Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Long-term quality control of self-compacting semi-lightweight concrete using short-term compressive strength and combinatorial artificial neural networks

  • Mazloom, Moosa (Department of Civil Engineering, Shahid Rajaee Teacher Training University) ;
  • Tajar, Saeed Farahani (Department of Civil Engineering, Shahid Rajaee Teacher Training University) ;
  • Mahboubi, Farzan (Department of Civil Engineering, Shahid Rajaee Teacher Training University)
  • Received : 2019.04.17
  • Accepted : 2020.04.03
  • Published : 2020.05.25
⟨ Previous Next ⟩

Abstract

Artificial neural networks are used as a useful tool in distinct fields of civil engineering these days. In order to control long-term quality of Self-Compacting Semi-Lightweight Concrete (SCSLC), the 90 days compressive strength is considered as a key issue in this paper. In fact, combined artificial neural networks are used to predict the compressive strength of SCSLC at 28 and 90 days. These networks are able to re-establish non-linear and complex relationships straightforwardly. In this study, two types of neural networks, including Radial Basis and Multilayer Perceptron, were used. Four groups of concrete mix designs also were made with two water to cement ratios (W/C) of 0.35 and 0.4, as well as 10% of cement weight was replaced with silica fume in half of the mixes, and different amounts of superplasticizer were used. With the help of rheology test and compressive strength results at 7 and 14 days as inputs, the neural networks were used to estimate the 28 and 90 days compressive strengths of above-mentioned mixes. It was necessary to add the 14 days compressive strength in the input layer to gain acceptable results for 90 days compressive strength. Then proper neural networks were prepared for each mix, following which four existing networks were combined, and the combinatorial neural network model properly predicted the compressive strength of different mix designs.

Keywords

References

  1. ACI 213 R-87 (1998), Guide for Structural Lightweight Aggregate Concrete.
  2. Afzali Naniz, O. and Mazloom, M. (2018), "Effects of colloidal nano-silica on fresh and hardened properties of self-compacting lightweight concrete", J. Build. Eng., 20, 400-410. https://doi.org/10.1016/j.jobe.2018.08.014.
  3. Afzali Naniz, O. and Mazloom, M. (2019a), "Assessment of the influence of micro and Nano-silica on the behavior of self-compacting lightweight concrete using full factorial design", Asian J. Civil Eng., 20(1), 57-70. https://doi.org/10.1007/s42107-018-0088-2.
  4. Afzali Naniz, O. and Mazloom, M. (2019b), "Fracture behavior of self-compacting semi-lightweight concrete containing nano-silica", Adv. Struct. Eng., 22(10), 2264-2277. https://doi.org/10.1177/1369433219837426.
  5. Alshihri, M., Azmey, A.M. and EL-Bisy, M.S. (2009), "Neural network for predicting compressive strength of structural lightweight concrete", Constr. Build. Mater., 23, 2214-2219. https://doi.org/10.1016/j.conbuildmat.2008.12.003.
  6. Altun, F., Kisi, O. and Aydin, K. (2008), "Predicting the compressive strength of steel fiber added lightweight concrete using neural network", Comput. Mater. Sci., 42, 259-265. https://doi.org/10.1016/j.commatsci.2007.07.011.
  7. Cheng, Y.I. (2007), "Modeling slump flow of concrete second-order regression and artificial neural network", Cement Concrete Compos., 29, 474-480. https://doi.org/10.1016/j.cemconcomp.2007.02.001.
  8. Choi, Y.W., Kim, Y.J., Shin, H.C. and Moon, H.Y. (2006), "An experimental research on the fluidity and mechanical properties of high-strength lightweight self-compacting concrete", Cement Concrete Res., 36, 1595-1602. https://doi.org/10.1016/j.cemconres.2004.11.003.
  9. Demirboga, R., Orung, I. and Gul, R. (2001), "Effect of expanded perlite aggregate and mineral admixtures on compressive strength of Low-density concrete", Cement Concrete Res., 31, 627-632. https://doi.org/10.1016/S0008-8846(01)00615-9.
  10. EFNARC (2002), Specification and Guidelines for Self-Compacting Concrete, EUROPEAN Federation.
  11. Elshafey, A., Dawood, N., Marzouk, H. and Haddara, M. (2013), "Crack width in concrete using artificial neural network", Eng. Struct., 52, 676-686. https://doi.org/10.1016/j.engstruct.2013.03.020.
  12. Fausett, L. (1993), Fundamental of Neural Network: Architectures, Algorithms and Application, Prentice-Hall, New Jersey.
  13. Karamloo, M., Mazloom, M. and Payganeh, G. (2016a), "Effects of maximum aggregate size on fracture behaviors of self- compacting lightweight concrete", Constr. Build. Mater., 123, 508-515. https://doi.org/10.1016/j.conbuildmat.2016.07.061.
  14. Karamloo, M., Mazloom, M. and Payganeh, G. (2016b), "Influences of water to cement ratio on brittleness and fracture parameters of self-compacting lightweight concrete", Eng. Fract. Mech., 168, 227-241. https://doi.org/10.1016/j.engfracmech.2016.09.011.
  15. Karamloo, M., Mazloom, M. and Payganeh, G. (2017), "Effect of size on nominal strength of self-compacting lightweight concrete and self-compacting normal weight concrete: A stress-based approach", Mater. Tod. Commun., 13, 36-45. https://doi.org/10.1016/j.mtcomm.2017.08.002.
  16. Kardos, M., Kania, P., Budzyna, P., Blachnik, M., Wieczorek, T. and Golak, S. (2012), "Combining the advantages of Neural Networks and decision tree for regression problems in a steel temperature prediction system", HAIS'12 Proceedings of the 7th International Conference on Hybrid Artificial Intelligent Systems.
  17. Kaveh, A. and Servati, H. (2011), "Artificial neural networks in design of structures and analysis", Road, Housing and Development Research Center.
  18. Khasheie, M. and Bijary, M. (2010), "Using the combined model of artificial neural networks with fuzzy regression to predict gold price", Indus. Eng. J., 44(1), 39-47.
  19. Mamdoohi, A.R. and Ardeshiri, A. (2013), "Artificial neural network model for telecommuting demand: A technique to decrease urban traffic", J. Civil Eng. Modar., 13, 149-200.
  20. Mazloom, M. (2008), "Estimating long-term creep and shrinkage of high-strength concrete", Cement Concrete Compos., 30(4), 316-326. https://doi.org/10.1016/j.cemconcomp.2007.09.006.
  21. Mazloom, M. and Hatami, H. (2015), "The behavior of self-compacting light weight concrete produced by magnetic water", Int. J. Civil Envir. Struct. Constr. Arch. Eng., 9(12), 1616-1620. https://doi.org/10.5281/zenodo.1125669.
  22. Mazloom, M. and Mahboubi, F. (2017), "Evaluating the settlement of lightweight coarse aggregate in self-compacting lightweight concrete", Comput. Concrete, 19(2), 203-210. https://doi.org/10.12989/cac.2017.19.2.203.
  23. Mazloom, M. and Miri, M.S. (2017), "Interaction of magnetic water, silica fume and superplasticizer on fresh and hardened properties of concrete", Adv. Concrete Constr., 5(2), 87-99. https://doi.org/10.12989/acc.2017.5.2.087.
  24. Mazloom, M. and Ranjbar, A. (2010), "Relation between the workability and strength of self-compacting concrete", 35th Conference on Our World in Concrete & Structure, Singapore.
  25. 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.
  26. Mazloom, M. Ramezanianpour, A.A. and Brooks, J.J. (2004), "Effect of silica fume on mechanical properties of high-strength concrete", Cement Concrete Compos., 26(1), 347-357. https://doi.org/10.1016/S0958-9465(03)00017-9.
  27. Mazloom, M., Allahabadi, A. and Karamloo, M. (2017), "Effect of silica fume and polyepoxide-based polymer on electrical resistivity, mechanical properties, and ultrasonic response of SCLC", Adv. Concrete Constr., 5(6), 587-611. https://doi.org/10.12989/acc.2017.5.6.587.
  28. Mazloom, M., Homayooni, S.M. and Miri, S.M. (2018), "Effect of rock flour type on rheology and strength of self-compacting lightweight concrete", Comput. Concrete, 21(2), 199-207. https://doi.org/10.12989/cac.2018.21.2.199.
  29. Mazloom, M., Saffari, A. and Mehrvand, M. (2015), "Compressive, shear and torsional strength of beams made of self-compacting concrete", Comput. Concrete, 15(6), 935-950. https://doi.org/10.12989/cac.2015.15.6.935.
  30. Nehdi, M., Chabib, H.E.L. and Naggar, M.EL. (2001), "Predicting performance of self-compacting concrete mixtures using artificial neural networks", ACI Mater. J., 98(5), 394-401.
  31. Okamura, H. (1997), "Self-compacting high performance concrete", Concrete Int., 19(7), 50-54.
  32. Okamura, H., Maekawa, K. and Ozawa, K. (1993), High Performance Concrete, Gihoudou Publisher, Tokyo. (in Japanese)
  33. Ozawa, K., Sakata, N. and Okamura, H. (1995), "Evaluation of self-compactibility of fresh concrete using the funnel test", Concrete Lib. JSCE, 25, 59-75. https://doi.org/10.2208/jscej.1994.490_61.
  34. Oztas, A., Pala, E., Ozbay, E., Kanca, N., Caglar, A. and Bhatti, M. (2006), "Predicting the 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.
  35. Pedrycz, W. (1998), "Conditional fuzzy clustering in the design of radial basis function neural network", IEEE Tran. Neur. Netw, 9(4), 601-612. https://doi.org/10.1109/72.701174.
  36. Petersson, O., Billberg, P. and Van, B.K. (1996), "A model for self- compacting concrete", Proceedings of International RILEM Conference on Production Methods and Workability of Concrete, Eds. Bartos, P.J.M. et al., Paisley, 483-490.
  37. Rossignolo, J.A., Agnesini, M.V.C. and Morais, J.A. (2003), "Properties of high performance LWAC for precast structures with Brazilian lightweight aggregate", Cement Concrete Compos., 25, 77-82. https://doi.org/10.1016/S0958-9465(01)00046-4.
  38. Salehi, H. and Mazloom, M. (2018a), "Experimental and numerical studies on crack propagation in self-compacting lightweight concrete", Modares Mech. Eng., 18(4), 667-678.
  39. Salehi, H. and Mazloom, M. (2018b), "Effect of magnetic-field intensity on fracture behaviors of self-compacting lightweight concrete", Mag. Concrete Res., 71(13), 665-679. https://doi.org/10.1680/jmacr.17.00418.
  40. Salehi, H. and Mazloom, M. (2019), "Opposite effects of ground granulated blast-furnace slag and silica fume on the fracture behavior of self-compacting lightweight concrete", Constr. Build. Mater., 222, 622-632. https://doi.org/10.1016/j.conbuildmat.2019.06.183.
  41. Siddique, R., Aggrawal, P. and Aggrawal, Y. (2011), "Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural network", Adv. Eng. Softw., 42, 780-786. https://doi.org/10.1016/j.advengsoft.2011.05.016.
  42. Takada, K., Pelora, G. and Walraven, J.C. (2001), "influence of mixing efficiency on the mixture proportions of general purpose self- compacting concrete", University of Sherbrook.
  43. Yasar, E., Atis, C.D., Kilic, A. and Gulsen, H. (2003), "Strength properties of lightweight concrete made with basaltic pumice and fly-ash", Mater. Lett., 57, 2267-2270. https://doi.org/10.1016/S0167-577X(03)00146-0.
  44. Yi, S.T., Kim, J.K. and Oh, T.K. (2003), "Effect of strength and age on the stress-strain curves of concrete specimens", Cement Concrete Res., 33, 1235-1244. https://doi.org/10.1016/S0008-8846(03)00044-9.

Cited by

  1. A neuro-fuzzy approach to predict the shear contribution of end-anchored FRP U-jackets vol.26, pp.5, 2020, https://doi.org/10.12989/cac.2020.26.5.397