Advances in concrete construction

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Effect of aggregate mineralogical properties on high strength concrete modulus of elasticity

  • Kaya, Mustafa (Faculty of Engineering, Aksaray University) ;
  • Komur, M. Aydin (Faculty of Engineering, Aksaray University) ;
  • Gursel, Ercin (Faculty of Engineering, Aksaray University)
  • Received : 2021.01.15
  • Accepted : 2022.05.18
  • Published : 2022.06.25
⟨ Previous Next ⟩

Abstract

Aggregates mineralogical, and petrographic properties directly affect the mechanical properties of the produced high strength. This study is focused on the effects of magmatic, sedimentary, and metamorphic aggregates on the performance of high strength concrete. In this study, the effect of the mineralogical properties of aggregates on the compressive strength and modulus of elasticity of high-strength concrete was estimated by Artifical Neural Network (ANN). To estimate the compressive strength and elasticity modules, 96 test specimens were produced. After 28 days under suitable conditions, tests were carried out to determine the compressive strength and modulus of elasticity of the test specimens. This study also focused on the application of artificial neural networks (ANN) to predict the 28-day compressive strength and the modulus of elasticity of high-strength concrete. An ANN model is developed, trained, and tested by using the available test data obtained from the experimental studies. The ANN model is found to predict the modulus of elasticity, and 28 days compressive strength of high strength concrete well, within the ranges of the input parameters. These comparisons show that ANNs have a strong potential to predict the compressive strength and modulus of elasticity of high-strength concrete over the range of input parameters considered.

Keywords

References

  1. Adeli, H. and Yeh C. (1998), "Perceptron learning in engineering design", Comput. Aid. Civil Infrastr. Eng., 4, 247-256. https://doi.org/10.1111/j.1467-8667.1989.tb00026.x.
  2. Adhikary, B.B. and Mutsuyoshi, H. (2006), "Prediction of shear strength of steel fiber RC beams using neural networks", Constr. Build. Mater., 20, 801-811. https://doi.org/10.1016/j.conbuildmat.2005.01.047.
  3. Aguilar, V., Sandoval, C., Adam, J.M., Garzon-Roca, J. and Valdebenito, G. (2016), "Prediction of the shear strength of reinforced masonry walls using a large experimental database and artificial neural networks", Struct. Infrastr. Eng., 12(12), 1661-1674. https://doi.org/10.1080/15732479.2016.1157824.
  4. Amir, H.N. and Fakirhan, R.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.
  5. Armaghani, D.J. and Asteris, P.G. (2020), "A comparative study of ANN, and ANFIS models for the prediction of cement-based mortar materials compressive strength", Neur. Comput. Appl., 33(9), 4501-4532. https://doi.org/10.1007/s00521-020-05244-4.
  6. Asteris, P.G. and Mokos, V.G. (2020), "Concrete compressive Strength using Artificial Neural Networks", Neur. Comput. Appl., 32, 1807-11826. https://doi.org/10.1007/s00521-019-04663-2.
  7. Asteris, P.G. and Plevris, V. (2016), "Anisotropic masonry failure criterion using artificial neural networks", Neur. Comput. Appl., 28(8), 2207-2229. https://doi.org/10.1007/s00521-016-2181-3.
  8. Asteris, P.G. and Plevris, V. (2016), "Anisotropic masonry failure criterion using artificial neural networks", Neur. Comput. Appl., 28(8), 2207-2229. https://doi.org/10.1007/s00521-016-2181-3.
  9. Asteris, P.G., Apostolopoulou, M., Armaghani, D.J., Cavaleri, L., Chountalas, A.T., Guney, D., ... & Nguyen, H. (2020), "On the metaheuristic models for the prediction of cement-metakaolin mortars compressive strength", Metah. Comput. Appl., 1(1), 63-99. http://doi.org/10.12989/mca.2020.1.1.063.
  10. Asteris, P.G., Koopialipoor, M., Armaghani, D.J., Kotsonis, E.A. and Lourenco, P.B. (2021), "Prediction of cement-based mortars compressive strength using machine learning techniques", Neur. Comput. Appl., 33(19), 13089-13121. https://doi.org/10.1007/s00521-021-06004-8.
  11. Asteris, P.G., Tsaris, A.K., Cavaleri, L., Repapis, C.C., Papalou, A., Trapani, F.D. and Karypidis, D.F. (2016), "Prediction of the fundamental period of infilled RC frame structures using artificial neural networks", Comput. Intel. Neurosci., 12, 5104907. https://doi.org/10.1155/2016/5104907.
  12. Bai, J., Wild, S., Ware, J.A. and Sabir, B.B. (2016), "Using neural networks to predict workability of concrete incorporating metakaolin, and fly ash", Adv. Eng. Softw., 34, 663-669. https://doi.org/10.1016/S0965-9978(03)00102-9.
  13. Beushausen, H. and Dittmer, T. (2015), "The influence of aggregate type on the strength, and elastic modulus of high-strength concrete", Constr. Build. Mater., 74, 132-139. https://doi.org/10.1016/j.conbuildmat.2014.08.055.
  14. Bogas, J.A. and Gomes, A. (2013), "Compressive behavior, and failure modes of structural lightweight aggregate concrete characterization, and strength prediction", Mater. Des., 46, 832-841. https://doi.org/10.1016/j.matdes.2012.11.004.
  15. Demir, A. (2015), "Prediction of hybrid fiber-added concrete strength using artificial neural Networks", Comput. Concrete, 15(4), 503-514. http://doi.org/10.12989/cac.2015.15.4.503.
  16. Demir, F. (2008), "Prediction of elastic modulus of normal, and high strength concrete by artificial neural Networks", Constr. Build. Mater., 22(7), 1428-1435. https://doi.org/10.1016/j.conbuildmat.2007.04.004.
  17. Demir, F. and Korkmaz, KA. (2008), "Prediction of lower, and upper bounds of elastic modulus of high strength concrete", Constr. Build. Mater., 22(7), 1385-1393. https://doi.org/10.1016/j.conbuildmat.2007.04.012.
  18. Dias, W.P.S. and Pooliyadda, S.P. (2011), "Neural networks for predicting properties of concretes with admixtures", Constr. Build. Mater., 15, 371-379. https://doi.org/10.1016/S0950-0618(01)00006-X.
  19. Duan, J., Asteris, P.G., Nguyen, H. Bui, X.N. and Moayedi, H. (2020), "A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost Model", Eng. Comput., 37(4), 3329-3346. https://doi.org/10.1007/s00366-020-01003-0.
  20. Duan, Z.H., Kou, S.C. and Poon, C.S, (2013), "Prediction of compressive strength of recycled aggregate concrete using artificial neural Networks", Constr. Build. Mater., 40, 1200-1206. https://doi.org/10.1016/j.conbuildmat.2012.04.063.
  21. Erdem, R.T., Kantar, E., Gucuyen, E. and Anil, O. (2013). "Estimation of compression strength of polypropylene fibre reinforced concrete using artificial neural networks", Comput. Concrete, 12(5), 613-625. https://doi.org/10.12989/cac.2013.12.5.613.
  22. Galobarde,s I., Cavalaro, S.H., Goodier, C.I., Austin, S. and Rueda, A. (2015), "Maturity method to predict the evolution of the properties of sprayed concrete", Constr. Build. Mater., 79, 357-369. https://doi.org/10.1016/j.conbuildmat.2014.12.038.
  23. Garzon, R.J., Adam, J.M.S., Andoval, C. and Roca, P. (2013), "Estimation of the axial behavior of masonry walls based on artificial neural networks", Comput. Struct., 125, 145-152. https://doi.org/10.1016/j.compstruc.2013.05.006.
  24. Garzon, R.J., Marco, C.O. and Adam, J.M. (2013), "Compressive strength of masonry made of clay bricks, and cement mortar: Estimation based on neural networks, and fuzzy logic", Eng. Struct., 48, 21-27. https://doi.org/10.1016/j.engstruct.2012.09.029.
  25. Hakim, S.J.S., Noorzaei, J., Jaafar, M.S., Jameel, M. and Mohammad hassani, M. (2011), "Application of artificial neural networks to predict compressive strength of high strength concrete", Int. J. Phys. Sci., 6, 975-981.
  26. 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.
  27. Haykin, S. (1994), Neural Networks: A Comprehensive Foundation, Prentice-Hall, New Jersey.
  28. Jin, N.J., Seung, I., Choi, Y.S. and Yeon, J. (2017), "Prediction of early-age compressive strength of epoxy resin concrete using the maturity method", Constr. Build. Mater., 152, 990-998. https://doi.org/10.1016/j.conbuildmat.2017.07.066.
  29. Kasabov, N.K. (1996), Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, MIT Press, Cambridge, MA.
  30. Kasperkiewicz, J., Racz, J. and Dubrawski, A. (1995), "HPC strength prediction using artificial neural network", J. Comput. Civil Eng., 9, 279-284. https://doi.org/10.1061/(ASCE)0887-3801(1995)9:4(279).
  31. Kumar, S. and Barai, S.V. (2010), "Neural networks modeling of shear strength of SFRC corbels without stirrups", Appl. Soft Comput., 10, 135-148. https://doi.org/10.1016/j.asoc.2009.06.012.
  32. Lai, S. and Serra, M. (1997), "Concrete strength prediction by means of neural network", Constr. Build. Mater., 11, 93-98. https://doi.org/10.1016/S0950-0618(97)00007-X.
  33. Lee, S.C. (2003), "Prediction of concrete strength using artificial neural networks", Eng. Struct., 25, 849-857. https://doi.org/10.1016/S0141-0296(03)00004-X.
  34. Malhotra, V.M. and Mehta, P.K. (2002), "High performance, high volume fly ash concrete supplementary cementing materials for sustainable development", Proceedings of The International Workshop on Sustainable Development and Concrete Technology, Iowa State University, Ames, IA, USA.
  35. Mazloom, M. and Yoosefi, M.M. (2013), "Predicting the indirect tensile strength of self-compacting concrete using artificial neural Networks", Comput. Concrete, 12(3), 285-301. https://doi.org/10.12989/cac.2013.12.3.285.
  36. Mohammadi, E. and Behnood, AG. (2018), "Application of soft computing methods for predicting the elastic modulus of recycled aggregate concrete", J. Clean. Prod., 176, 1163-1176. https://doi.org/10.1016/j.jclepro.2017.11.186.
  37. Ozkan, I.A. and Altin, M. (2015), "Estimating of compressive strength of concrete with artificial neural network according to concrete mixture ratio and age", Int. J. Intel. Syst. Appl. Eng., 4(3), 76-79. https://doi.org/10.18201/ijisae.263977.
  38. Oztas, A., Pala, M., O zbay, E.A., Kanca, E.A., Caglar, N. and Bhatti, M.A. (2006), "Predicting compressive strength, and slump of high strength concrete using neural network", Constr. Build. Mater., 20, 769-775. https://doi.org/10.1016/j.conbuildmat.2005.01.054.
  39. Parichatprecha, R. and Nimityongskul, P. (2009), "Analysis of durability of high performance concrete using artificial neural networks", Constr. Build. Mater., 23, 910-917. https://doi.org/10.1016/j.conbuildmat.2008.04.015.
  40. Plevris, V. and Asteris, P.G. (2014), "Modeling the masonry failure surface under biaxial compressive stress using neural networks", Constr. Build. Mater., 55, 447-461. https://doi.org/10.1016/j.conbuildmat.2014.01.041.
  41. Plevris, V. and Asteris, P.G. (2014), "Modeling the masonry failure surface under biaxial compressive stress using neural networks", Constr. Build. Mater., 55, 447-461. https://doi.org/10.1016/j.conbuildmat.2014.01.041.
  42. Rumelhart, D.E. and MacClell, J.L. (1986), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, MIT Press, Cambridge, MA.
  43. Shah, S.P. (1993), "Recent trends in the science, and technology of concrete, concrete technology, new trends, industrial applications", Proceedings of the International RILEM Workshop, London, E& FN Spon.
  44. Topcu, I.B. and Saridemir, M. (2008), "Prediction of rubberized concrete properties using artificial neural network, and fuzzy logic", Constr. Build. Mater., 22, 532-540. https://doi.org/10.1016/j.conbuildmat.2006.11.007.
  45. TS 3502 (2002), Determination of Static Elasticity Modulus, and Poisson Ratio of Concrete, Turkish Standardization Institute, Ankara, Turkey.
  46. TS EN 12390-3 (2003), Testing Hardened Concrete-Part 3: Compressive Strength of Test Specimens, Turkish Standardization Institute, Ankara, Turkey.
  47. Yeh, I.C. (1999), "Design of high-performance concrete mixture using neural Networks, and nonlinear programming", J. Comput. Civil Eng., 13(1), 36-42. https://doi.org/10.1061/(ASCE)0887-3801(1999)13:1(36).