Computers and Concrete

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The prediction of compressive strength and non-destructive tests of sustainable concrete by using artificial neural networks

  • Tahwia, Ahmed M. (Civil Engineering Department, Faculty of Engineering, Mansoura University) ;
  • Heniegal, Ashraf (Civil Engineering Department, Faculty of Engineering, Suez University) ;
  • Elgamal, Mohamed S. (Civil Engineering Department, Faculty of Engineering, Mansoura University) ;
  • Tayeh, Bassam A. (Civil Engineering Department, Faculty of Engineering, Islamic University of Gaza)
  • Received : 2020.09.14
  • Accepted : 2020.12.11
  • Published : 2021.01.25
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Abstract

The Artificial Neural Network (ANN) is a system, which is utilized for solving complicated problems by using nonlinear equations. This study aims to investigate compressive strength, rebound hammer number (RN), and ultrasonic pulse velocity (UPV) of sustainable concrete containing various amounts of fly ash, silica fume, and blast furnace slag (BFS). In this study, the artificial neural network technique connects a nonlinear phenomenon and the intrinsic properties of sustainable concrete, which establishes relationships between them in a model. To this end, a total of 645 data sets were collected for the concrete mixtures from previously published papers at different curing times and test ages at 3, 7, 28, 90, 180 days to propose a model of nine inputs and three outputs. The ANN model's statistical parameter R2 is 0.99 of the training, validation, and test steps, which showed that the proposed model provided good prediction of compressive strength, RN, and UPV of sustainable concrete with the addition of cement.

Keywords

References

  1. Aflaki, E. and Moodi, F. (2017), "Laboratory tests for studying the performance of grouted micro-fine cement", Comput. Concrete, 20(2), 145-154. https://doi.org/10.12989/cac.2017.20.2.145.
  2. Alabduljabbar, H., Haido, J.H., Alyousef, R., Yousif, S.T., McConnell, J., Wakil, K. and Jermsittiparsert, K. (2020), "Prediction of the flexural behavior of corroded concrete beams using combined method", Struct., 25, 1000-1008. https://doi.org/10.1016/j.istruc.2020.03.057.
  3. Al-Mishhadani, S.A., Joni, H.H. and Radhi, E.M.S. (2012), "Effect of age on nondestructive tests results for existing concrete", Iraqi J. Mech. Mater. Eng., 12(4), 630-646.
  4. Ali-Benyahia, K., Kenai, S. and Ghrici, M. (2010), "Correlation between nondestructive and destructive tests of low strength concrete", 37th IAHS World Congress on Housing Science.
  5. Amini, K., Jalalpour, M. and Delatte, N. (2016), "Advancing concrete strength prediction using non-destructive testing: Developmentand verification of a generalizable model", Constr. Build. Mater., 102, 762-768. https://doi.org/10.1016/j.conbuildmat.2015.10.131
  6. Asteris, P.G. and Mokos, V.G. (2020), "Concrete compressive strength using a rtificial neural networks", Neural Comput. Appl., 32(15), 11807-11826. https://doi.org/10.1007/s00521-019-04663-2.
  7. Atakulreka, A. and Sutivong, D. (2007), "Avoiding local minima in feedforward neural networks by simultaneous learning", Australasian Joint Conference on Artificial Intelligence, Springer, Berlin, Heidelberg.
  8. 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.
  9. 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.
  10. Azreen, M.N., Pauzi, I.M., Nasharuddin, I., Haniza, M.M., Akasyah, J., Karsono, A.D. and Lei, V.Y. (2016), "Prediction of concrete compression strength using ultrasonic pulse velocity", AIP Conf. Proc., 1704(1), 040006. https://doi.org/10.1063/1.4940092.
  11. Banu, S.S., Kartikeyan, J. and Jayabalan, P. (2020), "Influence of using agrowaste as partial replacement in cement on the compressive strength of concrete-A statistical approach", Constr. Build. Mater., 250, 118746. https://doi.org/10.1016/j.conbuildmat.2020.118746
  12. Bogas, J.A., Gomes, M.G. and Gomes, A. (2013), "Compressive strength evaluation of structural lightweight concrete by non destructive ultrasonic pulse velocity method", Ultrasonic., 53(5), 962-972. https://doi.org/10.1016/j.ultras.2012.12.012.
  13. Bzeni, D.K. and Ihsan, M.A. (2013), "Estimating strength of SCC using non-destructive combined method", 3rd International Conference on Sustainable Construction Materials and Technologies.
  14. Choi, H. and Azari, H. (2017), "Guided wave analysis of aircoupled impact-echo in concrete slab", Comput. Concrete, 20(3), 257-262. https://doi.org/10.12989/cac.2017.20.3.257.
  15. Diab, A.M., Elyamany, H.E., Abd Elmoaty, M. and Shalan, A.H. (2014), "Prediction of concrete compressive strength due to long term sulfate attack using neural network", Alex. Eng. J., 53(3), 627-642. https://doi.org/10.1016/j.aej.2014.04.002.
  16. Domingo, R. and Hirose, S. (2009), "Correlation between concrete strength and combined nondestructive tests for concrete using high-early strength cement", The Sixth Regional Symposium on Infrastructure Development.
  17. Hamidian, M., Shariati, A., Khanouki, M.A., Sinaei, H., Toghroli, A. and Nouri, K. (2012), "Application of Schmidt rebound hammer and ultrasonic pulse velocity techniques for structural health monitoring", Scientif. Res. Essay., 7(21), 1997-2001. https://doi.org/10.5897/SRE11.1387.
  18. Heniegal, A.M. (2012), "Numerical Analysis for predicting of self compacting concrete mixtures using artificial neural networks", J. Engng. Sci., 40(6), 1575-1597.
  19. Hobbs, B. and Tchoketch Kebir, M. (2007), "Non-destructive testing techniques for the forensic engineering investigation of reinforced concrete buildings", Forensic Sci. Int., 167(2), 167-172. https://doi.org/10.1016/j.forsciint.2006.06.065.
  20. Hola, J. and Schabowicz, K. (2005), "Application of artificial neural networks to determine concrete compressive strength based on non‐destructive tests", J. Civil Eng. Manage., 11(1), 23-32. https://doi.org/10.3846/13923730.2005.9636329
  21. Jain, A., Kathuria, A., Kumar, A., Verma, Y. and Murari, K. (2013), "Combined use of non-destructive tests for assessment of strength of concrete in structure", Procedia Eng., 54, 241-251. https://doi.org/10.1016/j.proeng.2013.03.022.
  22. Khademi, F. and Jamal, S.M. (2016), "Predicting the 28 days compressive strength of concrete using artificial neural network", I-manager's J. Civil Eng., 6, 1.
  23. Kim, H.J., Park, T.W. and Chung, L. "Intergration of neural networks with NDE for concrete strength prediction",
  24. Kim, J.I. and Kim, D.K. (2002), "Application of neural networks for estimation of concrete strength", KSCE J. Civil Eng., 6(4), 429-438. https://doi.org/10.1061/(ASCE)0899-1561(2004)16:3(257).
  25. Kellouche, Y., Boukhatem, B., Ghrici, M. and Tagnit-Hamou, A. (2019), "Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network", Neur. Comput. Appl., 31(2), 969-988. https://doi.org/10.1007/s00521-017-3052-2.
  26. Lal, T., Sharma, S. and Naval, S. (2013), "Reliability of nondestructive tests for hardened concrete strength", Int. J. Eng. Res. Technol., 2, 1-7.
  27. Liu, G. and Zheng, J. (2019), "Prediction model of compressive strength development in concrete containing four kinds of gelled materials with the artificial intelligence method", Appl. Sci., 9(6), 1039. https://doi.org/10.3390/app9061039.
  28. Malek, J. and Kaouther, M. (2014), "Destructive and nondestructive testing of concrete structures", Jordan J. Civil Eng., 159(3269), 1-10.
  29. Mohammed, B.S., Abdullahi, M. and Hoong, C.K. (2014), "Statistical models for concrete containing wood chipping as partial replacement to fine aggregate", Constr. Build. Mater., 55, 13-19. https://doi.org/10.1016/j.conbuildmat.2014.01.021.
  30. Nash't, I.H., A'bour, S.H. and Sadoon, A.A. (2005), "Finding an unified relationship between crushing strength of concrete and non destructive tests", Middle East Nondestructive Testing Conference & Exhibition, Citeseer.
  31. Nasir, M., Gazder, U., Maslehuddin, M., Al-Amoudi, O.S.B. and Syed, I.A. (2020), "Prediction of properties of concrete cured under hot weather using multivariate regression and ANN models", Arab. J. Sci. Eng., 1-13. https://doi.org/10.1007/s13369-020-04403-y.
  32. Neville, A.M. (1995), Properties of Concrete, Longman, London.
  33. Nikhil, M.V., Minal, B.R., Deep, C.S., Vijay, G.D., Vishal, T.S. and Shweta, P. (2015), "The use of combined non destructive testing in the concrete strength assessment from laboratory specimens and existing buildings", ICBCCE-2015.
  34. Ozcan, F., Atis, C.D., Karahan, O., Uncuoglu, E. and Tanyildizi, H. (2009), "Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete", Adv. Eng. Softw., 40(9), 856-863. https://doi.org/10.1016/j.advengsoft.2009.01.005.
  35. Pann, K.S., Yen, T., Tang, C.W. and Lin, T.D. (2003), "New strength model based on water-cement ratio and capillary porosity", ACI Mater. J., 100(4), 311-318.
  36. Philips, J. and Mano, R.R. (2016), "Experimental investigation on mechanical properties of polypropylene fibre incorporated concrete with silica fume", Int. J. Civil Eng. Technol., 7, 5.
  37. Qasrawi, H.Y. (2000), "Concrete strength by combined nondestructive methods simply & reliably predicted", Cement Concrete Res., 30(5), 739-746. https://doi.org/10.1016/S0008-8846(00)00226-X.
  38. Raju, S. and Dharmar, B. (2016), "Mechanical properties of concrete with copper slag and fly ash by DT and NDT", Periodica Polytechnica Civil Eng., 60(3), 313-322. https://doi.org/10.3311/PPci.7904.
  39. Sadowski, L., Nikoo, M. and Nikoo, M. (2018), "Concrete compressive strength prediction using the imperialist competitive algorithm", Comput. Concrete, 22(4), 355-363. https://doi.org/10.12989/cac.2018.22.4.355.
  40. Saridemir, M. (2009), "Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic", Adv. Eng. Softw., 40(9), 920-927. https://doi.org/10.1016/j.advengsoft.2008.12.008.
  41. Shariati, M., Mafipour, M.S., Haido, J.H., Yousif, S.T., Toghroli, A., Trung, N.T. and Shariati, A. (2020), "Identification of the most influencing parameters on the properties of corroded concrete beams using an Adaptive Neuro-Fuzzy Inference System (ANFIS)", Steel Compos. Struct., 34(1), 155-170. https://doi.org/10.12989/scs.2020.34.1.155.
  42. Soshiroda, T., Voraputhaporn, K. and Nozaki, Y. (2006), "Earlystage inspection of concrete quality in structures by combined nondestructive method", Mater. Struct., 39(2), 149. https://doi.org/10.1617/s11527-005-9007-6.
  43. Topcu, I.B. and Saridemir, M. (2007), "Prediction of properties of waste AAC using artificial neural network", Comput. Mater. Sci., 41(1), 117-125. https://doi.org/10.1016/j.commatsci.2007.03.010.
  44. Toutanji, H., Delatte, N., Aggoun, S., Duval, R. and Danson, A. (2004), "Effect of supplementary cementitious materials on the compressive strength and durability of short-term cured concrete", Cement Concrete Res., 34(2), 311-319. https://doi.org/10.1016/j.cemconres.2003.08.017.
  45. Xu, C., Zhang, X., Haido, J.H., Mehrabi, P., Shariati, A., Mohamad, E.T., ... & Wakil, K. (2019), "Using genetic algorithms method for the paramount design of reinforced concrete structures", Struct. Eng. Mech., 71(5), 503-513. https://doi.org/10.12989/sem.2019.71.5.503.