KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
권호 표지

Analysis of Chloride Penetration in Concrete with Mineral Admixtures using Neural Network Algorithm and Micro Modelling

신경망 이론과 마이크로 모델링을 통한 혼화재를 사용한 콘크리트의 염화물 침투해석

Kwon, Seung-Jun;Song, Ha-Won;Byun, Keun-Joo;Park, Chan-Kyu
권성준;송하원;변근주;박찬규

  • Published : 20070100

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Abstract

Chloride attack is one of the major deteriorations in concrete structures. In order to quantify chloride behavior in concrete, accurate prediction of diffusivity coefficient of chloride ion is necessary. In conventional prediction equations, the apparent diffusivity coefficient is expressed only with the water to cement ratio (W/C), i.e., the detailed mix proportions of concrete are not considered to obtain the diffusivity coefficient of concrete with mineral admixtures. In this study, a numerical technique for chloride penetration in the concrete with mineral admixtures using diffusivity coefficient from neural network algorithm is proposed. In order to obtain the comparable data set considering various mineral admixtures such as ground granulated blast-furnace slag (GGBFS), fly ash (FA) and slica fume (SF), a series of rapid chloride penetration tests are performed and a limited number of diffusivity coefficients are obtained. Total eight neurons (seven material components in trial mixture designs and curing period in submerged condition) are used for neural network. Through the neural network, 120 diffusivity coefficients from 30 mixture designs are obtained and the average of analyzed data is evaluated to be about 7%, which can be statistically acceptable. Finally, an evaluation technique for chloride penetration in the HPC is also developed by utilizing multi component hydration heat model (MCHHM) and micro pore structure formation model (MPSFM) along with obtained diffusivity coefficients from neural network. The applicability of the developed technique is also verified by comparing the simulation results with experimental results as well as other data.

Keywords

References

  1. 김인수, 이종헌, 양동석, 박선규(2002) 신경망을 이용한 콘크리트 배합요소 및 압축강도 추정, 한국콘크리트학회논문집, 한국콘크리트학회, Vol. 14, No.4, pp. 457-466
  2. 송하원, 권성준, 이석원, 변근주(2003) 고로슬래그 미분말 콘크리트의 염화물 침투 저항성에 관한 연구, 한국콘크리트학회논문집, 한국콘크리트학회, 제15권, 제3호, pp. 400-408
  3. 송하원, 권성준, 변근주, 박찬규(2005) 혼화재를 사용한 고성능 콘크리트의 배합특성을 고려한 염화물 확산 해석기법에 관한 연구. 대한토목학회논문집, 대한토목학회, 제25권 제1A호, pp.213-223
  4. 송하원, 박상순, 변근주(2001) 초기재령에서 균열을 갖는 콘크리트의 염화물 침투 해석, 대한토목학회논문집, 대한토목학회, 제21권 제6-A호, pp. 925-936
  5. 양승일, 윤영수, 이승훈, 김규동(2002) 신경망을 이용한 고성능 콘크리트의 배합설계, 한국콘크리트 학회 봄 학술발표회, pp. 545-550
  6. 오주원, 이종원, 이인원(1997) 콘크리트 배합설계에 있어서 신경망의 이용, 한국콘크리트학회지, 한국콘크리트학회, 제9권 2호, pp. 145-151
  7. 이승창, 임재홍(2002) 다중신경망을 이용한 콘크리트 강도 추정, 한국콘크리트 학회 가을 학술발표회, pp. 647-652
  8. 한국콘크리트학회(2004) 콘크리트 표준시방서-내구성편. 건설교통부
  9. 小山建, 花田潤治(1998) コンクイ-ト構造物の耐久設計指針(案)に基づいた耐久性確保の度合いについて, 土木學會論文報告集, No. 585/V-38, 199-203. pp. 199-203
  10. ASTM C 1202 (1997) Annual Book of ASTM Standards, v.04.02. Demuth, H. and Beagle, M. (2002), Neural Network Toolbox for Use with MATLAB, ver.4, The Math Works
  11. Jang, S.Y. (2003) Modeling of Chloride Transport and Carbonation in Concrete and Prediction of Service Life of Concrete Structures considering Corrosion of Steel Reinforcement, Ph. D. Dissertation, Dept. of Civil Engineering, Seoul National University, Korea
  12. Maekawa, K., Chaube, R., and Kishi, T. (1999) Modeling of Concrete Performance: Hydration, Microstructure Formation and Mass Transport, Routledge, London and New York
  13. Maekawa, K., Ishida, T., and Kishi, T. (2003) Multi-scale modeling of concrete performance, Journal of Advanced Concrete Technology, Vol. 1, No.2, pp. 91-126 https://doi.org/10.3151/jact.1.91
  14. McCulloch, W. and Pitt, W. (1943) A Logical Calaulus of the Ideas Immanent, Bulletin of Mathematical Biophysics 5, pp. 115-133 https://doi.org/10.1007/BF02478259
  15. Nagesh, M. and Bishwajit, B. (1998) Modelling of chloride diffusion in concrete and determination of diffusion coefficients, ACI Materials Journal, Vol. 95, No.2, pp. 113-120
  16. Park, K.B., Noguchi, T., and Plawsky, J. (2004) Modeling of hydration reactions using neural networks to predict the average properties of cement paste, Cement and Concrete Research
  17. Silica Fume Association (2001) Life-365 Service Life Prediction Model Manual, Jan. 2001
  18. Song, H.-W., Cho, H.J., Park, S.S., Byun, K.J., and Maekawa, K. (2001) Early-age cracking resistance evaluation of concrete structure, Concrete Science and Engineering, Vol. 3, pp. 62-72
  19. Song, H.-W., Kim, H.-I., Kwon, S.-J., Lee, C.-H., Byun, K. J., and Park, C.K. (2005) Prediction of service life in cracked reinforced concrete structures subjected to chloride attack and carbonation, 6th International Congress Global Construction: Ultimate Concrete Opportunities, Dundee, Scotland, 5-7 July, Vol Cement Combinations for Durable Concrete, pp. 767-776
  20. Stegemann, J.A. and Buenfeld, N.R. (2002) Prediction of unconfined compressive strength of cement paste with pure metal compound additions, Cement and Concrete Research 32, pp. 903-913 https://doi.org/10.1016/S0008-8846(02)00722-6
  21. Tang, L. (1996a) Chloride Transport in Concrete, Publication P96:6. Division of Building Materials, Chalmers University of Technology, Sweden
  22. Tang, L. (1996b) Electrically accelerated methods for determining chloride diffusivity in concrete-current development, Magazine of Concrete Research, Vol. 48, No. 176, pp. 173-179 https://doi.org/10.1680/macr.1996.48.176.173
  23. Tang, L. and Nilsson, L.O. (1993) Chloride binding capacity and binding isotherms of OPC paste and mortar, Cement and Concrete Research, 23(2), 347-353 https://doi.org/10.1016/0008-8846(93)90100-N
  24. Wang J.-Z., Ni H.-G., and He J.-Y. (1999) The application of automatic acquisition of knowledge to mix design of concrete, Cement and Concrete Research 29, pp. 1875-1880 https://doi.org/10.1016/S0008-8846(99)00152-0
  25. Yeh, I.-C. (1998) Modeling of strength of high-performance concrete using artificial neural networks, Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 https://doi.org/10.1016/S0008-8846(98)00165-3