Computers and Concrete

  • 제28권1호
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  • Pages.77-91
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  • 2021
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Investigation of the effects of corrosion on bond strength of steel in concrete using neural network

  • Concha, Nolan C. (Department of Civil Engineering, FEU-Institute of Technology) ;
  • Oreta, Andres Winston C. (Department of Civil Engineering, De La Salle University)
  • 투고 : 2019.04.01
  • 심사 : 2021.05.31
  • 발행 : 2021.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

Corrosion of steel reinforcement due to hostile environments is regarded as one vital structural health concerns in concrete structures. Specifically, the development of corrosion affects the necessary bond strength of rebar in concrete contributing to the loss of resilience and possible structural failures. It is thus essential to understand the effects of corrosion on bond strength so that remedial measures can be done on existing and deteriorating RC structures. Hence, this study investigated through laboratory experiments and Artificial Neural Network (ANN) modeling the effects of corrosion on bond strength. Experimental results showed that at small amounts of corrosion less than 0.27%, the bond strength was observed to increase. At these levels, the amounts of corrosion products were sufficient enough to expand freely through the permeable structure of concrete and occupy the pore spaces. Beyond this level, however, the bond strength of concrete deteriorated significantly. There was an observed average decrease of 1.391 MPa in the bond strength values for every percent increase in the amount of corrosion. The expansive and progressive internal radial stress due to corrosion resulted to the development of internal and surface cracks in concrete. In the parametric investigation of the derived ANN model, the bond strength was also observed to decline continuously with the growth of corrosion derivatives as represented by the relative magnitudes of the ultrasonic pulse velocity (UPV). The prediction results of the model can be utilized as basis for design and select appropriate mitigating measures to prolong the service life of concrete structures.

키워드

참고문헌

  1. Afshar, A., Jahandari, S., Rasekh, H., Shariati, M., Afshar, A. and Shokrgozar, A. (2020), "Corrosion resistance evaluation of rebars with various primers and coatings in concrete modified with different additives", Constr. Build. Mater., 262, 120034. https://doi.org/10.1016/j.conbuildmat.2020.120034.
  2. Ahmad, S., Pilakoutas, K., Rafi, M.M. and Zaman, Q. U. (2018), "Bond strength prediction of steel bars in low strength concrete by using ANN", Comput. Concrete, 22(2), 249-259. https://doi.org/10.12989/cac.2018.22.2.249.
  3. Asen, F. and Dehestani, M. (2021), "Influence of concrete mix proportions on lifetime flexural load-bearing capacity of RC beams under chloride corrosion of rebars", Struct., 29, 2017-2027. https://doi.org/10.1016/j.istruc.2021.01.009.
  4. Ashour, A.F. and Alqedra, M.A. (2005), "Concrete breakout strength of single anchors in tension using neural networks", Adv. Eng. Softw., 36(2), 87-97. https://doi.org/10.1016/j.advengsoft.2004.08.001.
  5. Bal, L. and Buyle-Bodin, F. (2013), "Artificial neural network for predicting drying shrinkage of concrete", Constr. Build. Mater., 38, 248-254. https://doi.org/10.1016/j.conbuildmat.2012.08.043.
  6. Bhargava, K., Ghosh, A.K., Mori, Y. and Ramanujam, S. (2005), "Modeling of time to corrosion-induced cover cracking in reinforced concrete structures", Cement Concrete Res., 35(11), 2203-2218. https://doi.org/10.1016/j.cemconres.2005.06.007.
  7. Bhargava, K., Ghosh, A.K., Mori, Y. and Ramanujam, S. (2008), "Suggested empirical models for corrosion-induced bond degradation in reinforced concrete", J. Struct. Eng., 134(2), 221-230. https://doi.org/10.1061/(ASCE)0733-9445(2008)134:2(221).
  8. Cabrera, J.G. (1996), "Deterioration of concrete due to reinforcement steel corrosion", Cement Concrete Compos., 18(1), 47-59. https://doi.org/10.1016/0958-9465(95)00043-7.
  9. Carpenter, W.C. and Hoffman, M.E. (1995), "Training backprop neural networks", AI Exp., 10(3), 30-33.
  10. Choudhary, G.K. and Dey, S. (2012), "Crack detection in concrete surfaces using image processing, fuzzy logic, and neural networks", Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference, October.
  11. Chung, L., Kim, J.H.J. and Yi, S.T. (2008), "Bond strength prediction for reinforced concrete members with highly corroded reinforcing bars", Cement Concrete Compos., 30(7), 603-611. https://doi.org/10.1016/j.cemconcomp.2008.03.006.
  12. Concha, N. and Oreta, A.W. (2018), "A model for time-to-cracking of concrete due to chloride induced corrosion using artificial neural network", IOP Conf. Ser.: Mater. Sci. Eng., 431(7), 072009. https://doi.org/10.1088/1757-899X/431/7/072009
  13. Concha, N.C. and Dadios, E.P. (2015), "Optimization of the rheological properties of self compacting concrete using neural network and genetic algorithm", 2015 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), December.
  14. Concha, N.C. and Oreta, A.W.C. (2019), "Bond strength prediction model of corroded reinforcement in concrete using neural network", Int. J. Geom., 16(54), 55-61. https://doi.org/10.21660/2019.54.4785.
  15. Dahou, Z., Sbartai, Z.M., Castel, A. and Ghomari, F. (2009), "Artificial neural network model for steel-concrete bond prediction", Eng. Struct., 31(8), 1724-1733. https://doi.org/10.1016/j.engstruct.2009.02.010.
  16. Desnerck, P., Lees, J.M. and Morley, C.T. (2015), "Bond behaviour of reinforcing bars in cracked concrete", Constr. Build. Mater., 94, 126-136. https://doi.org/10.1016/j.conbuildmat.2015.06.043.
  17. El Maaddawy, T. and Soudki, K. (2007), "A model for prediction of time from corrosion initiation to corrosion cracking", Cement Concrete Compos., 29(3), 168-175. https://doi.org/10.1016/j.cemconcomp.2006.11.004.
  18. Garson, G.D. (1991), "Interpreting neural-network connection weights", AI Exp., 6(4), 46-51.
  19. Goffin, B., Banthia, N. and Yonemitsu, N. (2020), "Use of infrared thermal imaging to detect corrosion of epoxy coated and uncoated rebar in concrete", Constr. Build. Mater., 263, 120162. https://doi.org/10.1016/j.conbuildmat.2020.120162.
  20. Haque, M.E. and Sudhakar, K.V. (2001), "Prediction of corrosion-fatigue behavior of DP steel through artificial neural network", Int. J. Fatig., 23(1), 1-4. https://doi.org/10.1016/S0142-1123(00)00074-8.
  21. Hou, L., Liu, H., Xu, S., Zhuang, N. and Chen, D. (2017), "Effect of corrosion on bond behaviors of rebar embedded in ultra-high toughness cementitious composite", Constr. Build. Mater., 138, 141-150. https://doi.org/10.1016/j.conbuildmat.2017.02.008.
  22. Ince, R. (2004), "Prediction of fracture parameters of concrete by artificial neural networks", Eng. Fract. Mech., 71(15), 2143-2159. https://doi.org/10.1016/j.engfracmech.2003.12.004.
  23. Lee, H.S., Noguchi, T. and Tomosawa, F. (2002), "Evaluation of the bond properties between concrete and reinforcement as a function of the degree of reinforcement corrosion", Cement Concrete Res., 32(8), 1313-1318. https://doi.org/10.1016/S0008-8846(02)00783-4.
  24. Maurel, O., Dekoster, M. and Buyle-Bodin, F. (2005), "Relation between total degradation of steel concrete bond and degree of corrosion of RC beams experimental and computational studies", Comput. Concrete, 2(1), 1-18. https://doi.org/10.12989/cac.2005.2.1.001.
  25. Ongpeng, J., Soberano, M., Oreta, A. and Hirose, S. (2017), "Artificial neural network model using ultrasonic test results to predict compressive stress in concrete", Comput. Concrete, 19(1), 59-68. https://doi.org/10.12989/cac.2017.19.1.059
  26. Oreta, A.W. and Ongpeng, J. (2011), "Modeling the confined compressive strength of hybrid circular concrete columns using neural networks", Comput. Concrete, 8(5), 597-616. https://doi.org/10.12989/cac.2011.8.5.597.
  27. Park, K.B., Noguchi, T. and Plawsky, J. (2005), "Modeling of hydration reactions using neural networks to predict the average properties of cement paste", Cement Concrete Res., 35(9), 1676-1684. https://doi.org/10.1016/j.cemconres.2004.08.004.
  28. Qiao, D., Nakamura, H., Yamamoto, Y. and Miura, T. (2016), "Crack patterns of concrete with a single rebar subjected to nonuniform and localized corrosion", Constr. Build. Mater., 116, 366-377. https://doi.org/10.1016/j.conbuildmat.2016.04.149.
  29. Rao, G.A., Pandurangan, K., Sultana, F. and Eligehausen, R. (2004), "Studies on the pull-out strength of ribbed bars in high-strength concrete", Proceeding of the FraMCos-6 conference. International Association of Fracture Mechanics for Concrete and Concrete Structures, 5, 05-17.
  30. Rinchon, J.P.M., Concha, N.C. and Calilung, M.G.V. (2017), "Reinforced concrete ultimate bond strength model using hybrid neural network-genetic algorithm", 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), December.
  31. Saleem, M. (2017), "Study to detect bond degradation in reinforced concrete beams using ultrasonic pulse velocity test method", Struct. Eng. Mech., 64(4), 427-436. http://doi.org/10.12989/sem.2017.64.4.427.
  32. Tondolo, F. (2015), "Bond behaviour with reinforcement corrosion", Constr. Build. Mater., 93, 926-932. https://doi.org/10.1016/j.conbuildmat.2015.05.067.
  33. Wu, F., Gong, J.H. and Zhang, Z. (2014), "Calculation of corrosion rate for reinforced concrete beams based on corrosive crack width", J. Zhejiang Univ. Sci. A, 15(3), 197-207. https://doi.org/10.1631/jzus.A1300280
  34. Yalciner, H., Eren, O. and Sensoy, S. (2012), "An experimental study on the bond strength between reinforcement bars and concrete as a function of concrete cover, strength and corrosion level", Cement Concrete Res., 42(5), 643-655. https://doi.org/10.1016/j.cemconres.2012.01.003.
  35. Zhang, X., Liang, X., Huang, H. and Zhou, H. (2016), "An experimental study on effect of steel corrosion on the bond-slip performance of reinforced concrete", International Conference on Durability of Concrete Structures.
  36. Zhao, Y., Lin, H., Wu, K. and Jin, W. (2013), "Bond behaviour of normal/recycled concrete and corroded steel bars", Constr. Build. Mater., 48, 348-359. https://doi.org/10.1016/j.conbuildmat.2013.06.091.