Computers and Concrete

  • 제5권6호
  • /
  • Pages.573-597
  • /
  • 2008
  • /
  • 1598-8198(pISSN)
  • /
  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Neuro-fuzzy based prediction of the durability of self-consolidating concrete to various sodium sulfate exposure regimes

  • Bassuoni, M.T. (School of Planning, Architecture and Civil Engineering, Centre for Built Environment Research, Queen's University of Belfast) ;
  • Nehdi, M.L. (Department of Civil and Environmental Engineering, The University of Western Ontario London)
  • 투고 : 2008.03.13
  • 심사 : 2008.10.23
  • 발행 : 2008.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

Among artificial intelligence-based computational techniques, adaptive neuro-fuzzy inference systems (ANFIS) are particularly suitable for modelling complex systems with known input-output data sets. Such systems can be efficient in modelling non-linear, complex and ambiguous behaviour of cement-based materials undergoing single, dual or multiple damage factors of different forms (chemical, physical and structural). Due to the well-known complexity of sulfate attack on cement-based materials, the current work investigates the use of ANFIS to model the behaviour of a wide range of self-consolidating concrete (SCC) mixture designs under various high-concentration sodium sulfate exposure regimes including full immersion, wetting-drying, partial immersion, freezing-thawing, and cyclic cold-hot conditions with or without sustained flexural loading. Three ANFIS models have been developed to predict the expansion, reduction in elastic dynamic modulus, and starting time of failure of the tested SCC specimens under the various high-concentration sodium sulfate exposure regimes. A fuzzy inference system was also developed to predict the level of aggression of environmental conditions associated with very severe sodium sulfate attack based on temperature, relative humidity and degree of wetting-drying. The results show that predictions of the ANFIS and fuzzy inference systems were rational and accurate, with errors not exceeding 5%. Sensitivity analyses showed that the trends of results given by the models had good agreement with actual experimental results and with thermal, mineralogical and micro-analytical studies.

키워드

참고문헌

  1. ACI Committee 237. (2007), Self-Consolidating Concrete (237R-07), American Concrete Institute, Farmington Hills, MI, USA, 30p.
  2. Al-Amoudi, O.S.B. (2002), "Attack on plain and blended cements exposed to aggressive sulfate environments", Cement Concrete Comp., 24(3-4), 305-316. https://doi.org/10.1016/S0958-9465(01)00082-8
  3. Bassuoni, M.T. (2008), Integrated Approach for Investigating the Durability of Self-consolidating Concrete to Sulfate Attack, PhD Dissertation, University of Western Ontario, Canada, 402p.
  4. Boel, V., Audenaert, K., De Schutter, G, Heirman, G., Vandewalle, L., Desmet, B., and Vantomme, J. (2007), "Transport properties of self compacting concrete with limestone filler and fly ash", Mater. Struct., 40(5), 507-516. https://doi.org/10.1617/s11527-006-9159-z
  5. Anoop, M.B., Rao, K.B., and Rao, T.V. (2002), "Application of fuzzy sets for estimating service life of reinforced concrete structural members in corrosive environments", Eng. Struct., 24(9), 1229-1242. https://doi.org/10.1016/S0141-0296(02)00060-3
  6. ASTM C 1012, (2004), "Standard test method for length change of hydraulic-cement mortar exposed to a sulfate solution", Annual Book of American Society for Testing Materials, V. 4.01, West Conshohocken, PA.
  7. ASTM C 215, (2004), "Standard test method for fundamental transverse, longitudinal, and torsional resonant frequencies of concrete specimens", Annual Book of American Society for Testing Materials, V. 4.02, West Conshohocken, PA.
  8. ASTM C 666, (2004), "Standard test method for resistance of concrete to rapid freezing and thawing", Annual Book of American Society for Testing Materials, V. 4.02, West Conshohocken, PA.
  9. Bentz, D., Ehlen, M., Ferraris, C., and Garboczi, E. (2001), "Sorptivity-based service life predictions for concrete pavements", Proceedings of 7th International Conference on Concrete Pavements, Orlando, Florida, USA, V. 1, pp. 181-193.
  10. Brown, M. and Harris, C. (1994), Neurofuzzy Adaptive Modelling and Control, Prentice Hall, NY, USA, 508p.
  11. Brown, P. and Taylor, H. (1999), "The role of ettringite in external sulfate attack", In: J. Marchand and J. Skalny (eds.) Materials Science of Concrete Special Volume: Sulfate Attack Mechanisms, The American Ceramic Society, pp. 73-98.
  12. Chiu, S. (1994), "Fuzzy model identification based on cluster estimation", J. Intelligent. Fuzzy Sys., 2(3), 267-278.
  13. Cohen, M. and Mather, B. (1991), "Sulfate attack on concrete-research needs", ACI Mater. J., 88(1), 62-69.
  14. De Schutter G, Audenaert K (eds), (2007), Durability of self-compacting concrete, State-of-the-Art Report of RILEM Technical Committee 205-DSC, RILEM Report 38, RILEM Publications S.A.R.L., 185p.
  15. Do, J. (2006), "Fuzzy inference based cover thickness estimation of reinforced concrete structure quantitatively considering salty environment impact", Comput. Concrete, 3(2/3), 145-161. https://doi.org/10.12989/cac.2006.3.2_3.145
  16. Do, J., Song, H., So, S., and Soh, Y. (2005), "Fuzzy methodology application for modeling uncertainties in chloride ingress models of rc building structure", Comput. Concrete, 2(4), 325-343. https://doi.org/10.12989/cac.2005.2.4.325
  17. El-Chabib, H. and Nehdi, M. (2005), "Neural network modelling of properties of cement-based materials demystified", Advances in Cement Res., 17(3), 91-102. https://doi.org/10.1680/adcr.2005.17.3.91
  18. Ferraris, C., Stutzman, P., and Snyder, K. (2006), "Sulfate resistance of concrete: a new approach", PCA R & D No. 2486, Portland Cement Association, Skokie, IL, 78p.
  19. Göktepe, A., nan, G., Ramyar, K., and Sezer, A. (2006), "Estimation of sulfate expansion of PC mortar using statistical and neural approaches", Constr. Build. Mater., 20(7), 441-449. https://doi.org/10.1016/j.conbuildmat.2005.01.041
  20. Haj-Ali, R., Kurtis, K., and Sthapit, A. (2001), "Neural network modeling of concrete expansion during longterm sulfate exposure", ACI Mater. J., 98(1), 36-43.
  21. Haykin, S. (1999), Neural Networks: A Comprehensive Foundation, Prentice Hall, NJ, USA, 842p.
  22. Hewlett, P.C., (1998), Lea's Chemistry of Cement and Concrete, Arnold, UK, 1053p.
  23. Inan, G., Göktepe, A., Ramyar, K., and Sezer, A. (2007), "Prediction of sulfate expansion of PC mortar using adaptive neuro-fuzzy methodology", Build. Environ., 42(3), 1264-1269. https://doi.org/10.1016/j.buildenv.2005.11.029
  24. Jang, J., Sun, C., and Mizutani, E. (1997), Neuro-Fuzzy and Soft Computing, Prentice Hall, N. J., USA, 614p.
  25. Kawamura, K. and Miyamoto, A. (2003), "Condition state evaluation of existing reinforced concrete bridges using neuro-fuzzy hybrid system", Comput. Struct., 81(18-19), 1931-1940. https://doi.org/10.1016/S0045-7949(03)00213-X
  26. Kim, Y., Kim, C., and Hong, S. (2006), "Fuzzy based state assessment for reinforced concrete building structures", Eng. Struct., 28(9), 1286-1297. https://doi.org/10.1016/j.engstruct.2005.12.011
  27. Marchand, J. (2001), "Modelling the behaviour of unsaturated cement systems exposed to aggressive chemical environments", Mater. Struct., 34(4), 195-200. https://doi.org/10.1007/BF02480588
  28. Mehta, P.K. (2000), "Sulfate attack on concrete: separating myths from reality", Concrete Int., 22(8), 57-61.
  29. Ming-yu, H. and Ming-shu, T. (2006), "Use of fuzzy neural network to evaluate effect of composition of fly ash in suppressing AAR", ACI Mater. J., 103(3), 161-168.
  30. Neville, A. (2004), "The confused world of sulfate attack on concrete", Cement Concrete Res., 34(8), 1275-1296. https://doi.org/10.1016/j.cemconres.2004.04.004
  31. Ross, T. (2004), Fuzzy logic with engineering applications, John Wiley & Sons, UK, 628p.
  32. Samson, E. and Marchand, J. (2007), "Modeling the transport of ions in unsaturated cement-based materials", Comput. Struct., 85(23-24), 1740-1756. https://doi.org/10.1016/j.compstruc.2007.04.008
  33. Skalny, J. and Pierce, J. (1999), "Sulfate attack: An Overview", In: J. Marchand and J. Skalny (eds.) Materials Science of Concrete Special Volume: Sulfate Attack Mechanisms, The American Ceramic Society, pp. 49-63.
  34. Skalny, J., Marchand, J., and Odler, I. (2002), Sulfate Attack on Concrete, Spon Press, UK, 217p.
  35. Zhu, W. and Bartos, J. (2003), "Permeation properties of self-compacting concrete", Cement Concrete Res., 33(6), 921-926. https://doi.org/10.1016/S0008-8846(02)01090-6

피인용 문헌

  1. Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai vol.7, pp.9, 2017, https://doi.org/10.3390/app7090923
  2. Modeling of damage in cement paste subject to external sulfate attack vol.16, pp.6, 2015, https://doi.org/10.12989/cac.2015.16.6.847
  3. Artificial Intelligence in Civil Engineering vol.2012, 2012, https://doi.org/10.1155/2012/145974
  4. Factorial Design Approach in Proportioning Prestressed Self-Compacting Concrete vol.8, pp.12, 2015, https://doi.org/10.3390/ma8031089
  5. Fuzzy inference systems based prediction of engineering properties of two-stage concrete vol.19, pp.2, 2008, https://doi.org/10.12989/cac.2017.19.2.133
  6. Neuro-fuzzy model of concrete exposed to various regimes combined with De-icing salts vol.21, pp.6, 2008, https://doi.org/10.12989/cac.2018.21.6.649