Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

A neural-based predictive model of the compressive strength of waste LCD glass concrete

  • Kao, Chih-Han (Department of Civil Engineering and Engineering Management, National Quemoy University) ;
  • Wang, Chien-Chih (Department of Civil Engineering and Geomatics, Cheng Shiu University) ;
  • Wang, Her-Yung (Department of Civil Engineering, National Kaohsiung University of Applied Sciences)
  • Received : 2016.11.21
  • Accepted : 2017.01.16
  • Published : 2017.05.25
⟨ Previous Next ⟩

Abstract

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

Keywords

References

  1. Adaway, M. and Wang, Y. (2015), "Recycled glass as a partial replacement for fine aggregate in structural concrete-effects on compressive strength", J. Struct. Eng., 14(1), 116-122.
  2. Chopra, P., Sharma, R.K. and Kumar, M. (2015), "Artificial neural networks for the prediction of compressive strength of concrete", J. Appl. Sci. Eng., 13(3), 187-204. https://doi.org/10.5937/jaes13-7750
  3. Chun, Y.M., Claisse, P., Naik, T.R. and Ganjian, E.S. (2007), "Sustainable construction materials and technologies", Proceedings of the International Conference on Sustainable Construction Materials and Technology, Coventry, U.K.
  4. Glantz, S.A. and Slinker, B.K. (1990), Primer of Applied Regression and Analysis of Variance, McGraw-Hill, New York, U.S.A.
  5. Hanehara, S., Tomosawa, F., Kobayakawa, M. and Hwang, K. (2001), "Effect of water/powder ratio, mixing ratio of fly ash and curing temperature on pozzolanic reaction of fly ash in cement pastes", Cement Concrete Res., 31(1), 31-39. https://doi.org/10.1016/S0008-8846(00)00441-5
  6. Hussain, M.V. and Chandak, R. (2015), "Strength properties of concrete containing waste glass powder", J. Eng. Res. Appl., 5(4), 1-4.
  7. Hwang, K., Noguchi, T. and Tomosawa, F. (1999), "Numerical prediction model for compressive strength development of concrete containing fly ash", J. Struct. Constr. Eng., 519, 1-6.
  8. Ji, T., Lin, T. and Lin, X. (2006), "A concrete mix proportion design algorithm based on artificial neural networks", Cement Concrete Res., 36(7), 1399-1408. https://doi.org/10.1016/j.cemconres.2006.01.009
  9. Kou, S.C. and Poon, C.S. (2009), "Properties of self-compacting concrete prepared with coarse and fine aggregates", Cement Concrete Compos., 31(9), 622-627. https://doi.org/10.1016/j.cemconcomp.2009.06.005
  10. Kumar, S.C., Varanasi, V.K. and Saha, P. (2012), "Sustainable development using supplementary cementitious materials and recycled aggregate", J. Mod. Eng. Res., 2(1), 165-171.
  11. Liang, J.F., Yang, Z.P., Yi, P.H. and Wang, J.B. (2015), "Mechanical properties of recycled fine glass aggregate concrete under uniaxial loading", Comput. Concrete, 16(2), 275-285. https://doi.org/10.12989/cac.2015.16.2.275
  12. Lin, K.L., Huang, W.J., Shie, J.L., Lee, T.C., Wang, K.S. and Lee, C.H. (2009), "The utilization of thin film transistor liquid crystal display waste glass as a pozzolanic material", J. Hazard. Mater., 163(2), 916-921. https://doi.org/10.1016/j.jhazmat.2008.07.044
  13. Liu, M.J. (2015), Flat Panel Display Industry Yearbook, Industrial Technology Research Institute, Taipei, Taiwan.
  14. May, R., Dandy, G. and Maier, H. (2011), "Review of input variable selection methods for artificial neural networks", Artif. Neur. Net.-Methodol. Adv. Bio. Appl.
  15. Park, S.B., Lee, B.C. and Kim, J.H. (2004), "Studies on mechanical properties of concrete containing waste glass aggregate", Cement Concrete Res., 34(12), 2181-2189. https://doi.org/10.1016/j.cemconres.2004.02.006
  16. Peng, C.H., Yeh, I.C. and Lien, L.C. (2010), "Building strength models for high-performance concrete at different ages using genetic operation trees, nonlinear regression, and neural networks", Eng. Comput., 26(1), 61-73. https://doi.org/10.1007/s00366-009-0142-5
  17. Sajedi, F., Razak, H.A., Mahmudb, H.B. and Shafigh, P. (2012), "Relationships between compressive strength cement-slag concrete under air and water curing regimes", Res. Civil Environ. Eng., 1(4), 202-225.
  18. Sarath, P., Bonda, S., Mohanty, S. and Nayak, S.K. (2015), "Mobile phone waste management and recycling: views and trends", Waste Manage., 46, 536-545. https://doi.org/10.1016/j.wasman.2015.09.013
  19. Scott, D.W. (1992), Multivariate Density Estimation: Theory, Practice and Visualization, John Wiley and Sons, New York, U.S.A.
  20. Scrivener, K.L. and Kirkpatrick, R.J. (2008), "Innovation in use and research on cementitious material", Cement Concrete Res., 38(2), 128-136. https://doi.org/10.1016/j.cemconres.2007.09.025
  21. Shanker, R. and Sachan, A.K. (2014), "Concrete mix design using neural network", J. Civil Environ. Struct. Constr. Arch. Eng., 8(8), 910-913.
  22. Topcu, İ.B. and Canbaz, M. (2004), "Properties of concrete containing waste glass", Cement Concrete Res., 34(2), 267-274. https://doi.org/10.1016/j.cemconres.2003.07.003
  23. Utans, J., Moody, J., Rehfuss, S. and Siegelmann, H. (1995), "Input variable selection for neural networks: Application to predicting the U.S. business cycle", Comput. Intell. Fin. Eng. Pro. IEEE/IAFE, 9-11.
  24. Wang, C.C., Chen, T.T., Wang, H.Y. and Huang, C. (2014), "A predictive model for compressive strength of waste LCD glass concrete by nonlinear-multivariate regression", Comput. Concrete, 13(4), 531-545. https://doi.org/10.12989/cac.2014.13.4.531
  25. Wang, H.Y. (2009), "A study of the engineering properties of waste LCD glass applied to controlled low strength materials concrete", Constr. Build. Mater., 23(6), 2127-2131. https://doi.org/10.1016/j.conbuildmat.2008.12.012
  26. Wang, H.Y. and Chen, J.S. (2008), "Study of thin film transition liquid crystal display (TFT-LCD) optical waste glass applied in early-high-strength controlled low strength materials", Comput. Concrete, 5(5), 491-501. https://doi.org/10.12989/cac.2008.5.5.491
  27. Wang, H.Y. and Huang, W.L. (2010), "A study on the properties of flesh self-consolidating glass concrete (SCGC)", Constr. Build. Mater., 24(4), 619-624. https://doi.org/10.1016/j.conbuildmat.2009.08.047
  28. Zain, M.F.M., Abd, S.M., Sopian, K., Jamil, M. and Che-Ani, A.I. (2008), "Mathematical regression model for the prediction of concrete strength, mathematical methods", Proceedings of the 10th WSEAS International Conference on Mathematical Methods, Computers in Technology and Intelligent Systems, 396-402.

Cited by

  1. Tunnelling on terrace soil deposits: Characterization and experiences on the Bogota-Villavicencio road vol.15, pp.3, 2017, https://doi.org/10.12989/gae.2018.15.3.899
  2. Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars vol.24, pp.4, 2017, https://doi.org/10.12989/cac.2019.24.4.329