Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

An assessment of machine learning models for slump flow and examining redundant features

  • Unlu, Ramazan (Management and Information Systems, Gumushane University)
  • Received : 2018.12.17
  • Accepted : 2020.05.24
  • Published : 2020.06.25
⟨ Previous Next ⟩

Abstract

Over the years, several machine learning approaches have been proposed and utilized to create a prediction model for the high-performance concrete (HPC) slump flow. Despite HPC is a highly complex material, predicting its pattern is a rather ambitious process. Hence, choosing and applying the correct method remain a crucial task. Like some other problems, prediction of HPC slump flow suffers from abnormal attributes which might both have an influence on prediction accuracy and increases variance. In recent years, different studies are proposed to optimize the prediction accuracy for HPC slump flow. However, more state-of-the-art regression algorithms can be implemented to create a better model. This study focuses on several methods with different mathematical backgrounds to get the best possible results. Four well-known algorithms Support Vector Regression, M5P Trees, Random Forest, and MLPReg are implemented with optimum parameters as base learners. Also, redundant features are examined to better understand both how ingredients influence on prediction models and whether possible to achieve acceptable results with a few components. Based on the findings, the MLPReg algorithm with optimum parameters gives better results than others in terms of commonly used statistical error evaluation metrics. Besides, chosen algorithms can give rather accurate results using just a few attributes of a slump flow dataset.

Keywords

References

  1. Anderson, D. and George, M. (1992), "Artificial neural networks technology", Kaman Sci. Corp., 258(6), 1-83.
  2. Aydogmus, H.Y., Erdal, H.I., Karakurt, O., Namli, E., Turkan, Y.S. and Erdal, H. (2015), "A comparative assessment of bagging ensemble models for modeling concrete slump flow", Comput. Concrete, 16(5), 741-757. http://dx.doi.org/10.12989/cac.2015.16.5.741.
  3. Breiman, L. (2001), "Random forests", Mach. Learn., 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
  4. Brouwers, H.J.H. and Radix, H.J. (2005), "Self-compacting concrete: theoretical and experimental study", Cement Concrete Res., 35(11), 2116-2136. https://doi.org/10.1016/j.cemconres.2005.06.002.
  5. Busari, A., Joseph, A. and Bamidele, D. (2018b), "Mechanical properties of dehydroxylated kaolinitic clay in self-compacting concrete for pavement construction", Silicon, 11, 2429-2437. https://doi.org/10.1007/s12633-017-9654-6.
  6. Busari, A.A., Joseph, O.A. and Bamidele, I.D. (2018a), "Review of sustainability in self-compacting concrete: the use of waste and mineral additives as supplementary cementitious materials and aggregates", Portugaliae Electrochimica Acta, 36(3), 147-62. http://dx.doi.org/10.4152/pea.201803147.
  7. Cao, Y.F., Wei, W., Han, L.Z. and Jun, M.P. (2013), "Prediction of the elastic modulus of self-compacting concrete based on svm", Appl. Mech. Mater., 357-360, 1023-1026. https://doi.org/10.4028/www.scientific.net/AMM.357-360.1023.
  8. Chandwani, V., Vinay, A. and Ravindra, N. (2015), "Modeling slump of ready mix concrete using genetic algorithms assisted training of artificial neural networks", Exp. Syst. Appl., 42(2), 885-893. https://doi.org/10.1016/j.eswa.2014.08.048.
  9. Cheng, M.Y., Chou, J.S., Roy, A.F. and Wu, Y.W. (2012), "High-performance concrete compressive strength prediction using time-weighted evolutionary fuzzy support vector machines inference model", Auto. Constr., 28, 106-115. https://doi.org/10.1016/j.autcon.2012.07.004.
  10. Cortes, C. and Vapnik, V. (1995), "Support-vector networks", Mach. Learn., 20(3), 273-297. https://doi.org/10.1007/BF00994018.
  11. Dias, W.P.S. and Pooliyadda, S.P. (2001), "Neural networks for predicting properties of concretes with admixtures", Constr. Build. Mater., 15(7), 371-379. https://doi.org/10.1016/S0950-0618(01)00006-X.
  12. Domone, P. (1998), "The slump flow test for high-workability concrete", Cement Concrete Res., 28(2), 177-182. https://doi.org/10.1016/S0008-8846(97)00224-X.
  13. Ferrara, L., Park, Y.D. and Shah, S.P. (2007), "A method for mix-design of fiber-reinforced self-compacting concrete", Cement Concrete Res., 37(6), 957-971. https://doi.org/10.1016/j.cemconres.2007.03.014.
  14. Gonzalez-Taboada, I., Gonzalez-Fonteboa, B., Martinez-Abella, F. and Seara-Paz, S. (2018), "Evaluation of self-compacting recycled concrete robustness by statistical approach", Constr. Build. Mater., 176, 720-736. https://doi.org/10.1016/j.conbuildmat.2018.05.059.
  15. Habibi, A. and Ghomashi, J. (2018), "Development of an optimum mix design method for self-compacting concrete based on experimental results", Constr. Build. Mater., 168, 113-123. https://doi.org/10.1016/j.conbuildmat.2018.02.113.
  16. Haykin, S. (1994), Neural Networks: a Comprehensive Foundation, Prentice Hall PTR.
  17. Hornik, K., Stinchcombe, M. and White, H. (1989), "Multilayer feedforward networks are universal approximators", Neur. Network., 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  18. Jain, A., Jha, S.K. and Misra, S. (2008), "Modeling and analysis of concrete slump using artificial neural networks", J. Mater. Civil Eng., 20(9), 628-633. https://doi.org/10.1061/(ASCE)0899-1561(2008)20:9(628).
  19. Khatib, J.M. (2008), "Performance of self-compacting concrete containing fly ash", Constr. Build. Mater., 22(9), 1963-1971. https://doi.org/10.1016/j.conbuildmat.2007.07.011.
  20. Lawrence, I. and Lin, K. (1989), "A concordance correlation coefficient to evaluate reproducibility", Biometrics, 255-268. https://doi.org/10.2307/2532051.
  21. Lee, S.C. (2003), "Prediction of concrete strength using artificial neural networks", Eng. Struct., 25(7), 849-857. https://doi.org/10.1016/S0141-0296(03)00004-X.
  22. Mashhadban, H., Kutanaei, S.S. and Sayarinejad, M.A. (2016), "Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network", Constr. Build. Mater., 119, 277-287. https://doi.org/10.1016/j.conbuildmat.2016.05.034.
  23. Massana, J., Reyes, E., Bernal, J., Leon, N. and Sanchez-Espinosa, E. (2018), "Influence of nano-and micro-silica additions on the durability of a high-performance self-compacting concrete", Constr. Build. Mater., 165, 93-103. https://doi.org/10.1016/j.conbuildmat.2017.12.100.
  24. Mechtcherine, V., Gram, A., Krenzer, K., Schwabe, J.H., Shyshko, S. and Roussel, N. (2014), "Simulation of fresh concrete flow using discrete element method (dem): theory and applications", Mater. Struct., 47(4), 615-630. https://doi.org/10.1617/s11527-013-0084-7.
  25. Okamura, H. and Ouchi, M. (2003), "Self-compacting concrete", J. Adv. Concrete Technol., 1(1), 5-15. https://doi.org/10.3151/jact.1.5.
  26. Okamura, H., Ozawa, K. and Ouchi, M. (2000), "Self-compacting concrete", Struct. Concrete, 1(1), 3-17. https://doi.org/10.3151/jact.1.5.
  27. Oztas, A., Pala, M., Ozbay, E., Kanca, E., Caglar, N. and Bhatti, M.A. (2006), "Predicting the compressive strength and slump of high strength concrete using neural network", Constr. Build. Mater., 20(9), 769-75. https://doi.org/10.1016/j.conbuildmat.2005.01.054.
  28. Pelisser, F., Vieira, A. and Bernardin, A.M. (2018), "Efficient self-compacting concrete with low cement consumption", J. Clean. Prod., 175, 324-332. https://doi.org/10.1016/j.jclepro.2017.12.084.
  29. Quinlan, J.R. (1992), "Learning with continuous classes", 5th Australian Joint Conference on Artificial Intelligence, World Scientific, 343-348.
  30. Rosenblatt, F. (1958), "The perceptron: a probabilistic model for information storage and organization in the brain", Psychol. Rev., 65(6), 386. https://doi.org/10.1037/h0042519.
  31. Roussel, N. (2006), "Correlation between yield stress and slump: comparison between numerical simulations and concrete rheometers results", Mater. Struct., 39, 501-509. ttps://doi.org/10.1617/s11527-005-9035-2.
  32. Saak, A.W., Jennings, H.M. and Shah, S.P. (2001), "New methodology for designing self-compacting concrete", Mater. J., 98(6), 429-439.
  33. Sahmaran, M., Christianto, H.A. and Yaman, I.O. (2006), "The effect of chemical admixtures and mineral additives on the properties of self-compacting mortars", Cement Concrete Compos., 28(5), 432-440. https://doi.org/10.1016/j.cemconcomp.2005.12.003.
  34. Sari, M., Prat, E. and Labastire, J.F. (1999), "High strength self-compacting concrete original solutions associating organic and inorganic admixtures", Cement Concrete Res., 29(6), 813-818. https://doi.org/10.1016/S0008-8846(99)00037-X.
  35. 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.
  36. Sonebi, M., Cevik, A., Grunewald, S. and Walraven, J. (2016), "Modelling the fresh properties of self-compacting concrete using support vector machine approach", Constr. Build. Mater., 106, 55-64. https://doi.org/10.1016/j.conbuildmat.2015.12.035.
  37. Su, N., Hsu, K.C. and Chai, H.W. (2001), "A simple mix design method for self-compacting concrete", Cement Concrete Res., 31(12), 1799-1807. https://doi.org/10.1016/S0008-8846(01)00566-X.
  38. Tregger, N., Gregori, A., Ferrara, L. and Shah, S. (2012), "Correlating dynamic segregation of self-consolidating concrete to the slump-flow test", Constr. Build. Mater., 28(1), 499-505. https://doi.org/10.1016/j.conbuildmat.2011.08.052.
  39. Unlu, R. (2019), "A comparative study of machine learning and deep learning for time series forecasting: a case study of choosing the best prediction model for turkey electricity production", J. Nat. Appl. Sci., 23(2), 635-646. https://doi.org/10.19113/sdufenbed.494396.
  40. Uysal, M. and Kemalettin, Y. (2011), "Effect of mineral admixtures on properties of self-compacting concrete", Cement Concrete Compos., 33(7), 771-776. https://doi.org/10.1016/j.cemconcomp.2011.04.005.
  41. Uysal, M. and Sumer, M. (2011), "Performance of self-compacting concrete containing different mineral admixtures", Constr. Build. Mater., 25(11), 4112-4120. https://doi.org/10.1016/j.conbuildmat.2011.04.032.
  42. Vapnik, V., Golowich, S. and Smola, A. (1997), Advances in Neural Information Processing Systems 9, Chapter Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing.
  43. Yeh, I.C. (1999), "Design of high-performance concrete mixture using neural networks and nonlinear programming", J. Comput. Civil Eng., 13, 36-42. https://doi.org/10.1061/(ASCE)0887-3801(1999)13:1(36).
  44. Yeh, I.C. (2007), "Modeling slump flow of concrete using second-order regressions and artificial neural networks", Cement Concrete Compos., 29(6), 474-80. https://doi.org/10.1016/j.cemconcomp.2007.02.001.