Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Predicting the high temperature effect on mortar compressive strength by neural network

  • Yuzer, N. (Yildiz Technical University, Department of Civil Engineering) ;
  • Akbas, B. (Gebze Institute of Technology) ;
  • Kizilkanat, A.B. (Yildiz Technical University, Department of Civil Engineering)
  • Received : 2010.02.24
  • Accepted : 2011.01.20
  • Published : 2011.10.25
⟨ Previous Next ⟩

Abstract

Before deciding if structures exposed to high temperature are to be repaired or demolished, their final state should be carefully examined. Destructive and non-destructive testing methods are generally applied for this purpose. Compressive strength and color change in mortars are observed as a result of the effects of high temperature. In this study, ordinary and pozzolan-added mortar samples were produced using different aggregates, and exposed to 100, 200, 300, 600, 900 and $1200^{\circ}C$. The samples were divided into two groups and cooled to room temperature in water and air separately. Compression tests were carried out on these samples, and the color change was evaluated by the Munsell Color System. The relationships between the change in compressive strength and color of mortars were determined by using a multi-layered feed-forward Neural Network model trained with the back-propagation algorithm. The results showed that providing accurate estimates of compressive strength by using the color components and ultrasonic pulse velocity design parameters were possible using the approach adopted in this study.

Keywords

References

  1. Akman, M.S. and Guner, A. (1984), "The applicability of sonreb method on damaged concrete", Materiaux et Constructions, 17(99), 195-200. https://doi.org/10.1007/BF02475244
  2. Andrade, C., Alonso, C. and Khoury, G.A. (2003), "Relating microstructure to properties", International Centre for Mechanical Sciences, Course on Effect of Heat on Concrete, Udine/Italy.
  3. ASTM D 1535-08 (2008), Standard practice for specifying color by the Munsell system.
  4. Bilim, C., Atis, C.D., Yanyildizi, H. and Karahan, O. (2009), "Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial network", Adv. Eng. Softw., 40(5), 334-340. https://doi.org/10.1016/j.advengsoft.2008.05.005
  5. Chiang, C.H. and Yang, C.C. (2005), "Artificial neural networks in prediction of concrete strength reduction due to high temperature", ACI Mater. J., 102(2), 93-102.
  6. Dias, W.P.S., Khoury, G.A. and Sullivan, P.J.E. (1990), "Mechanical properties of hardened cement paste exposed to temperatures up to 700 C (1292 F)", ACI Mater. J., 87(2), 160-166.
  7. Flood, I. (1989), "A neural network approach to the sequencing of construction tasks", Proceedings of the Sixth International Symposium on Automation and Robotics in Construction, Austin, Texas, Construction Industry Institute, 204-211.
  8. Garret, J.H., Gunaratham, D.J. and Ivezic, N. (1997), Artificial neural networks for civil engineers: fundamentals and applications, Kartam N, Flood I, editors. ASCE, 1-17.
  9. Gunaydin, H.M. and Dogan, S.Z. (2004), "A neural network approach for early cost estimation of structural systems of buildings", Int. J. Project Manage., 22, 595-602. https://doi.org/10.1016/j.ijproman.2004.04.002
  10. Haykin, S. (1994), Neural networks: a comprehensive foundation, Macmillan College Publishing Company, Inc, NJ-USA.
  11. Hegazy, T. and Ayed, A. (1998), "Neural network model for parametric cost estimation of highway projects", J. Constr. Eng. Manage., 124(3), 210-218. https://doi.org/10.1061/(ASCE)0733-9364(1998)124:3(210)
  12. Luke, J.T. (1996), The Munsell Color System: A Language for color, Fairchild Publications.
  13. Luo, H.L. and Lin, D.F. (2007), "Study the surface color of sewage mortar at high temperature", Constr. Build. Mater., 21(1), 90-97. https://doi.org/10.1016/j.conbuildmat.2005.06.053
  14. Neuro Solutions (2003), Neurodimension Inc., Version 4.24.
  15. Neville, A.M. (2000), Properties of Concrete, Fourth Edition, Longman Scientific and Technical, 359-411.
  16. Parichatprecha, R. and Nimityongskul, P. (2009), "Analysis of durability of high performance concrete using artificial neural networks", Constr. Build. Mater., 23(2), 910-917. https://doi.org/10.1016/j.conbuildmat.2008.04.015
  17. Rafiq, M.Y., Bugmann, G. and Easterbrook, D.J. (2001), "Neural network design for engineering applications", Comp. Struct., 79(17), 1541-1552. https://doi.org/10.1016/S0045-7949(01)00039-6
  18. Saridemir, M. (2009), "Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial networks", Adv. Eng. Softw., 40(5), 350-355. https://doi.org/10.1016/j.advengsoft.2008.05.002
  19. Shackelford, J.F. (2005), Introduction to materials science for engineers, Sixth Edition, Pearson Education Inc.
  20. Short, N.R., Purkiss, J.A. and Guise, S.E. (2001), "Assessment of fire damaged concrete using colour image analysis", Constr. Build. Mater., 15(1), 9-15. https://doi.org/10.1016/S0950-0618(00)00065-9
  21. TS EN 196-1 (2002), Methods of testing cement-Part 1: Determination of strength.
  22. Yeung, W.T. and Smith, J.W. (2005), "Damage detection in bridges using neural networks for pattern recognition of vibration signatures", Eng. Struct., 27(5), 685-698. https://doi.org/10.1016/j.engstruct.2004.12.006
  23. Yuzer, N., Akbas, B. and Kizilkanat, A.B. (2007), "Predicting the compressive strength of concrete exposed to high temperatures with a neural network model", TCMB 3rd International Symposium Sustainability in Cement and Concrete, Istanbul, Turkey, 455-464.
  24. Yuzer, N. and Kizilkanat, A.B. (2008), "Compressive strength-color change relationship in mortars subjected to high temperatures", Teknik Dergi, 19(2), 4381-4392.
  25. Wang, H.Y. (2008), "Effect of elevated temperatures on properties and color intensities of fly ash mortar", Comput. Concrete, 5(2), 89-100. https://doi.org/10.12989/cac.2008.5.2.089
  26. Zhao, Z. (2006), "Steel columns under fire-A neural network based strength model", Adv. Eng. Softw., 37(2), 97-105. https://doi.org/10.1016/j.advengsoft.2005.04.003

Cited by

  1. Prediction of lightweight concrete strength by categorized regression, MLR and ANN vol.12, pp.2, 2013, https://doi.org/10.12989/cac.2013.12.2.151
  2. An investigation on the mortars containing blended cement subjected to elevated temperatures using Artificial Neural Network (ANN) models vol.10, pp.6, 2012, https://doi.org/10.12989/cac.2012.10.6.649