Computers and Concrete

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Artificial intelligence for the compressive strength prediction of novel ductile geopolymer composites

  • Yaswanth, K.K. (Department of Civil Engineering, School of Infrastructure, B.S. Abdur Rahman Crescent Institute of Science & Technology) ;
  • Revathy, J. (Department of Civil Engineering, School of Infrastructure, B.S. Abdur Rahman Crescent Institute of Science & Technology) ;
  • Gajalakshmi, P. (Department of Civil Engineering, School of Infrastructure, B.S. Abdur Rahman Crescent Institute of Science & Technology)
  • Received : 2020.10.13
  • Accepted : 2021.06.09
  • Published : 2021.07.25
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Abstract

Engineered Geopolymer Composites has proved to be an excellent eco-friendly strain hardening composite materials, as well as, it exhibits high tensile strain capacity. An intelligent computing tool based predictive model to anticipate the compressive strength of ductile geopolymer composites would help various researchers to analyse the material type and its contents; the dosage of fibers; producing tailor-made materials; less time consumption; cost-saving etc., which could suit for various infrastructural applications. This paper attempts to develop a suitable ANN based machine learning model in predicting the compressive strength of strain hardening geopolymer composites with greater accuracy. A simple ANN network with a various number of hidden neurons have been trained, tested and validated. The results revealed that with seventeen inputs and one output parameters respectively for mix design & compressive strength and thirteen hidden neurons in its layer have provided the notable prediction with R2 as 96% with the RMSE of 2.64. It is concluded that a simple ANN model would have the perspective of estimating the compressive strength properties of engineered geopolymer composite to an accuracy level of more than 90%. The sensitivity analysis of ANN model with 13-hidden neurons, also confirms the accuracy of prediction of compressive strength.

Keywords

References

  1. Al-mashhadani, M., Canpolat, O., Aygormez, Y., Uysal, M. and Erdem, S. (2018), "Mechanical and microstructural characterization of fiber reinforced fly ash based geopolymer composites", Constr. Build. Mater., 167, 505-513. https://doi.org/10.1016/j.conbuildmat.2018.02.061.
  2. Altwair, N.M., Megat Johari, M.A. and Saiyid Hashim, S.F. (2012), "Flexural performance of green engineered cementitious composites containing high volume of palm oil fuel ash", Constr. Build. Mater., 37, 518-525. https://doi.org/10.1016/j.conbuildmat.2012.08.003.
  3. Ammasi, A.K. (2018), "Strength and durability of high volume fly ash in engineered cementitious composites", Mater. Today: Proc., 5(11), 24050-24058. https://doi.org/10.1016/j.matpr.2018.10.198.
  4. Asteris, P.G., Apostolopoulou, M., Skentou, A.D. and Moropoulou, A. (2019), "Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars", Comput. Concrete, 24(4), 329-345. https://doi.org/10.12989/cac.2019.24.4.329.
  5. Bai, J., Wild, S., Ware, J. and Sabir, B. (2003), "Using neural networks to predict workability of concrete incorporating metakaolin and fly ash", Adv. Eng. Softw., 34(11-12), 663-669. https://doi.org/10.1016/s0965-9978(03)00102-9.
  6. Bang, J.W., Ganesh Prabhu, G., Jang, Y.I. and Kim, Y.Y. (2015), "Development of ecoefficient engineered cementitious composites using supplementary cementitious materials as a binder and bottom ash aggregate as fine aggregate", Int. J. Polym. Sci., 2015, Article ID 681051. https://doi.org/10.1155/2015/681051.
  7. Bilgehan, M. and Turgut, P. (2010), "The use of neural networks in concrete compressive strength estimation", Comput. Concrete, 7(3), 271-283. https://doi.org/10.12989/cac.2010.7.3.271.
  8. Camoes, A. and Martins, F.F. (2017), "Compressive strength prediction of CFRP confined concrete using data mining techniques", Comput. Concrete, 19(3), 233-241. https://doi.org/10.12989/cac.2017.19.3.233.
  9. Chen, G., Wang, H., Bezold, A., Broeckmann, C., Weichert, D. and Zhang, L. (2019), "Strengths prediction of particulate reinforced metal matrix composites (PRMMCs) using direct method and artificial neural network", Compos. Struct., 223(1), 110951. https://doi.org/10.1016/j.compstruct.2019.110951.
  10. Chopra, P., Sharma, R.K. and Kumar, M. (2016), "Prediction of compressive strength of concrete using artificial neural network and genetic programming", Adv. Mater. Sci. Eng., Article ID 7648467, 1-10. https://doi.org/10.1155/2016/7648467.
  11. Dao, D., Ly, H.B., Trinh, S., Le, T.T. and Pham, B. (2019), "Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete", Mater., 12(6), 983. https://doi.org/10.3390/ma12060983.
  12. Ekaputri, J.J. and Junaedi, S. (2017), "Effect of curing temperature and fiber on metakaolin-based geopolymer", Procedia Eng., 171, 572-583. https://doi.org/10.1016/j.proeng.2017.01.376.
  13. Farooq, M., Bhutta, A. and Banthia, N. (2019), "Tensile performance of eco-friendly ductile geopolymer composites (EDGC) incorporating different micro-fibers", Cement Concrete Compos., 103, 183-192. https://doi.org/10.1016/j.cemconcomp.2019.05.004.
  14. Garson, G.D. (1991), "Interpreting neural network connection weights", AI Expert, 6, 47-51.
  15. Hossain, K.M.A., Anwar, M.S. and Samani, S.G. (2016), "Regression and artificial neural network models for strength properties of engineered cementitious composites", Neur. Comput. Appl., 29(9), 631-645. https://doi.org/10.1007/s00521-016-2602-3.
  16. Jayaseelan, R., Pandulu, G. and Ashwini, G. (2019), "Neural networks for the prediction of fresh properties and compressive strength of flowable concrete", J. Urban Environ. Eng., 13(1), 183-197. https://doi.org/10.4090/juee.2019.v13n1.183197bagha.
  17. Kan, L., Zhang, L., Zhao, Y. and Wu, M. (2020), "Properties of polyvinyl alcohol fiber reinforced fly ash based Engineered Geopolymer Composites with zeolite replacement", Constr. Build. Mater., 231, 117161. https://doi.org/10.1016/j.conbuildmat.2019.117161.
  18. Kan, L.L., Wang, W.S., Liu, W.D. and Wu, M. (2020), "Development and characterization of fly ash based PVA fiber reinforced Engineered Geopolymer Composites incorporating metakaolin", Cement Concrete Compos., 108, 103521. https://doi.org/10.1016/j.cemconcomp.2020.103521.
  19. Khademi, F., Akbari, M. and Jamal, S.M. (2017), "Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete", Front. Struct. Civil Eng., 11, 90-99. https://doi.org/10.1007/s11709-016-0363-9.
  20. Krishnaraja, A.R. and Kandasamy, S. (2017), "Flexural performance of engineered cementitious composite layered reinforced concrete beams", Arch. Civil Eng., 63(4), 173-188. https://doi.org/10.1515/ace-2017-0048.
  21. Kumar, B. and Topping, B.H. (1999), Artificial Intelligence Applications in Civil and Structural Engineering, Civil-Comp.
  22. Li, V.C. (2003), "Engineered cementitious composites (ECC): a review of the material and its applications", J. Adv. Concrete Technol., 1(3), 215-230. https://doi.org/10.3151/jact.1.215.
  23. Li, V.C. (2019), Engineered Cementitious Composites (ECC): Bendable Concrete For Sustainable And Resilient Infrastructure, Springer Nature.
  24. Li, W. and Du, H. (2018), "Properties of pva fiber reinforced geopolymer mortar", International Congress on Polymers in Concrete (ICPIC 2018), 557-564. https://doi.org/10.1007/978-3-319-78175-4_71.
  25. Ling, Y., Wang, K., Li, W., Shi, G. and Lu, P. (2019), "Effect of slag on the mechanical properties and bond strength of fly ash-based engineered geopolymer composites", Compos. Part B, 231, 117161. https://doi.org/10.1016/j.compositesb.2019.01.092.
  26. Lingam, A. and Karthikeyan, J. (2014), "Prediction of compressive strength for HPC mixes containing different blends using ANN", Comput. Concrete, 13(5), 621-632. https://doi.org/10.12989/cac.2014.13.5.621.
  27. Liu, F., Ding, W. and Qiao, Y (2020), "An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag power", Front. Struct. Civil Eng., 14, 1299-1315. https://doi.org/10.1007/s11709-020-0712-6.
  28. Liu, J.C. and Zhang, Z. (2020), "Neural network models to predict explosive spalling of PP fiber reinforced concrete under heating", J. Build. Eng., 32, 101472. https://doi.org/10.1016/j.jobe.2020.101472.
  29. Mohammed, B.S., Khed, V.C. and Liew, M.S. (2018), "Optimization of hybrid fibres in engineered cementitious composites", Constr. Build. Mater., 190, 24-37. https://doi.org/10.1016/j.conbuildmat.2018.08.188.
  30. Nematollahi, B., Ranade, R., Sanjayan, J. and Ramakrishnan, S. (2017), "Thermal and mechanical properties of sustainable lightweight strain hardening geopolymer composites", Arch. Civil Mech. Eng., 17(1), 55-64. https://doi.org/10.1016/j.acme.2016.08.002.
  31. Nematollahi, B., Sanjayan, J. and Shaikh, F.U.A. (2014), "Comparative deflection hardening behavior of short fiber reinforced geopolymer composites", Constr. Build. Mater., 70, 54-64. https://doi.org/10.1016/j.conbuildmat.2014.07.085.
  32. Nematollahi, B., Sanjayan, J. and Shaikh, F.U.A. (2016), "Matrix design of strain hardening fiber reinforced engineered geopolymer composite", Compos. Part B, 89, 253-265. https://doi.org/10.1016/j.compositesb.2015.11.039.
  33. Ohno, M. and Li, V.C. (2018), "An integrated design method of engineered geopolymer composite", Cement Concrete Compos., 88, 73-85. https://doi.org/10.1016/j.cemconcomp.2018.02.001.
  34. Parichatprecha, R. and Nimityongskul, P. (2009), "An integrated approach for optimum design of HPC mix proportion using genetic algorithm and artificial neural networks", Comput. Concrete, 6(3), 253-268. https://doi.org/10.12989/cac.2009.6.3.253.
  35. Pourfalah, S. (2018), "Behaviour of engineered cementitious composites and hybrid engineered cementitious composites at high temperatures", Constr. Build. Mater., 158, 921-937. https://doi.org/10.1016/j.conbuildmat.2017.10.077.
  36. Sheela, K.G. and Deepa, S.N. (2013). "Review on methods to fix number of hidden neurons in neural networks", Math. Prob. Eng., 2013, Article ID 425740. https://doi.org/10.1155/2013/425740.
  37. Shi, L., Lin, S.T.K., Lu, Y., Ye, L. and Zhang, Y.X. (2018), "Artificial neural network based mechanical and electrical property prediction of engineered cementitious composites", Constr. Build. Mater., 174, 667-674. https://doi.org/10.1016/j.conbuildmat.2018.04.127.
  38. Shirkhani, A., Davarnia, D. and Azar, B.F. (2019), "Prediction of bond strength between concrete and rebar under corrosion using ANN", Comput. Concrete, 23(4), 273-279. https://doi.org/10.12989/cac.2019.23.4.273.
  39. 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.
  40. Topping, B.H., Neves, L.C. and Barros, R.C. (2008), Trends in Civil and Structural Engineering Computing, Saxe-Coburg Publications.
  41. Towards Data Science (2020), Concrete Compressive Strength Prediction using Machine Learning TDS,
  42. Tugrul, E., Erkan, K., Engin, G. and Ozgur, A. (2013), "Estimation of compression strength of polypropylene fibre reinforced concrete using artificial neural networks", Comput. Concrete, 12(5), 613-625. http://doi.org/10.12989/cac.2013.12.5.613.
  43. Uneb, G., Omar, S.B., Saad Muhammad, S. and Mohammad M. (2017), "Predicting compressive strength of blended cement concrete with ANNs", Comput. Concrete, 20(6), 627-634. http://doi.org/10.12989/cac.2017.20.6.627.
  44. Xu, F., Deng, X., Peng, C., Zhu, J. and Chen, J. (2017), "Mix design and flexural toughness of PVA fiber reinforced fly ash-geopolymer composites", Constr. Build. Mater., 150, 179-189. http://doi.org/10.1016/j.conbuildmat.2017.05.172.
  45. Yan, F., Lin, Z., Wang, X., Azarmi, F. and Sobolev, K. (2017), "Evaluation and prediction of bond strength of GFRP-bar reinforced concrete using artificial neural network optimized with genetic algorithm", Compos. Struct., 161, 441-452. http://doi.org/10.1016/j.compstruct.2016.11.068.
  46. Yeh, I..C. (2008), "Modeling slump of concrete with fly ash and superplasticizer", Comput. Concrete, 5(6), 559-572. https://doi.org/10.12989/cac.2008.5.6.559.
  47. Yeh, I.C. (2008), "Prediction of workability of concrete using design of experiments for mixtures", Comput. Concrete, 5(1), 1-20. https://doi.org/10.12989/cac.2008.5.1.001.
  48. Yu, J. and Leung, C.K.Y. (2017), "Strength improvement of strain-hardening cementitious composites with ultrahigh-volume fly ash", J. Mater. Civil Eng., 29(9), 1-10. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001987.
  49. Yu, J., Yao, J., Lin, X., Li, H., Lam, J. Y.K., Leung, C.K.Y. and Shih, K. (2018), "Tensile performance of sustainable strain-hardening cementitious composites with hybrid pva and recycled pet fibers", Cement Concrete Res., 107, 110-123. https://doi.org/10.1016/j.cemconres.2018.02.013.
  50. Zahid, M. and Shafiq, N. (2020), "Effects of sand/fly ash and the water/solid ratio on the mechanical properties of engineered geopolymer composite and mix design optimization", Min., 10(4), 333. https://doi.org/10.3390/min10040333.