Journal of the Korea Concrete Institute (콘크리트학회논문집)

Korea Concrete Institute (한국콘크리트학회)
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Concrete Mixture Design for RC Structures under Carbonation - Application of Genetic Algorithm Technique to Mixture Conditions

탄산화에 노출된 콘크리트 구조물의 배합설계에 대한 연구 - 유전자 알고리즘 적용성 평가

  • Lee, Sung-Chil (Dept. of Civil and Environmental Engineering, University of California Irvine) ;
  • Maria, Q. Feng (Dept. of Civil and Environmental Engineering, University of California Irvine) ;
  • Kwon, Sung-Jun (Dept. of Civil and Environmental Engineering, University of California Irvine)
  • 이성칠 (캘리포니아 주립대학교 어바인대 토목환경공학과) ;
  • ;
  • 권성준 (캘리포니아 주립대학교 어바인대 토목환경공학과)
  • Received : 2009.10.26
  • Accepted : 2010.04.16
  • Published : 2010.06.30
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Abstract

Steel corrosion in reinforced concrete (RC) structures is a critical problem to structural safety and many researches are being actively conducted on developing methods to maintain the required performance of the RC structures during their intended service lives. In this study, concrete mixture proportioning technique through genetic algorithm (GA) for RC structures under carbonation, which is considered to be serious in underground site and big cities, is investigated. For this, mixture proportions and diffusion coefficients of $CO_2$ from the previous researches were analyzed and fitness function for $CO_2$ diffusion coefficient was derived through regression analysis. This function based on the 12 experimental results consisted of 5 variables including water-cement ratio (W/C), cement content, sand percentage, coarse aggregate content per unit volume of concrete in unit, and relative humidity. Through genetic algorithm (GA) technique, simulated mixture proportions were proposed for 3 cases of verification and they showed reasonable results with less than relative error of 10%. Finally, assuming intended service life, different exposure conditions, design parameters, intended $CO_2$ diffusion coefficients, and cement contents were determined and related mixture proportions were simulated. This proposed technique is capable of suggesting reasonable mix proportions and can be modified based on experimental data which consider various mixing components like mineral admixtures.

콘크리트 내부의 철근부식은 구조물의 안전성에 큰 영향을 주므로, 목표 내구수명동안 구 조물의 성능을 확보하려는 연구가 활발하게 진행되고 있다. 이 연구는 대도시나 지하구조물에서 중요하게 평가되는 탄산화에 대하여, 유전자 알고리즘을 적용한 콘크리트 배합기법에 대한 연구이다. 이를 위해, 배합인자에 따른 이산화탄소 확산계수를 문헌조사를 통하여 분석하였으며, 습도를 고려한 최적 함수식을 회귀분석을 통하여 도출하였다. 최적 함수식은 12개의 실험자료에 대하여, 물-시멘트비, 단위 시멘트량, 잔골재율, 단위 굵은골재량, 그리고 상대습도를 포함하도록 고려하였으며, 유전자 알고리즘을 통하여, 주어진 이산화탄소 확산계수에 대한 콘크리트 배합을 도출하였다. 3개의 배합에 대하여 검증한 결과, 10% 미만의 상대오차를 보이며 주어진 배합을 잘 추정하였다. 최종적으로 서로 다른 환경과 설계 제원을 가지는 콘크리트 구조물을 가정하여, 목표 확산계수와 단위 시멘트량을 계산하였으며, 이를 이용하여 배합을 추정하였다. 제안된 기법은 주어진 확산계수와 배합을 잘 추정하였으며, 다양한 배합인자 및 혼화재료가 고려된 실험 자료를 이용한다면 더욱 합리적인 배합 기법으로 발전할 것이다.

Keywords

References

  1. Kobayashi, K. and Uno, Y., “Mechanism of Carbonation of Concrete,” Concrete library of JSCE, No. 16, 1990, pp. 139-151.
  2. 和泉意登志, 喜多達夫, 前田熙信, 中性化, 技報堂出版, 1986, pp. 62-75.
  3. Saeki, T., Ohga, H., and Nagataki, S, “Change in Micro-Structure of Concrete due to Carbonation,” Concrete Library of JSCE, No. 18, 1991, pp. 1-11.
  4. CEB, Durable Concrete Structures-Design Guide, 2nd Edition, Thomas Telford, London, 1992, pp. 28-35.
  5. Papadakis, V, G., Vayenas, C. G., and Fardis, M. N., “Fundamental Modeling and Experimental Investigation of Concrete Carbonation,” ACI Materials Journal, Vol. 88, No. 4, 1991, pp. 363-373.
  6. Papadakis, V, G., Vayenas, C. G., and Fardis, M. N., “Physical and Chemical Characteristics Affecting the Durability of Concrete,” ACI Materials Journal, Vol. 8, No. 2, 1991, pp. 186-196.
  7. Ishida, T. and Maekawa, K., “Modeling of PH Profile in Pore Water Based on Mass Transport and Chemical Equilibrium Theory,” Concrete Library of JSCE, No. 37, 2001, pp. 151-166.
  8. Ishida, T. and Maekawa, K., “Modeling of Durability Performance of Cementitious Materials and Structures Based on Thermo-hygro Physics,” Rilem Proceeding PRO 29, Life Prediction and Aging Management of Concrete Structures, 2003, pp. 39-49.
  9. Glasser, F. P., Marchand, J., and Samson, E., “Durability of Concrete-Degradation Phenomena Involving Detrimental Chemical Reactions,” Cement and Concrete Research, Vol. 38, Issue 2, 2008, pp. 226-246. https://doi.org/10.1016/j.cemconres.2007.09.015
  10. 阿部保彦, “ひび割れ幅がコンクリ一トの中性化深さに及 ぼす影響に關する文獻調査結果,” コンクリ一ト構造物の リハビリテ一シヨンに 關するシンポジウム論文集, Jan. 1999, pp. 7-14.
  11. 권성준, 박상순, 남상혁, 조호진, “국내 도심지 콘크리트 교각 취약부의 탄산화 조사에 대한 연구,” 한국구조물진단학회 논문집, 11권, 3호, 2007, pp. 116-122.
  12. Song, H. W., Kwon, S. J., Byun, K. J., and Park, C. K., “Predicting Carbonation in Early-Aged Cracked Concrete,” Cement and Concrete Research, Vol. 36, Issue 5, 2006, pp. 979-989. https://doi.org/10.1016/j.cemconres.2005.12.019
  13. Isgor, O. B. and Razaqpur, A. G., “Finite Element Modeling of Coupled Heat Transfer, Moisture Transfer and Carbonation Processes in Concrete Structures,” Cement and Concrete Composites, Vol. 26, Issue 1, 2004, pp. 57-73. https://doi.org/10.1016/S0958-9465(02)00125-7
  14. Houst, Y. F. and Wittmann, F. H., “Influence of Porosity and Water Content on the Diffusivity of $CO_2$ and $O_2$ through Hydrated Cement Paste,” Cement and Concrete Research, Vol. 24, Issue 6, 1994, pp. 1165-1176. https://doi.org/10.1016/0008-8846(94)90040-X
  15. 오병환, 정상화, 이명규, “콘크리트 중의 이산화탄소 확산계수에 대한 상대습도 영향 연구,” 콘크리트학회 논문집, 15권, 6호, 2003, pp. 778-784. https://doi.org/10.4334/JKCI.2003.15.6.778
  16. 권성준, 송하원, 변근주, “인공신경망을 통한 확산계수 도출과 공극구조 변화를 고려한 콘크리트 탄산화 해석,” 대한토목학회 논문집, 27권, 1A호, 2007, pp. 107-116.
  17. 김승억, 마상수, “유전자 알고리즘을 이용한 비선형 비탄성 최적설계,” 대한토목학회 논문집, 23권, 5A호, 2003, pp. 841-850.
  18. 윤영묵, 김병헌, “2차 비탄성 해석과 유전자 알고리즘을 이용한 평면 강골조 구조물의 최적설계,” 대한토목학회 논문집, 24권, 1A호, 2004, pp. 87-100.
  19. I-Cheng Y., “Computer-Aided Design for Optimum Concrete Mixtures,” Cement and Concrete Composites, Vol. 29, Issue 3, 2007, pp.193-202. https://doi.org/10.1016/j.cemconcomp.2006.11.001
  20. Lim, C. H., Yoon, Y. S., and Kim, J. H., “Genetic Algorithm in Mix Proportioning of High-Performance Concrete,” Cement and Concrete Research, Vol. 34, 2004, pp. 409-420. https://doi.org/10.1016/j.cemconres.2003.08.018
  21. 임철현, 윤영수, 이승훈, 손유신, “고성능 콘크리트 배합에서의 유전자 알고리즘의 적용,” 한국콘크리트학회 봄 학술대회 논문집, 14권, 1호, 2002, pp. 551-556.
  22. Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Welsley, Reading, MA, 1989, pp. 22-29.
  23. Cantu´-Paz, E. and Goldberg, D. E., “Efficient Parallel Genetic Algorithms: Theory and Practice,” Computer Methods in Applied Mechanics and Engineering, Vol. 186, 2000, pp. 221-238. https://doi.org/10.1016/S0045-7825(99)00385-0
  24. Mathworks, Genetic Algorithm and Direct Search Toolbox 2, User's Guide, 2007, pp. 108-132.
  25. CEB Task Group 5.1, 5.2, New Approach to Durability Design, CEB, Sprint-Druck, Stuttgart, 1997, pp. 53-62.
  26. Jung, S. H., “Diffusivity of Carbon Dioxide and Carbonation in Concrete through Development of Gas Diffusion Measuring System,” Ph. D. Dissertation, Dept. of Civil Engineering, Seoul National University, Korea, 2002, pp. 35-42.
  27. RILEM, “Durability Design of Concrete Structures,” Report of RILEM Technical Committee 130-CSL, E&FN, 1994, pp. 75-78.
  28. 한국콘크리트학회, 콘크리트 표준시방서-내구성편, 건설교통부, 2004, pp. 65-69.
  29. 지하철 건설본부, 지하철 구조물의 내구성 확보방안에 대한 연구, 대한토목학회, 1999, pp. 168-189.
  30. 日本土木學會, コンクリ-ト構造物の耐久性設計指針(案), 日本土木學會, コンクリ-トライブラリ-, 82 , Nov. 1995, pp. 2-18.