DOI QR코드

DOI QR Code

Estimation of Concrete Durability Subjected to Freeze-Thaw Based on Artificial Neural Network

인공신경망 기반 동결융해 작용을 받는 콘크리트의 내구성능 평가

  • 할리오나 (서울시립대학교 건축공학과 스마트시티융합전공) ;
  • 허인욱 (서울시립대학교 도시방재안전연구소) ;
  • 최승호 (서울시립대학교 방재공학과) ;
  • 김강수 (서울시립대학교 건축공학과 스마트시티융합전공)
  • Received : 2023.11.15
  • Accepted : 2023.12.05
  • Published : 2023.12.31

Abstract

In this study, a database was established by collecting experimental results on various concrete mixtures subjected to freeze-thaw cycles, based on which an artificial neural network-based prediction model was developed to estimate durability resistance of concrete. A regression analysis was also conducted to derive an equation for estimating relative dynamic modulus of elasticity subjected to freeze-thaw loads. The error rate and coefficient of determination of the proposed artificial neural network model were approximately 11% and 0.72, respectively, and the regression equation also provided very similar accuracy. Thus, it is considered that the proposed artificial neural network model and regression equation can be used for estimating relative dynamic modulus of elasticity for various concrete mixtures subjected to freeze-thaw loads.

이 연구에서는 동결융해 작용을 받는 다양한 콘크리트 배합에 대한 실험결과를 수집하여 데이터베이스를 구축하였다. 이를 바탕으로 동결융해 작용을 받는 콘크리트의 인공지능 기반 내구성능 평가모델을 개발하였으며, 회귀분석을 통해 상대동탄성계수 추정식을 도출하였다. 제안된 인공신경망 모델의 오류율과 결정계수는 각각 약 10.4%와 0.7이었으며, 회귀분석 추정식도 유사한 결과를 나타내었다. 따라서, 제안된 인공신경망 모델 및 회귀분석 추정식은 다양한 배합의 동결융해 작용을 받는 콘크리트에 대한 상대동탄성계수를 추정하는 데에 활용될 수 있을 것으로 판단된다.

Keywords

Acknowledgement

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. RS-2023-00220019).

References

  1. Neville, A. M. (1995), Properties of Concrete, Fourth and Final Edition, Logman.
  2. Lee, S. T., and Park, K. P. (2018), Resistance to Freezing and Thawing of Concrete Subjected to Carbonation, Journal of Korea Academia-Industrial cooperation Society, 19(2), 623-631.
  3. Choi, H. J., Kim, R. R., Lee, J. S., and Min, J. Y. (2021), Evaluation of Freeze-Thaw Damage on Concrete Using Nonlinear Ultrasound, ournal of the Korea Institute for Structural Maintenance and Inspection, 25(4), 56-64.
  4. Yoon, Y. G., Lee, I. B., Sa, M. H., and Oh, T. K. (2017), A Study on the Statistical Distribution of Ultrasonic Velocities for the Condition Evaluation of Concrete Wide Beam, Journal of Korean Society of Safety, 32(2), 98-104.
  5. You, Y. C., Choi, K. S., and Kim, K. H. (2010), Freezing-Thawing Resistance of Fiber Reinforced Polymers in Strengthening RC Members, Journal of the Korea Institute for Structural Maintenance and Inspection, 14(1), 182-189.
  6. Yan, W., Wu, Z., Niu, F., Wan, T., and Zheng, H. (2020), Study on the service life prediction of freeze- thaw damaged concrete with high permeability and inorganic crystal waterproof agent additions based on ultrasonic velocity, Constuction and Building Materials, 259, 1-11. https://doi.org/10.1016/j.conbuildmat.2020.120405
  7. Nimlyat, P. S., Audu, A. U., Ola-Adisa, E. O., and Gwatau, D. (2017), An evaluation of the fire safety measures in high-rise buildings in Nigeria, Sustainable Cities and Society, 35, 774-785. https://doi.org/10.1016/j.scs.2017.08.035
  8. Baek, E. S., Baek, G. J., Shin, H., Song, M. J., Kook, C. H., Kim, S. W. (2010), A study on the awareness of fire safety and evacuation guide system, Jornal of Korean Institute of Fire Science and Engineering, 24(6), 45-53.
  9. Stankovi, G, Petelin, S., Vidmar, P., and Perkovi. M., (2018), Impact of LNG Vapor Dispersion on Evacuation Routes inside LNG Terminals, Journal of Mechanical Engineering, 64(3), 176-184. https://doi.org/10.5545/sv-jme.2017.4956
  10. Popescu, I., Nikitopoulos, D.,Constantinou, P., and Nafornita, I. (2006), Comparison of ANN Based Models for Path Loss Prediction in Indoor Environment, IEEE Vehicular Technology Conference.
  11. Yu, H., Ma, H., and Yan, K., (2017), An equation for determining freeze-thaw fatigue damage in concrete and a model for predicting the service life, Construction and Building Materials, 137, 104-116. https://doi.org/10.1016/j.conbuildmat.2017.01.042
  12. Shang, H., Song, Y., and Ou, J., (2009), Behavior of Air-Entrained Concrete After Freeze-Thaw Cycles, Acta Mechanica Solida Sinica, 22(3), 261-266. https://doi.org/10.1016/S0894-9166(09)60273-1
  13. Shang, H. S., and Yi, T. H., (2013), Freeze-Thaw Durability of Air-Entrained concrete, The Scientific World Journal, 2013, 1-6. https://doi.org/10.1155/2013/650791
  14. Wu, H., Liu, Z., Sun, B., and Yin, J., (2016), Experimental investigation on freeze-thaw durability of Portland cement pervious concrete (PCPC), Construction and Building Materials, 117, 63-71. https://doi.org/10.1016/j.conbuildmat.2016.04.130
  15. Duan, A., Tian, Y., Dai, J. G., and Jin, W. L., (2013), A stochastic damage model for evaluating the internal deterioration of concrete due to freeze-thaw action, Materials and Structures, 47, 1025-1039. https://doi.org/10.1617/s11527-013-0111-8
  16. Cho, T., (2007), Prediction of cyclic freeze-thaw damage in concrete structures based on response surface method, Construction and Building Materials, 21(12), 2031-2040. https://doi.org/10.1016/j.conbuildmat.2007.04.018
  17. Janssen, D. J., and Snyder, M. B. (1994), Resistance of Concrete to Freezing and Thawing, Highway Research Program National Research Council, Washington DC.
  18. Russell, S., Norvig, P. (2010), Artificial Intelligence A Modern Approach.
  19. Cho, H. C., Lee, D. H., Ju, H. J., Kim, K. S., Kim, K. H., and Paulo, J. M. (2015), Monteiro. Remaining Service Life Estimation of Reinforced Concrete Buildings based on Fuzzy Approach, Computers and Concrete, 15(6), 879-902. https://doi.org/10.12989/CAC.2015.15.6.879
  20. Cho, H. C., Lee, D. H., Ju, H. J., Park, H. C., Kim, H. Y., and Kim, K. S. (2017), Fire Damage Assessment of Reinforced Concrete Structures Using Fuzzy Theory, Applied Sciences, 7(5), 1-16. https://doi.org/10.3390/app7050518
  21. Kang, H., Cho, H. C, Choi, S. H., Heo, I. W., Kim, H. Y., and Kim, K. S. (2019), Estimation of Heating Temperature for Fire-Damaged Concrete Structures Using Adaptive Neuro-Fuzzy Inference System, Materials, 12, 1-17. https://doi.org/10.3390/ma12233964
  22. Darkhanbat, K, Heo, I. W., Han, S. J., Cho, H. C., and Kim, K. S. (2021), Real-Time Egress Model for Multiplex Buildings under Fire Based on Artificial Neural Network, Applied Sciences, 11 1-22. https://doi.org/10.3390/app11146337
  23. Hagan, M. T., and Menhaj, M. (1994), Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks, 5(6), 989-993. https://doi.org/10.1109/72.329697
  24. Lee, J. S., and Suh, K. D. (2016), Calculation of Stability Number of Tetrapods Using Weight and Biases of ANN Model, Journal of Korean Society of Coastal and Ocean Engineers, 28(5), 277-283. https://doi.org/10.9765/KSCOE.2016.28.5.277
  25. Nielsen, R. H. (1989), Theory of the Backpropagation Neural Network, IEEE IJCNN, New York , 1593-1605.
  26. Trottier, C., Grazia, M. T., Macedo, H. F., Sanchez, L. F. M., Andrade, G. P. de., Souza. D. J. de., Naboka, O., Fathifazl, G., Nkinamubanzi, P. C., and Demers, A. (2022), Freezing and Thawing Resistance of Fine Recycled Concrete Aggregate (FRCA) Mixtures Designed with Distinct Techniques, Materials, 15(4), 1-23. https://doi.org/10.3390/ma15041342
  27. Choi, S. G., and Kim, S. B. (1997), A Study on Freezing and Trawing Resistance of Concrete with the Ratio of Ground Granulated Blast-Furnace Slag Replacement, Journal of Korea Concrete Institute, 9, 149-155.
  28. Shang, H. S., Yi, T. H., and Song,Y. P. (2012), Behavior of Plain Concrete of a High Water-Cement Ratio after Freeze-Thaw Cycles, Materials, 5(9), 1698-1707. https://doi.org/10.3390/ma5091698
  29. Zhao. N., and Zhang, A. (2013), Prediction of Concrete Freezing Resistance under Site Environment based on ANN, Applied Mechanics and Materials, 438-439, 202-206. https://doi.org/10.4028/www.scientific.net/AMM.438-439.202
  30. Im, S. Y., and Chun. B. S. (2013), The Prediction of Flow and Strength of Controlled Low-Strength Material Using Artificial Neural Networks, Research Institute of Korean Traditional Dance, 3, 164-183.
  31. Yang, S. I., Yoon, Y. S., Lee, S. H., and Kim, G. D., (2002), High Performance Concrete Mixture Design using Artificial Neural Networks, Korea Concrete Institute, (2002.05a), 545-550.