Computers and Concrete

  • 제21권6호
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  • Pages.649-659
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  • 2018
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Neuro-fuzzy model of concrete exposed to various regimes combined with De-icing salts

  • Ghazy, Ahmed (Public Works Department, City of Winnipeg, Canada and Department of Civil Engineering, Alexandria University) ;
  • Bassuoni, Mohamed. T. (Department of Civil Engineering, University of Manitoba)
  • 투고 : 2017.10.05
  • 심사 : 2018.02.09
  • 발행 : 2018.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Adaptive neuro-fuzzy inference systems (ANFIS) can be efficient in modelling non-linear, complex and ambiguous behavior of cement-based materials undergoing combined damage factors of different forms (physical and chemical). The current work investigates the use of ANFIS to model the behavior (time of failure (TF)) of a wide range of concrete mixtures made with different types of cement (ordinary and portland limestone cement (PLC)) without or with supplementary cementitious materials (SCMs: fly ash and nanosilica) under various exposure regimes with the most widely used chloride-based de-icing salts (individual and combined). The results show that predictions of the ANFIS model were rational and accurate, with marginal errors not exceeding 3%. In addition, sensitivity analyses of physical penetrability (magnitude of intruding chloride) of concrete, amount of aluminate and interground limestone in cement and content of portlandite in the binder showed that the predictive trends of the model had good agreement with experimental results. Thus, this model may be reliably used to project the deterioration of customized concrete mixtures exposed to such aggressive conditions.

키워드

과제정보

연구 과제 주관 기관 : Natural Sciences and Engineering Research Council of Canada, University of Manitoba

참고문헌

  1. Ashok, M., Parande, A.K. and Jayabalan, P. (2017), "Strength and durability study on cement mortar containing nano materials", Adv. Nano Res., 5(2), 99-111. https://doi.org/10.12989/ANR.2017.5.2.099
  2. ASTM C 157 (2014), Standard Test Method for Length Change of Hardened Hydraulic-Cement Mortar and Concrete1, ASTM International, West Conshohocken, PA, V. 4.02.
  3. ASTM C 215 (2014), Standard Test Method for Fundamental Transverse, Longitudinal, and Torsional Resonant Frequencies of Concrete Specimens, ASTM International, West Conshohocken, PA , V. 4.02.
  4. ASTM C1202 (2012), Standard Test Method for Electrical Indication of Concrete's Ability to Resist Chloride Ion Penetration, ASTM International, West Conshohocken, PA, V. 4.02.
  5. ASTM C192 (2016), Standard Practice for Making and Curing Concrete Test Specimens in the Laboratory, ASTM International, West Conshohocken, PA , V. 4.02.
  6. ASTM C494 (2016), Standard Specification for Chemical Admixtures for Concrete, ASTM International, West Conshohocken, PA, V. 4.02.
  7. ASTM D4327 (2011), Standard Test Method for Anions in Water by Suppressed Ion Chromatography, ASTM International, West Conshohocken, PA , V. 11.01.
  8. Balasubramaniam, V., Raghunath, P.N. and Suguna, K. (2012), "An adaptive neuro-fuzzy inference system based modeling for corrosion-damaged reinforced HSC beams strengthened with external glass fibre reinforced polymer laminates", J. Comput. Sci., 8(6), 879-890. https://doi.org/10.3844/jcssp.2012.879.890
  9. Bassuoni, M.T. and Nehdi, M.L. (2006), "Enhancing the reliability of evaluating chloride ingress in concrete using the ASTM C1202 rapid chloride penetrability test", J. ASTM Int., 3(3), 1-13.
  10. Bassuoni, M.T. and Nehdi, M.L. (2008), "Neuro-fuzzy based prediction of the durability of self-consolidating concrete to various sodium sulfate exposure regimes", Comput. Concrete, 5(6), 573-597. https://doi.org/10.12989/cac.2008.5.6.573
  11. Bianchini, A. and Bandini, P. (2010), "Prediction of pavement performance through Neuro-Fuzzy reasoning", Comput. Aid. Civi. Infra. Eng., 25(1), 39-54. https://doi.org/10.1111/j.1467-8667.2009.00615.x
  12. Boga, A.R., O zturk, M. and Topcu, I.B. (2013), "Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI", Compos. B: Eng., 45(1), 688-696. https://doi.org/10.1016/j.compositesb.2012.05.054
  13. Brown, M. and Harris, C. (1994), Neuro-fuzzy Adaptive Modelling and Control, 1st Edition, Prentice Hall, New Jersey, USA.
  14. CAN/CSA-A3001 (2013), Cementitious Materials for Use in Concrete, Canadian Standards Association, CSA, Mississauga, Ontario, Canada.
  15. Cho, H.C., Ju, H., Oh, J.Y., Lee, K.J., Hahm, K.W. and Kim, K.S. (2016), "Estimation of concrete carbonation depth considering multiple influencing factors on the deterioration of durability for reinforced concrete structures", Adv. Mater. Sci. Eng., 2016, Article ID 4814609, 18.
  16. Cody, R.D., Cody, A.M., Spry, P.G. and Gan, G.L. (1996), "Experimental deterioration of highway concrete by chloride deicing salts", Environ. Eng. Geosci., 2(4), 575-588.
  17. Ghazy, A. and Bassuoni, M.T. (2017a), "Resistance of concrete to different exposures with chloride-based salts", Cement Concrete Res., 101, 144-158. https://doi.org/10.1016/j.cemconres.2017.09.001
  18. Ghazy, A. and Bassuoni, M.T. (2018a) "Response of concrete with blended binders and nanosilica to cyclic environments and chloride-based salts", Mag. Concrete Res., 1-14.
  19. Ghazy, A., Bassuoni, M.T. and Islam, A.K.M.R. (2018b), "Response of concrete with blended binders and nanosilica to freezing-thawing cycles and different concentrations of de-icing salts", J. Mater. Civil Eng., 1-44.
  20. Ghazy, A., Bassuoni, M.T. and Shalaby, A. (2016), "Nanomodified fly ash concrete: a repair option for concrete pavements", ACI Mater. J., 113(2), 231-242.
  21. Haykin, S. (1999), Neural Networks: A Comprehensive Foundation, 2nd Edition, Prentice-Hall, New Jersey, USA.
  22. Heisig, A., Urbonas, L., Beddoe, R.E. and Heinz, D. (2016), "Ingress of NaCl in concrete with alkali reactive aggregate: effect on silicon solubility", Mater. Struct., 49(10), 4291-4303. https://doi.org/10.1617/s11527-015-0788-y
  23. Hu, M.Y. and Tang, M.S. (2006), "Use of fuzzy neural network to evaluate effect of composition of fly ash in suppressing AAR", ACI Mater. J., 103(3), 161-168.
  24. Ipavec, A., Vuk, T., Gabrovsek, R. and Kaucic, V. (2013), "Chloride binding into hydrated blended cements: The influence of limestone and alkalinity", Cement Concrete Compos., 48, 74-85. https://doi.org/10.1016/j.cemconres.2013.02.010
  25. Johannesson, B.F. (2003), "A theoretical model describing diffusion of a mixture of different types of ions in pore solution of concrete coupled to moisture transport", Cement Concrete Res., 33, 481-488. https://doi.org/10.1016/S0008-8846(02)00993-6
  26. Kelting, D.L. and Laxon, C.L. (2010), "Review of effects and costs of road de-icing with recommendations for winter road management in the Adirondack Park", Adirondack Watershed Institute.
  27. Litvan, G.G. (1976), "Frost action in cement in the presence of deicers", Cement Concrete Res., 6(3), 351-356. https://doi.org/10.1016/0008-8846(76)90097-1
  28. Lothenbach, B., Le Saout, G., Gallucci, E. and Scrivener, K. (2008), "Influence of limestone on the hydration of Portland cements", Cement Concrete Res., 38(6), 848-860. https://doi.org/10.1016/j.cemconres.2008.01.002
  29. Madani, H., Bagheri, A. and Parhizkar, T. (2012), "The pozzolanic reactivity of monodispersed nanosilica hydrosols and their influence on the hydration characteristics of Portland cement", Cement Concrete Res., 42(12), 1563-1570. https://doi.org/10.1016/j.cemconres.2012.09.004
  30. Marchand, J. and Samson, E. (2009), "Predicting the service-life of concrete structures-Limitations of simplified models", Cement Concrete Compos., 31(8), 515-521. https://doi.org/10.1016/j.cemconcomp.2009.01.007
  31. MATLAB Documentation (2016), Fuzzy Toolbox User's Guide of MATLAB, The MathWorks, Inc.
  32. Ross, T. (2004), Fuzzy Logic with Engineering Applications, John Wiley & Sons, UK.
  33. Said, A.M., Zeidan, M.S., Bassuoni, M.T. and Tian, Y. (2012), "Properties of concrete incorporating nano-silica", Concrete Build. Mater., 36, 838-844. https://doi.org/10.1016/j.conbuildmat.2012.06.044
  34. Song, Z., Jiang, L. and Zhang, Z. (2016), "Chloride diffusion in concrete associated with single, dual and multi cation types", Comput. Concrete, 17(1), 53-66. https://doi.org/10.12989/cac.2016.17.1.053
  35. Tabatabaei, R., Sanjaria, H.R. and Shamsadini, M. (2014), "The use of artificial neural networks in predicting ASR of concrete containing nano-silica", Comput. Concrete, 13(6), 739-748. https://doi.org/10.12989/cac.2014.13.6.739
  36. Tamimi, A.K., Abdalla, J.A. and Sakka, Z.I. (2008), "Prediction of long term chloride diffusion of concrete in harsh environment", Constr. Build. Mater., 22, 829-836. https://doi.org/10.1016/j.conbuildmat.2007.01.001
  37. Wang, K., Nelsen, D.E. and Nixon, W.A. (2006), "Damaging effects of deicing chemicals on concrete materials", Cement Concrete Compos., 28(2), 173-188. https://doi.org/10.1016/j.cemconcomp.2005.07.006

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