p. 1023. https://doi.org/10.3390/ma13051023
  12. Armaghani, D.J. and Asteris, P.G. (2021), "A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength", Neural Comput. Applicat., 33(9), 4501-4532. https://doi.org/10.1007/s00521-020-05244-4
  13. Asteris, P.G., Koopialipoor, M., Armaghani, D.J., Kotsonis, E.A. and Lourenço, P.B. (2021), "Prediction of cement-based mortars compressive strength using machine learning techniques", Neural Comput. Applicat., 33(19), 13089-13121. https://doi.org/10.1007/s00521-021-06004-8
  14. 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, p. 106449. https://doi.org/10.1016/j.cemconres.2021.106449
  15. Awoyera, P.O., Kirgiz, M.S., Viloria, A. and Ovallos-Gazabon, D. (2020), "Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques", J. Mater. Res. Technol., 9(4), 9016-9028. https://doi.org/10.1016/j.jmrt.2020.06.008
  16. Awwad, M.T. (2014), "Developing a forecasting model of concrete compressive strength using relevance vector machines", Int. J. Eng. Technol., 3(2), p. 224. https://doi.org/10.14419/ijet. v3i2.2011
  17. Bakouregui, A.S., Mohamed, H.M., Yahia, A. and Benmokrane, B. (2021), "Explainable extreme gradient boosting tree-based prediction of load-carrying capacity of FRP-RC columns", Eng. Struct., 245, p. 112836. https://doi.org/10.1016/j.engstruct.2021.112836
  18. Basaran, B., Kalkan, I., Bergil, E. and Erdal, E. (2021), "Estimation of the FRP-concrete bond strength with code formulations and machine learning algorithms", Compos. Struct., 268, p. 113972. https://doi.org/10.1016/j.compstruct.2021.113972
  19. Behnood, A., Olek, J. and Glinicki, M.A. (2015a), "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
  20. Behnood, A., Verian, K.P. and Gharehveran, M.M. (2015b), "Evaluation of the splitting tensile strength in plain and steel fiber-reinforced concrete based on the compressive strength", Constr. Build. Mater., 98, 519-529. https://doi.org/10.1016/j.conbuildmat.2015.08.124
  21. Beljkaš, Ž. and Baša, N. (2021), "Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement", Appl. Sci., 11(8), p. 3429. https://doi.org/10.3390/app11083429
  22. Borah, P. and Gupta, D. (2020), "Unconstrained convex minimization based implicit Lagrangian twin extreme learning machine for classification (ULTELMC)", Appl. Intell., 50(4), 1327-1344. https://doi.org/10.1007/s10489-019-01596-0
  23. Breiman, L. (2001), "Random forests", Mach. Learn., 45, 5-32. https://doi.org/10.1023/A:1010933404324
  24. Cascardi, A., Micelli, F. and Aiello, M.A. (2017), "An Artificial Neural Networks model for the prediction of the compressive strength of FRP-confined concrete circular columns", Eng. Struct., 140, 199-208. http://dx.doi.org/10.1016/j.engstruct.2017.02.047
  25. Castelli, M., Vanneschi, L. and Silva, S. (2013), "Prediction of high-performance concrete strength using genetic programming with geometric semantic genetic operators", Expert Syst. Applicat., 40(17), 6856-6862. http://dx.doi.org/10.1016/j.eswa.2013.06.037
  26. Chaabene, W.B. and Nehdi, M.L. (2021), "Genetic programming based symbolic regression for shear capacity prediction of SFRC beams", Constr. Build. Mater., 280, p. 122523. https://doi.org/10.1016/j.conbuildmat.2021.122523
  27. Chaabene, W.B., Flah, M. and Nehdi, M.L. (2020), "Machine learning prediction of mechanical properties of concrete: Critical review", Constr. Build. Mater., 260, p. 119889. https://doi.org/10.1016/j.conbuildmat.2020.119889
  28. Chen, T. and Guestrin, C. (2016), "Xgboost: A scalable tree boosting system", Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794. https://doi.org/10.1145/2939672.2939785
  29. Cheng, M.Y., Firdausi, P.M. and Prayogo, D. (2014), "High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT)", Eng. Applicat. Artif. Intell., 29, 104-113. http://dx.doi.org/10.1016/j.engappai.2013.11.014
  30. 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), p. 5481705. https://doi.org/10.1155/2018/5481705
  31. Chou, J.S. and Tsai, C.F. (2012), "Concrete compressive strength analysis using a combined classification and regression technique", Automat. Constr., 24, 52-60. https://doi.org/10.1016/j.autcon.2012.02.001
  32. Chou, J.S., Chiu, C.K., Farfoura, M. and Al-Taharwa, I. (2011), "Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques", J. Comput. Civil Eng., 25(3), 242-253. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000088
  33. 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
  34. Congro, M., de Alencar Monteiro, V.M., Brandão, A.L., dos Santos, B.F., Roehl, D. and de Andrade Silva, F. (2021), "Prediction of the residual flexural strength of fiber reinforced concrete using artificial neural networks", Constr. Build. Mater., 303, p. 124502. https://doi.org/10.1016/j.conbuildmat.2021.124502
  35. 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(11). https://doi.org/10.1061/(ASCE) MT.1943-5533.0002902
  36. Cortes, C. and Vapnik, V. (1995), "Support-vector networks", Mach. Learn., 20, 273-297. https://doi.org/10.1007/BF00994018
  37. Cui, L., Chen, P., Wang, L., Li, J. and Ling, H. (2021), "Application of extreme gradient boosting based on grey relation analysis for prediction of compressive strength of concrete", Adv. Civil Eng., 2021(1), p. 8878396. https://doi.org/10.1155/2021/8878396
  38. Dao, D.V., Ly, H.B., Vu, H.L.T., Le, T.T. and Pham, B.T. (2020), "Investigation and optimization of the C-ANN structure in predicting the compressive strength of foamed concrete", Materials, 13(5), p. 1072. https://doi.org/10.3390/ma13051072
  39. Deepa, C., Sathiya Kumari, K. and Sudha, V.P. (2010), "Prediction of the compressive strength of high-performance concrete mix using tree-based modelling", Int. J. Comput. Applicat., 6(5), 18-24. https://doi.org/10.5120/1076-1406
  40. 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
  41. Dorafshan, S., Thomas, R.J. and Maguire, M. (2018), "Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete", Constr. Build. Mater., 186, 1031-1045. https://doi.org/10.1016/j.conbuildmat.2018.08.011
  42. Drucker, H., Burges, C.J., Kaufman, L., Smola, A. and Vapnik, V. (1996), "Support vector regression machines", Adv. Neural Inform. Process. Syst., 9.
  43. Dutta, D. and Barai, S.V. (2019), "Prediction of compressive strength of concrete: machine learning approaches", In: Recent Advances in Structural Engineering, Volume 1, pp. 503-513. https://doi.org/10.1007/978-981-13-0362-3_4
  44. Erdal, H.I., Karakurt, O. and Namli, E. (2013), "High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform", Eng. Applicat. Artif. Intell., 26(4), 1246-1254. http://dx.doi.org/10.1016/j.engappai.2012.10.014
  45. Erdal, H., Erdal, M., Simsek, O. and Erdal, H.I. (2018), "Prediction of concrete compressive strength using non-destructive test results", Comput. Concrete, Int. J., 21(4), 407-417. https://doi.org/10.12989/cac.2018.21.4.407
  46. Farooq, F., Nasir Amin, M., Khan, K., Rehan Sadiq, M., Javed, M.F., Aslam, F. and Alyousef, R (2020), "A comparative study of random forest and genetic engineering programming for the prediction of compressive strength of high strength concrete (HSC)", Appl. Sci., 10(20), p. 7330. https://doi.org/10.3390/app102073300
  47. Feng, D.C., Liu, Z.T., Wang, X.D., Jiang, Z.M. and Liang, S.X. (2020), "Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm", Adv. Eng. Inform., 45, p. 101126. https://doi.org/10.1016/j.aei.2020.101126
  48. Fu, B. and Feng, D.C. (2021), "A machine learning-based time-dependent shear strength model for corroded reinforced concrete beams", J. Build. Eng., 36, p. 102118. https://doi.org/10.1016/j.jobe.2020.102118
  49. Gao, D. and Zhang, C. (2020), "Shear strength prediction model of FRP bar-reinforced concrete beams without stirrups", Mathe. Probl. Eng., 2020(1), p. 7516502. https://doi.org/10.1155/2020/7516502
  50. 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(1), 215-239. https://doi.org/10.1007/s11709-018-0489-z
  51. Güçlüer, K., Özbeyaz, A., Göymen, S. and Günaydın, O. (2021), "A comparative investigation using machine learning methods for concrete compressive strength estimation", Mater. Today Commun., 27, p. 102278. https://doi.org/10.1016/j.mtcomm.2021.102278
  52. Guo, P., Meng, W., Xu, M., Li, V.C. and Bao, Y. (2021), "Predicting mechanical properties of high-performance fiber-reinforced cementitious composites by integrating micromechanics and machine learning", Materials, 14(12), p. 3143. https://doi.org/10.3390/ma14123143
  53. Gupta, D. and Natarajan, N. (2021), "Prediction of uniaxial compressive strength of rock samples using density weighted least squares twin support vector regression", Neural Comput. Applicat., 33(22), 15843-15850. https://doi.org/10.1007/s00521-021-06204-2
  54. Gupta, S. and Sihag, P. (2022), "Prediction of the compressive strength of concrete using various predictive modeling techniques", Neural Comput. Applicat., 34(8), 6535-6545. https://doi.org/10.1007/s00521-021-06820-y
  55. Gupta, D., Hazarika, B.B. and Berlin, M. (2020), "Robust regularized extreme learning machine with asymmetric Huber loss function", Neural Comput. Applicat., 32(16), 12971-12998. https://doi.org/10.1007/s00521-020-04741-w
  56. 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(8), p. 682. https://doi.org/10.1140/epjp/s13360-020-00703-2
  57. 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
  58. 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, p. 118271. https://doi.org/10.1016/j.conbuildmat.2020.118271
  59. Hazarika, B.B. and Gupta, D. (2021), "Density-weighted support vector machines for binary class imbalance learning", Neural Comput. Applicat., 33(9), 4243-4261. https://doi.org/10.1007/s00521-020-05240-8
  60. Hoang, N.D., Pham, A.D., Nguyen, Q.L. and Pham, Q.N. (2016), "Estimating compressive strength of high-performance concrete with Gaussian process regression model", Adv. Civil Eng., 2016(1), p. 2861380. https://doi.org/10.1155/2016/2861380 H
  61. Huang, G.B., Zhu, Q.Y. and Siew, C.K. (2006), "Extreme learning machine: theory and applications", Neurocomputing, 70(1-3), 489-501. https://doi.org/10.1016/j.neucom.2005.12.126
  62. Huang, H., Wei, X. and Zhou, Y. (2022), "An overview on twin support vector regression", Neurocomputing, 490, 80-92. https://doi.org/10.1016/j.neucom.2021.10.125
  63. Jang, Y., Ahn, Y. and Kim, H.Y. (2019), "Estimating compressive strength of concrete using deep convolutional neural networks with digital microscope images", J. Comput. Civil Eng., 33(3), p. 04019018. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000837
  64. Jeon, J.S., Shafieezadeh, A. and DesRoches, R. (2014), "Statistical models for shear strength of RC beam‐column joints using machine‐learning techniques", Earthq. Eng. Struct. Dyn., 43(14), 2075-2095. https://doi.org/10.1002/eqe.2437
  65. Kang, F., Liu, J., Li, J. and Li, S. (2017), "Concrete dam deformation prediction model for health monitoring based on extreme learning machine", Struct. Control Health Monitor., 24(10), p. e1997. https://doi.org/10.1002/stc.1997
  66. 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, p. 121117. https://doi.org/10.1016/j.conbuildmat.2020.121117
  67. Khademi, F., Akbari, M. and Jamal, S.M. (2015), "Prediction of compressive strength of concrete by data-driven models", I-Manager's J. Civil Eng., 5(2), 16-23. https://doi.org/10.26634/jce.5.2.3350
  68. 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
  69. Khademi, F., Akbari, M., Jamal, S.M. and Nikoo, M. (2017), "Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete", Front. Struct. Civil Eng., 11(1), 90-99. https://doi.org/10.1007/s11709-016-0363-9
  70. Koo, S., Shin, D. and Kim, C. (2021), "Application of principal component analysis approach to predict shear strength of reinforced concrete beams with stirrups", Materials, 14(13), p. 3471. https://doi.org/10.3390/ma14133471
  71. Kotsiantis, S.B. (2013), "Decision trees: a recent overview", Artif. Intell. Rev., 39, 261-283. https://doi.org/10.1007/s10462-011-9272-4
  72. 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", Sustainability, 14(4), p. 2404. https://doi.org/10.3390/su14042404
  73. Le, T.T., Asteris, P.G. and Lemonis, M.E. (2022), "Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques", Eng. Comput., 38(Suppl 4), 3283-3316. https://doi.org/10.1007/s00366-021-01461-0
  74. LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998), "Gradient-based learning applied to document recognition", Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791
  75. Liu, J.C. and Zhang, Z. (2020), "A machine learning approach to predict explosive spalling of heated concrete", Arch. Civil Mech. Eng., 20(4), 1-25. https://doi.org/10.1007/s43452-020-00135-w
  76. Ly, H.B., Le, T.T., Vu, H.L.T., Tran, V.Q., Le, L.M. and Pham, B.T. (2020), "Computational hybrid machine learning based prediction of shear capacity for steel fiber reinforced concrete beams", Sustainability, 12(7), p. 2709. https://doi.org/10.3390/su12072709
  77. Ly, H.B., Nguyen, T.A. and Pham, B.T. (2022), "Investigation on factors affecting early strength of high-performance concrete by Gaussian Process Regression", PloS one, 17(1), p. e0262930. https://doi.org/10.1371/journal.pone.0262930
  78. Mangalathu, S. and Jeon, J.S. (2018), "Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques", Eng. Struct., 160, 85-94. https://doi.org/10.1016/j.engstruct.2018.01.008
  79. Markou, G. and Bakas, N.P. (2021), "Prediction of the shear capacity of reinforced concrete slender beams without stirrups by applying artificial intelligence algorithms in a big database of beams generated by 3D nonlinear finite element analysis", Comput. Concrete, Int. J., 28(6), 533-547. https://doi.org/10.12989/cac.2021.28.6.533
  80. McCulloch, W.S. and Pitts, W. (1943), "A logical calculus of the ideas immanent in nervous activity", Bull. Mathe. Biophys., 5, 115-133. https://doi.org/10.1007/BF02478259
  81. Mishra, M., Agarwal, A. and Maity, D. (2019a), "Neural-network-based approach to predict the deflection of plain, steel-reinforced, and bamboo-reinforced concrete beams from experimental data", SN Appl. Sci., 1(6), 1-11. https://doi.org/10.1007/s42452-019-0622-1
  82. Mishra, M., Bhatia, A.S. and Maity, D. (2019b), "Support vector machine for determining the compressive strength of brick-mortar masonry using NDT data fusion (case study: Kharagpur, India)", SN Appl. Sci., 1(6), 1-11. https://doi.org/10.1007/s42452-019-0590-5
  83. Mishra, M., Bhatia, A.S. and Maity, D. (2020), "Predicting the compressive strength of unreinforced brick masonry using machine learning techniques validated on a case study of a museum through non-destructive testing", J. Civil Struct. Health Monitor., 10(3), 389-403. https://doi.org/10.1007/s13349-020-00391-7
  84. Mishra, M., Bhatia, A.S. and Maity, D. (2021), "A comparative study of regression, neural network and neuro-fuzzy inference system for determining the compressive strength of brick–mortar masonry by fusing non-destructive testing data", Eng. Comput., 37(1), 77-91. https://doi.org/10.1007/s00366-019-00810-4
  85. Mohamed, O.A., Ati, M. and Najm, O.F. (2017), "Predicting Compressive Strength of Sustainable Self-Consolidating Concrete Using Random Forest", In: Key Engineering Materials, Vol. 744, pp. 141-145. https://doi.org/10.4028/www.scientific.net/KEM.744.141
  86. Mohammadhassani, M., Nezamabadi-Pour, H., Suhatril, M. and Shariati, M. (2014), "An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups", Smart Struct. Syst., Int. J., 14(5), 785-809. http://dx.doi.org/10.12989/sss.2014.14.5.785
  87. Mohammed, H.R.M. and Ismail, S. (2021), "Random forest versus support vector machine models' applicability for predicting beam shear strength", Complexity, 2021(1), p. 9978409. https://doi.org/10.1155/2021/9978409
  88. Mohana, M.H., Hmood, M. and Mohana, M. (2020), "The determination of ground granulated concrete compressive strength-based machine learning models", Period. Eng. Nat. Sci. (PEN), 8, 1011-1023. http://dx.doi.org/10.21533/pen.v8i2.1344
  89. Muliauwan, H.N., Prayogo, D., Gaby, G. and Harsono, K. (2020), "Prediction of concrete compressive strength using artificial intelligence methods", In: Journal of Physics: Conference Series, Vol. 1625, No. 1, p. 012018. https://doi.org/10.1088/1742-6596/1625/1/012018
  90. Murphy, K.P. (2012), Machine Learning: A Probabilistic Perspective, MIT press.
  91. Nariman, N.A., Hamdia, K., Ramadan, A.M. and Sadaghian, H. (2021), "Optimum design of flexural strength and stiffness for reinforced concrete beams using machine learning", Appl. Sci., 11(18), p. 8762. https://doi.org/10.3390/app11188762
  92. Naser, M.Z. (2021), "Observational analysis of fire-induced spalling of concrete through ensemble machine learning and surrogate modeling", J. Mater. Civil Eng., 33(1), p. 04020428. https://doi.org/10.1061/(ASCE)MT.1943-5533.0003525
  93. Naseri, H., Jahanbakhsh, H., Hosseini, P. and Nejad, F.M. (2020a), "Designing sustainable concrete mixture by developing a new machine learning technique", J. Cleaner Product., 258, p. 120578. https://doi.org/10.1016/j.jclepro.2020.120578
  94. Naseri, H., Jahanbakhsh, H., Moghadas Nejad, F. and Golroo, A. (2020b), "Developing a novel machine learning method to predict the compressive strength of fly ash concrete in different ages", AUT J. Civil Eng., 4(4), 423-436. https://dx.doi.org/10.22060/ajce.2019.16124.5569
  95. Nguyen, K.T., Nguyen, Q.D., Le, T.A., Shin, J. and Lee, K. (2020a), "Analysing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches", Constr. Build. Mater., 247, p. 118581. https://doi.org/10.1016/j.conbuildmat.2020.118581
  96. Nguyen, T.A., Ly, H.B., Mai, H.V.T. and Tran, V.Q. (2020b), "Prediction of later-age concrete compressive strength using feedforward neural network", Adv. Mater. Sci. Eng., 2020(1), p. 9682740. https://doi.org/10.1155/2020/9682740
  97. Nguyen, H.D., Truong, G.T. and Shin, M. (2021a), "Development of extreme gradient boosting model for prediction of punching shear resistance of r/c interior slabs", Eng. Struct., 235, p. 112067. https://doi.org/10.1016/j.engstruct.2021.112067
  98. Nguyen, H., Vu, T., Vo, T.P. and Thai, H.T. (2021b), "Efficient machine learning models for prediction of concrete strengths", Constr. Build. Mater., 266, p. 120950. https://doi.org/10.1016/j.conbuildmat.2020.120950
  99. Nguyen, T.T., Ngoc, L.T., Vu, H.H. and Thanh, T.P. (2021c), "Machine learning-based model for predicting concrete compressive strength", GEOMATE Journal, 20(77), 197-204. https://doi.org/10.21660/2020.77.j2019
  100. Nguyen, H., Nguyen, N.M., Cao, M.T., Hoang, N.D. and Tran, X.L. (2022), "Prediction of long-term deflections of reinforced-concrete members using a novel swarm optimized extreme gradient boosting machine", Eng. Comput., 38(2), 1255-1267. http://doi.org/10.1007/s00366-020-01260-z
  101. 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, p. 119757. https://doi.org/10.1016/j.conbuildmat.2020.119757
  102. Ni, H.G. and Wang, J.Z. (2000), "Prediction of compressive strength of concrete by neural networks", Cement Concrete Res., 30(8), 1245-1250. https://doi.org/10.1016/S0008-8846(00)00345-8
  103. Nikoo, M., Torabian Moghadam, F. and Sadowski, Ł. (2015), "Prediction of concrete compressive strength by evolutionary artificial neural networks", Adv. Mater. Sci. Eng., 2015(1), p. 849126. https://doi.org/10.1155/2015/849126
  104. Okazaki, Y., Okazaki, S., Asamoto, S. and Chun, P.J. (2020), "Applicability of machine learning to a crack model in concrete bridges", Comput.‐Aided Civil Infrastr. Eng., 35(8), 775-792. https://doi.org/10.1111/mice.12532
  105. Olalusi, O.B. and Awoyera, P.O. (2021), "Shear capacity prediction of slender reinforced concrete structures with steel fibers using machine learning", Eng. Struct., 227, p. 111470. https://doi.org/10.1016/j.engstruct.2020.111470
  106. Olalusi, O.B. and Spyridis, P. (2020), "Machine learning-based models for the concrete breakout capacity prediction of single anchors in shear", Adv. Eng. Software, 147, p. 102832. https://doi.org/10.1016/j.advengsoft.2020.102832
  107. Pandey, S., Kumar, V. and Kumar, P. (2021), "Application and analysis of machine learning algorithms for design of concrete mix with plasticizer and without plasticizer", J. Soft Comput. Civil Eng., 5(1), 19-37. https://dx.doi.org/10.22115/scce.2021.248779.1257
  108. Park, J.Y., Yoon, Y.G. and Oh, T.K. (2019), "Prediction of concrete strength with P-, S-, R-wave velocities by support vector machine (SVM) and artificial neural network (ANN)", Appl. Sci., 9(19), p. 4053. https://doi.org/10.3390/app9194053
  109. Park, J.Y., Sim, S.H., Yoon, Y.G. and Oh, T.K. (2020), "Prediction of static modulus and compressive strength of concrete from dynamic modulus associated with wave velocity and resonance frequency using machine learning techniques", Materials, 13(13), p. 2886. https://doi.org/10.3390/ma13132886
  110. Pham, A.D., Ngo, N.T. and Nguyen, T.K. (2020a), "Machine learning for predicting long-term deflections in reinforce concrete flexural structures", J. Computat. Des. Eng., 7(1), 95-106. https://doi.org/10.1093/jcde/qwaa010
  111. Pham, A.D., Ngo, N.T., Nguyen, Q.T. and Truong, N.S. (2020b), "Hybrid machine learning for predicting strength of sustainable concrete", Soft Comput., 24(19), 14965-14980. https://doi.org/10.1007/s00500-020-04848-1
  112. Prayogo, D., Cheng, M.Y., Widjaja, J., Ongkowijoyo, H. and Prayogo, H. (2017), "Prediction of concrete compressive strength from early age test result using an advanced metaheuristic-based machine learning technique", In: ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, Vol. 34. https://doi.org/10.22260/ISARC2017/0120
  113. Prayogo, D., Santoso, D.I., Wijaya, D., Gunawan, T. and Widjaja, J.A. (2020), "Prediction of Concrete Properties Using Ensemble Machine Learning Methods", In: Journal of Physics: Conference Series, Vol. 1625, No. 1, p. 012024. https://doi.org/10.1088/1742-6596/1625/1/012024
  114. Rahman, J., Ahmed, K.S., Khan, N.I., Islam, K. and Mangalathu, S. (2021), "Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach", Eng. Struct., 233, p. 111743. https://doi.org/10.1016/j.engstruct.2020.111743
  115. Rebouh, R., Boukhatem, B., Ghrici, M. and Tagnit-Hamou, A. (2017), "A practical hybrid NNGA system for predicting the compressive strength of concrete containing natural pozzolan using an evolutionary structure", Constr. Build. Mater., 149, 778-789. https://doi.org/10.1016/j.conbuildmat.2017.05.165
  116. Reuter, U., Sultan, A. and Reischl, D.S. (2018), "A comparative study of machine learning approaches for modeling concrete failure surfaces", Adv. Eng. Software, 116, 67-79. https://doi.org/10.1016/j.advengsoft.2017.11.006
  117. Rezaiee-Pajand, M., Karimipour, A. and Abad, J.M.N. (2021), "Crack spacing prediction of fibre-reinforced concrete beams with lap-spliced bars by machine learning models", Iran. J. Sci. Technol., Transact. Civil Eng., 45(2), 833-850. https://doi.org/10.1007/s40996-020-00441-6
  118. Saad, S., Ishtiyaque, M. and Malik, H. (2016), "Selection of most relevant input parameters using WEKA for artificial neural network based concrete compressive strength prediction model", In: 2016 IEEE 7th Power India International Conference (PIICON), pp. 1-6. https://doi.org/10.1109/POWERI.2016.8077368
  119. Sagar, B.D., Cheng, Q., McKinley, J. and Agterberg, F. (Eds.) (2023), Encyclopedia of mathematical geosciences, Springer Nature.
  120. Seyed, J.S.H., Jamaloddin, N., Mohammed, J. and Mohammad, M. (2011), "Application of artificial neural networks to predict compressive strength of high strength concrete", Int. J. Phys. Sci., 6(5), 975-981. https://doi.org/10.5897/IJPS11.023
  121. Shariati, M., Mafipour, M.S., Ghahremani, B., Azarhomayun, F., Ahmadi, M., Trung, N.T. and Shariati, A. (2020), "A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement", Eng. Comput., 1-23. https://doi.org/10.1007/s00366-020-01081-0
  122. Sharma, M., Anotaipaiboon, W. and Chaiyasarn, K. (2018), "Concrete crack detection using the integration of convolutional neural network and support vector machine", Sci. Technol. Asia, 19-28. https://doi.org/10.14456/scitechasia.2018.11
  123. Silva, P.F. and Moita, G.F. (2019), "A comparative study of machine learning methods for compressive strength of concrete", In: The 4th World Congress on Civil, Structural, and Environmental Engineering. https://doi.org/10.11159/icsect19.136
  124. Song, H., Ahmad, A., Farooq, F., Ostrowski, K.A., Maślak, 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, p. 125021. https://doi.org/10.1016/j.conbuildmat.2021.125021
  125. Spijkerman, Z., Bakas, N., Markou, G. and Papadrakakis, M. (2021), "Predicting the shear capacity of reinforced concrete slender beams without stirrups by applying artificial intelligence algorithms", In: 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2021), pp. 3810-3819, Athens, Greece, June.
  126. Su, M., Zhong, Q., Peng, H. and Li, S. (2021), "Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete", Constr. Build. Mater., 270, p. 121456. https://doi.org/10.1016/j.conbuildmat.2020.121456
  127. Sun, Y., Li, G., Zhang, J. and Qian, D. (2019), "Prediction of the strength of rubberized concrete by an evolved random forest model", Adv. Civil Eng., 2019(1), p. 5198583. https://doi.org/10.1155/2019/5198583
  128. Sun, H., Du, W. and Liu, C. (2021), "Uniaxial compressive strength determination of rocks using X-ray computed tomography and convolutional neural networks", Rock Mech. Rock Eng., 54(8), 4225-4237. https://doi.org/10.1007/s00603-021-02503-1
  129. Taffese, W.Z. and Sistonen, E. (2017), "Machine learning for durability and service-life assessment of reinforced concrete structures: Recent advances and future directions", Automat. Constr., 77, 1-14. https://doi.org/10.1016/j.autcon.2017.01.016
  130. Tavana Amlashi, A., Ghanizadeh, A.R., Abbaslou, H. and Alidoust, P. (2020) "Developing three hybrid machine learning algorithms for predicting the mechanical properties of plastic concrete samples with different geometries", AUT J. Civil Eng., 4(1), 37-54. https://dx.doi.org/10.22060/ajce.2019.15026.5517
  131. Thirumalaiselvi, A., Verma, M., Anandavalli, N. and Rajasankar, J. (2018), "Response prediction of laced steel-concrete composite beams using machine learning algorithms", Struct. Eng. Mecha., Int. J., 66(3), 399-409. https://doi.org/10.12989/sem.2018.66.3.399
  132. Timur Cihan, M. (2019), "Prediction of concrete compressive strength and slump by machine learning methods", Adv. Civil Eng., 2019(1), p. 3069046. https://doi.org/10.1155/2019/3069046
  133. Tran, T.H., Nguyen, H. and Nhat-Duc, H. (2021), "A success history-based adaptive differential evolution optimized support vector regression for estimating plastic viscosity of fresh concrete", Eng. Comput., 37(2), 1485-1498. https://doi.org/10.1007/s00366-019-00899-7
  134. Ünlü, 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
  135. Wan, Z., Xu, Y. and Šavija, B. (2021), "On the use of machine learning models for prediction of compressive strength of concrete: Influence of dimensionality reduction on the model performance", Materials, 14(4), p. 713. https://doi.org/10.3390/ma14040713
  136. 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. Software, 115, 112-125. https://doi.org/10.1016/j.advengsoft.2017.09.004
  137. Yeh, J.P. and Yang, R.P. (2014), "Application of the adaptive neuro-fuzzy inference system for optimal design of reinforced concrete beams", J. Intell. Learn. Syst. Applicat., 6(04), p. 162. https://doi.org/10.4236/jilsa.2014.64013
  138. Zhang, J., Ma, G., Huang, Y., Aslani, F. and Nener, B. (2019a), "Modelling uniaxial compressive strength of lightweight selfcompacting concrete using random forest regression", Constr. Build. Mater., 210, 713-719. https://doi.org/10.1016/j.conbuildmat.2019.03.189
  139. Zhang, M., Li, M., Shen, Y., Ren, Q. and Zhang, J. (2019b), "Multiple mechanical properties prediction of hydraulic concrete in the form of combined damming by experimental data mining", Constr. Build. Mater., 207, 661-671. https://doi.org/10.1016/j.conbuildmat.2019.02.169
  140. Zhang, J., Huang, Y., Wang, Y. and Ma, G. (2020), "Multi-objective optimization of concrete mixture proportions using machine learning and metaheuristic algorithms", Constr. Build. Mater., 253, p. 119208. https://doi.org/10.1016/j.conbuildmat.2020.119208
  141. Zhang, M., Akiyama, M., Shintani, M., Xin, J. and Frangopol, D.M. (2021), "Probabilistic estimation of flexural loading capacity of existing RC structures based on observational corrosion-induced crack width distribution using machine learning", Struct. Safety, 91, p. 102098. https://doi.org/10.1016/j.strusafe.2021.102098
  142. Zheng, L., Cheng, H., Huo, L. and Song, G. (2019), "Monitor concrete moisture level using percussion and machine learning", Constr. Build. Mater., 229, p. 117077. https://doi.org/10.1016/j.conbuildmat.2019.117077
  143. Zhou, Q., Zhu, F., Yang, X., Wang, F., Chi, B. and Zhang, Z. (2017), "Shear capacity estimation of fully grouted reinforced concrete masonry walls using neural network and adaptive neuro-fuzzy inference system models", Constr. Build. Mater., 153, 937-947. https://doi.org/10.1016/j.conbuildmat.2017.07.171
  144. Zhuang, X. and Zhou, S. (2019), "The prediction of self-healing capacity of bacteria-based concrete using machine learning approaches", Comput. Mater. Continua, 59(2019), Nr. 1, pp. 57- 77. https://doi.org/10.15488/4767
  145. Ziolkowski, P. and Niedostatkiewicz, M. (2019), "Machine learning techniques in concrete mix design", Materials, 12(8), p. 1256. https://doi.org/10.3390/ma12081256