Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Machine learning in concrete's strength prediction

  • Al-Gburi, Saddam N.A. (International Organization for Migration) ;
  • Akpinar, Pinar (Department of Civil Engineering, Bahcesehir Cyprus University) ;
  • Helwan, Abdulkader (Department of Electrical and Computer Engineering, Lebanese American University)
  • Received : 2020.05.06
  • Accepted : 2022.06.07
  • Published : 2022.06.25
⟨ Previous Next ⟩

Abstract

Concrete's compressive strength is widely studied in order to understand many qualities and the grade of the concrete mixture. Conventional civil engineering tests involve time and resources consuming laboratory operations which results in the deterioration of concrete samples. Proposing efficient non-destructive models for the prediction of concrete compressive strength will certainly yield advancements in concrete studies. In this study, the efficiency of using radial basis function neural network (RBFNN) which is not common in this field, is studied for the concrete compressive strength prediction. Complementary studies with back propagation neural network (BPNN), which is commonly used in this field, have also been carried out in order to verify the efficiency of RBFNN for compressive strength prediction. A total of 13 input parameters, including novel ones such as cement's and fly ash's compositional information, have been employed in the prediction models with RBFNN and BPNN since all these parameters are known to influence concrete strength. Three different train: test ratios were tested with both models, while different hidden neurons, epochs, and spread values were introduced to determine the optimum parameters for yielding the best prediction results. Prediction results obtained by RBFNN are observed to yield satisfactory high correlation coefficients and satisfactory low mean square error values when compared to the results in the previous studies, indicating the efficiency of the proposed model.

Keywords

References

  1. Abubakar, M. and Akpinar, P. (2019), "Intelligent prediction of initial setting time for cement pastes by using artificial neural network", Proceedings of the 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions-ICSCCW, Prague, Czech Republic.
  2. 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.
  3. Akpinar, P. and Uwanuakwa, I.D. (2016), "Intelligent prediction of concrete carbonation depth using neural networks", Bull. Transilv. Univ. Bras. III: Math. Inform. Phys., 9(58), 99-108.
  4. Akpinar, P. and Uwanuakwa, I.D. (2020), "Investigation of the parameters influencing the progress of concrete carbonation depth by using artificial neural networks", Mater. Construccion, 70(337), 1-14. https://doi.org/10.3989/mc.2020.02019.
  5. Atici, U. (2011), "Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network", Exp. Syst. Appl., 38(8), 9609-9618. https://doi.org/10.1016/j.eswa.2011.01.156.
  6. Atis, C.D. (2003), "Accelerated carbonation and testing of concrete made with fly ash", Constr. Build. Mater., 17(3), 147-152. https://doi.org/10.1016/S0950-0618(02)00116-2.
  7. Atis, C.D. (2005), "Strength properties of high-volume fly ash roller compacted and workable concrete, and influence of curing condition", Cement Concrete Res., 35(6), 1112-1121. https://doi.org/10.1016/j.cemconres.2004.07.037.
  8. Ayat, H., Kellouche, Y., Ghrici, M. and Boukhatem, B. (2018), "Compressive strength prediction of limestone filler concrete using artificial neural networks", Adv. Comput. Des., 3(3), 289-302. https://doi.org/10.12989/acd.2018.3.3.289.
  9. Boukhatem, B., Kenai, S., Hamou, A.T., Ziou, D. and Ghrici, M. (2012), "Predicting concrete properties using neural networks (NN) with principal component analysis (PCA) technique", Comput. Concrete, 10(6), 557-573. https://doi.org/10.12989/cac.2012.10.6.557.
  10. Chithra, S., Kumar, S.S., Chinnaraju, K. and Ashmita, F.A. (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.
  11. Douma, O.B., Boukhatem, B., Ghrici, M. and Tagnit-Hamou, A. (2016), "Prediction of properties of self-compacting concrete containing fly ash using artificial neural network", Neur. Comput. Appl., 1-12. https://doi.org/10.1007/s00521-016-2368-7.
  12. Du, K.L. and Swamy, M.N. (2013), Neural Networks and Statistical Learning, Springer Science & Business Media.
  13. Gholampour, A. and Ozbakkaloglu, T. (2017), "Performance of sustainable concretes containing very high-volume Class-F fly ash and ground granulated blast furnace slag", J. Clean. Prod., 162, 1407-1417. https://doi.org/10.1016/j.jclepro.2017.06.087.
  14. Helwan, A. and Tantua, D.P. (2016), "IKRAI: intelligent knee rheumatoid arthritis identification", Int. J. Intell. Syst., 8(1), 18. https://doi.org/10.5815/ijisa.2016.01.03.
  15. Helwan, A. and Uzun Ozsahin, D. (2017), "Sliding window based machine learning system for the left ventricle localization in MR cardiac images", Appl. Comput. Intell. Soft Comput., 2017, Article ID 3048181. https://doi.org/10.1155/2017/3048181.
  16. Helwan, A., Idoko, J.B. and Abiyev, R.H. (2017), "Machine learning techniques for classification of breast tissue", Procedia Comput. Sci., 120, 402-410. https://doi.org/10.1016/j.procs.2017.11.256.
  17. Helwan, A., Khashman, A., Olaniyi, E.O., Oyedotun, O.K. and Oyedotun, O.A. (2016), "Seminal quality evaluation with RBF neural network", Bull. Transilv. Univ. Bras. III: Math. Inform. Phys., 9(2), 137.
  18. Helwan, A., Ozsahin, D.U., Abiyev, R. and Bush, J. (2017), "One-year survival prediction of myocardial infarction", Int. J. Adv. Comput. Sci. Appl., 8(6), 173-178. https://doi.org/10.14569/IJACSA.2017.080622.
  19. Kao, C.H., Wang, C.C. and Wang, H.Y. (2017), "A neural-based predictive model of the compressive strength of waste LCD glass concrete", Comput. Concrete, 19(5), 457-465. https://doi.org/10.12989/cac.2017.19.5.457.
  20. Khashman, A. and Akpinar, P. (2017), "Non-destructive prediction of concrete compressive strength using Neural Networks", Procedia Comput. Sci., 108, 2358-2362. https://doi.org/10.1016/j.procs.2017.05.039.
  21. Lai, S. and Serra, M. (1997), "Concrete strength prediction by means of neural network", Constr. Build. Mater., 11(2), 93-98. https://doi.org/10.1016/S0950-0618(97)00007-X.
  22. Liu, M. (2010), "Self-compacting concrete with different levels of pulverized fuel ash", Constr. Build. Mater., 24(7), 1245-1252. https://doi.org/10.1016/j.conbuildmat.2009.12.012.
  23. Markopoulos, A.P., Georgiopoulos, S. and Manolakos, D.E. (2016), "On the use of back propagation and radial basis function neural networks in surface roughness prediction", J. Ind. Eng., 12(3), 389-400. https://doi.org/10.1007/s40092-016-0146-x.
  24. Ndihokubwayo, A. (2011), "Compressive and flexural strengths for considerable volume fly-ash concrete", J. Civil Eng. Manage., 1(1), 21-23. https://doi.org/10.5923/j.jce.20110101.03.
  25. Neville, A.M. and Brooks, J.J. (2010), Concrete Technology, Pearson Education Ltd.
  26. 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.
  27. Nikoo, M., Torabian Moghadam, F. and Sadowski, L. (2015), "Prediction of concrete compressive strength by evolutionary artificial neural networks", Adv. Mater. Sci. Eng., 2015, Article ID 849126. https://doi.org/10.1155/2015/849126.
  28. Oner, A., Akyuz, S. and Yildiz, R. (2005), "An experimental study on strength development of concrete containing fly ash and optimum usage of fly ash in concrete", Cement Concrete Res., 35(6), 1165-1171. https://doi.org/10.1016/j.cemconres.2004.09.031.
  29. Poon, C.S., Lam, L. and Wong, Y.L. (2000), "A study on high strength concrete prepared with large volumes of low calcium fly ash", Cement Concrete Res., 30(3), 447-455. https://doi.org/10.1016/S0008-8846(99)00271-9.
  30. Ramezanianpour, A.A. and Malhotra, V.M. (1995, "Effect of curing on the compressive strength, resistance to chloride-ion penetration and porosity of concretes incorporating slag, fly ash or silica fume", Cement Concrete Compos., 17(2), 125-133. https://doi.org/10.1016/0958-9465(95)00005-W.
  31. 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.
  32. Saha, A.K. (2018), "Effect of class F fly ash on the durability properties of concrete", Sustain. Environ. Res., 28(1), 25-31. https://doi.org/10.1016/j.proeng.2011.07.144.
  33. Siddique, R., Aggarwal, P. and Aggarwal, Y. (2011), "Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks", Adv. Eng. Softw., 42(10), 780-786. https://doi.org/10.1016/j.advengsoft.2011.05.016.
  34. Sounthararajan, V.M. and Sivakumar, A. (2013), "Accelerated engineering properties of high and low volume fly ash concretes reinforced with glued steel fibers", Front. Struct. Civil Eng., 7(4), 429-445. https://doi.org/10.1007/s11709-013-0226-6.
  35. Uwanuakwa, I.D. and Akpinar, P. (2019), "Investigations on the influence of variations in hidden neurons and training data percentage on the efficiency of concrete carbonation depth prediction with ANN", Proceedings of the 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions-ICSCCW, Prague, Czech Republic.
  36. Yeh, I.C. (1998), "Modeling of strength of high-performance concrete using artificial neural networks", Cement Concrete Res., 28(12), 1797-1808. https://doi.org/10.1016/S0008-8846(98)00165-3
  37. Zhang, M.H. and Islam, J. (2012), "Use of nano-silica to reduce setting time and increase early strength of concretes with high volumes of fly ash or slag", Constr. Build. Mater., 29, 573-580. https://doi.org/10.1016/j.conbuildmat.2011.11.013.