Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Predicting bond strength of corroded reinforcement by deep learning

  • Tanyildizi, Harun (Department of Civil Engineering, Faculty of Technology, Firat University)
  • Received : 2021.10.09
  • Accepted : 2022.02.11
  • Published : 2022.03.25
⟨ Previous Next ⟩

Abstract

In this study, the extreme learning machine and deep learning models were devised to estimate the bond strength of corroded reinforcement in concrete. The six inputs and one output were used in this study. The compressive strength, concrete cover, bond length, steel type, diameter of steel bar, and corrosion level were selected as the input variables. The results of bond strength were used as the output variable. Moreover, the Analysis of variance (Anova) was used to find the effect of input variables on the bond strength of corroded reinforcement in concrete. The prediction results were compared to the experimental results and each other. The extreme learning machine and the deep learning models estimated the bond strength by 99.81% and 99.99% accuracy, respectively. This study found that the deep learning model can be estimated the bond strength of corroded reinforcement with higher accuracy than the extreme learning machine model. The Anova results found that the corrosion level was found to be the input variable that most affects the bond strength of corroded reinforcement in concrete.

Keywords

References

  1. Afzali-Naniz, O. and Mazloom, M. (2019), "Assessment of the influence of micro- and nano-silica on the behavior of selfcompacting lightweight concrete using full factorial design", Asian J. Civil Eng., 20(1), 57-70. https://doi.org/10.1007/s42107-018-0088-2.
  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. Al-Shamiri, A.K., Kim, J.H., Yuan, T.F. and Yoon, Y.S. (2019), "Modeling the compressive strength of high-strength concrete: An extreme learning approach", Constr. Build. Mater., 208, 204-219. https://doi.org/10.1016/j.conbuildmat.2019.02.165.
  4. Al-Shamiri, A.K., Yuan, T.F. and Kim, J.H. (2020), "Non-tuned machine learning approach for predicting the compressive strength of high-performance concrete", Mater., 13(5), https://doi.org/10.3390/ma13051023.
  5. Alabduljabbar, H., Haido, J.H., Alyousef, R., Yousif, S.T., McConnell, J., Wakil, K. and Jermsittiparsert, K. (2020), "Prediction of the flexural behavior of corroded concrete beams using combined method", Struct., 25, 1000-1008. https://doi.org/10.1016/j.istruc.2020.03.057.
  6. Alcin, O .F., Sengur, A. and Ince, M.C. (2015), "Forward-backward pursuit based sparse extreme learning machine", J. Faculty Eng. Arch. Gazi Univ., 30(1), 111-117.
  7. Alcin, O.F., Sengur, A., Ghofrani, S. and Ince, M.C. (2014), "GA-SELM: Greedy algorithms for sparse extreme learning machine", Meas. J. Int. Meas. Confed., 55, 126-132. https://doi.org/10.1016/j.measurement.2014.04.012.
  8. Alhawat, M. and Ashour, A. (2019), "Bond strength between corroded steel reinforcement and recycled aggregate concrete", Struct., 19, 369-385. https://doi.org/10.1016/j.istruc.2019.02.001.
  9. Almusallam, A.A. (2001), "Effect of degree of corrosion on the properties of reinforcing steel bars", Constr. Build. Mater., 15(8), 361-368. https://doi.org/10.1016/S0950-0618(01)00009-5.
  10. Almusallam, A.A., Al-Gahtani, A.S. and Aziz, A.R. (1996), "Effect of reinforcement corrosion on bond strength", Constr. Build. Mater., 10(2), 123-129. https://doi.org/10.1016/0950-0618(95)00077-1.
  11. Asif, S.A., Ahmed, N., Hayat, A., Hussan, S., Shabbir, F. and Mehmood, K. (2018), "Study of adhesion characteristics of different bitumen-aggregate combinations using bitumen bond strength test", J. Chinese Inst. Eng. Transac. Chinese Inst. Eng. Ser. A, 41(5), 430-440. https://doi.org/10.1080/02533839.2018.1490205.
  12. Atis, C.D., Tanyildizi, H. and Karahan, O. (2009), "Statistical analysis for strength properties of polypropylene-fibre-reinforced fly ash concrete", Mag. Concrete Res., 61(7), 557-566. https://doi.org/10.1680/macr.2007.00033.
  13. Auyeung, Y., Balaguru, P. and Chung, L. (2000), "Bond behavior of corroded reinforcement bars", ACI Struct. J., 97(2), 214-220. https://doi.org/10.14359/826.
  14. Bertolini, L., Elsener, B., Redaelli, E. and Polder, R. (2013), Corrosion of Steel in Concrete: Prevention, Diagnosis, Repair: Second Edition, Wiley-VCH. https://doi.org/10.1002/9783527651696.
  15. Chung, L., Jay Kim, J.H. 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.
  16. Concha, N.C. (2019), "Bond strength prediction model of corroded reinforcement in concrete using neural network", Int. J. Geomate, 16(54), https://doi.org/10.21660/2019.54.4785.
  17. Concha, N.C. and Oreta, A.W.C. (2019), "Investigation of the effects of corrosion on bond strength of steel in concrete using neural network", Advances in Structural Engineering and Mechanics, Jeju, September.
  18. Dauji, S. and Bhargava, K. (2018), "Neural estimation of bond strength degradation in concrete affected by reinforcement corrosion", INAE Lett., 3(4), 203-215. https://doi.org/10.1007/s41403-018-0050-3.
  19. Deng, F., He, Y., Zhou, S., Yu, Y., Cheng, H. and Wu, X. (2018), "Compressive strength prediction of recycled concrete based on deep learning", Constr. Build. Mater., 175, 562-569. https://doi.org/10.1016/j.conbuildmat.2018.04.169.
  20. Dutta, S., Ramachandra Murthy, A., Kim, D. and Samui, P. (2017), "Prediction of compressive strength of self-compacting concrete using intelligent computational modeling GPR and RVM-based predictions of surface and hole quality in drilling of AISI D2 cold work tool steel view project call for chapter : Risk, reliability", CMC-Comput. Mater. Continua, 53(2), 167-185.
  21. Fan, Y. and Hu, Z. (2007), "Application of artificial neural network in prediction of bond property between corroded reinforcement and concrete", Proceedings-Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, 4, 664-668. https://doi.org/10.1109/FSKD.2007.177.
  22. Fan, Y. and Hu, Z. (2007), "Application of artificial neural network in prediction of bond property between corroded reinforcement and concrete", Fourth International Conference on Fuzzy Systems and Knowledge Discovery, August.
  23. Fang, C., Lundgren, K., Chen, L. and Zhu, C. (2004), "Corrosion influence on bond in reinforced concrete", Cement Concrete Res., 34(11), 2159-2167. https://doi.org/10.1016/j.cemconres.2004.04.006.
  24. Gers, F.A., Schmidhuber, J. and Cummins, F. (1999), "Learning to forget: Continual prediction with LSTM", IEE Conference Publication, 2, 850-855. https://doi.org/10.1049/cp:19991218.
  25. Gers, F.A., Schraudolph, N.N. and Schmidhuber, J. (2003), "Learning precise timing with LSTM recurrent networks", J. Machine Learn. Res., 3(1), 115-143. https://doi.org/10.1162/153244303768966139.
  26. Guneyisi, E.M., Mermerdas, K. and Gultekin, A. (2016), "Evaluation and modeling of ultimate bond strength of corroded reinforcement in reinforced concrete elements", Mater. Struct. Mater. Constr., 49(8), 3195-3215. https://doi.org/10.1617/s11527-015-0713-4.
  27. Hoang, N.D., Tran, X.L. and Nguyen, H. (2020), "Predicting ultimate bond strength of corroded reinforcement and surrounding concrete using a metaheuristic optimized least squares support vector regression model", Neural Comput. Appl., 32(11), 7289-7309. https://doi.org/10.1007/s00521-019-04258-x.
  28. Hochreiter, S. and Schmidhuber, J. (1997), "Long short-term memory", Neural Comput., 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735.
  29. Horrigmoe, G., Saether, I., Antonsen, R. and Arntsen, B. (2007), "Laboratory investigations of steel bar corrosion in concrete: Sustainable bridges background document SB3. 10".
  30. Huang, G. Bin, Zhu, Q.Y. and Siew, C.K. (2006), "Extreme learning machine: Theory and applications", Neurocomput., 70(1-3), 489-501. https://doi.org/10.1016/j.neucom.2005.12.126.
  31. Imam, A., Anifowose, F. and Azad, A.K. (2015), "Residual strength of corroded reinforced concrete beams using an adaptive model based on ANN", Int. J. Concrete Struct. Mater., 9(2), 159-172. https://doi.org/10.1007/s40069-015-0097-4.
  32. Jang, Y., Ahn, Y. and Kim, H.Y. (2019), "Estimating compressive strength of concrete using deep convolutional neural networks with digital microscope images", J. Comput. Civil Eng., 33(3), https://doi.org/10.1061/(ASCE)CP.1943-5487.0000837.
  33. Karahan, O., Tanyildizi, H. and Atis, C.D. (2008), "An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash", J. Zhejiang Univ. Sci. A, 9(11), 1514-1523. https://doi.org/10.1631/jzus.A0720136.
  34. Kashani, M.M. (2017), "Size effect on inelastic buckling behavior of accelerated pitted corroded bars in porous media", J. Mater. Civil Eng., 29(7), 04017022. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001853.
  35. Krizhevsky, A., Sutskever, I. and Hinton, G.E. (n.d.), "ImageNet classification with deep convolutional neural networks", Adv. Neural Inform. Proc. Syst..
  36. Kulash, D., Director, E., Tabb, J.R., Agnew, W.G., Ray Chamberlain, A., Crawford, J.A. and Yarbrough, R.L. (1989), "Concrete and Structures".
  37. Lin, H., Zhao, Y., Feng, P., Ye, H., Ozbolt, J., Jiang, C. and Yang, J.Q. (2019), "State-of-the-art review on the bond properties of corroded reinforcing steel bar", Constr. Build. Mater., 213, 216-233. https://doi.org/10.1016/j.conbuildmat.2019.04.077.
  38. Liu, G., Bao, H. and Han, B. (2018), "A stacked autoencoder-based deep neural network for achieving gearbox fault diagnosis", Math. Prob. Eng., 2018, 5105709. https://doi.org/10.1155/2018/5105709.
  39. Narloch, P., Hassanat, A., Tarawneh, A.S., Anysz, H., Kotowski, J. and Almohammadi, K. (2019), "Predicting compressive strength of cement-stabilized rammed earth based on SEM images using computer vision and deep learnin", Appl. Sci., 9(23), 5131. https://doi.org/10.3390/app9235131.
  40. Nguyen, K.T., Nguyen, Q.D., Le, T.A., Shin, J. and Lee, K. (2020), "Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches", Constr. Build. Mater., 247, 118581. https://doi.org/10.1016/j.conbuildmat.2020.118581.
  41. Nikoo, M., Sadowski, L. and Nikoo, M. (2017), "Prediction of the corrosion current density in reinforced concrete using a self-organizing feature map", Coat., 7(10), "https://doi.org/10.3390/coatings7100160.
  42. Olubanwo, A.O. and Karadelis, J.N. (2015), "Applied mixture optimization techniques for paste design of bonded roller-compacted fibre reinforced polymer modified concrete (BRCFRPMC) overlays", Mater. Struct. Mater. Constr., 48(7), 2023-2042. https://doi.org/10.1617/s11527-014-0291-x.
  43. 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", HNICEM 2017-9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management. https://doi.org/10.1109/HNICEM.2017.8269560.
  44. Saifullah, M. and Clark, L.A. (1994), "Effect of corrosion rate on the bond strength of corroded reinforcement", Corros. Corros. Protect. Steel Concrete, 1, 591-602.
  45. Shima, H. (2001), "Local bond stress-slip relationship of corroded steel bars embedded in concrete", Third International Conference on Concrete Under Sever Conditions.
  46. Shin, H.K., Ahn, Y.H., Lee, S. and Kim, H.Y. (2019), "Digital vision based concrete compressive strength evaluating model using deep convolutional neural network", Comput. Mater. Contin., 61(2), 911-928. https://doi.org/10.32604/cmc.2019.08269.
  47. Shirkhani, A., Davarnia, D. and Farahmand Azar, B. (2019), "Prediction of bond strength between concrete and rebar under corrosion using ANN", Comput. Concrete, 23(4), 273-279. https://doi.org/doi.org/10.12989/cac.2019.23.4.273.
  48. Song, H.W. and Saraswathy, V. (2007), "Corrosion monitoring of reinforced concrete structures-A review", Int. J. Electrochem. Sci., 2(1), 1-28.
  49. Stewart, M.G. (2009), "Mechanical behaviour of pitting corrosion of flexural and shear reinforcement and its effect on structural reliability of corroding RC beams", Struct. Saf., 31(1), 19-30. https://doi.org/10.1016/j.strusafe.2007.12.001.
  50. Tang, J., Yan, D. and Zhao, L.J. (2014), "Comparison of several extreme learning machine algorithm for modeling concrete compressive strength", Appl. Mech. Mater., 2014, 1735-1738. https://doi.org/10.4028/www.scientific.net/AMM.548-549.1735.
  51. Tanyildizi, H. (2018a), "Long-term microstructure and mechanical properties of polymer-phosphazene concrete exposed to freeze-thaw", Constr. Build. Mater., 187, 1121-1129. https://doi.org/10.1016/j.conbuildmat.2018.08.068.
  52. Tanyildizi, H. (2018b), "Long-term performance of the healed mortar with polymer containing phosphazene after exposed to sulfate attack", Constr. Build. Mater., 167, 473-481. https://doi.org/10.1016/j.conbuildmat.2018.02.054.
  53. Tanyildizi, H. (2021), "Predicting the geopolymerization process of fly ash-based geopolymer using deep long short-term memory and machine learning", Cement Concrete Compos., 123, 104177. https://doi.org/10.1016/j.cemconcomp.2021.104177.
  54. Tanyildizi, H., Coskun, A. and Somunkiran, I. (2008), "An experimental investigation of bond and compressive strength of concrete with mineral admixtures at high temperatures", Arab. J. Sci. Eng., 33(2B), 443-449.
  55. Uysal, M., Akyuncu, V., Tanyildizi, H., Sumer, M. and Yildirim, H. (2019), "Optimization of durability properties of concrete containing fly ash using Taguchi's approach and Anova analysis", Revista de La Construccio https://doi.org/10.7764/RDLC.17.3.364.
  56. Wallbank, E.J. (1989), The Performance of Concrete in Bridges a Survey of 200 Highway Bridges, Performance of Concrete in Bridges a Survey of 200 Highway Bridges.
  57. Wang, B., Li, Q., Liu, F., Wang, J. and Xu, S. (2018), "Shear bond assessment of UHTCC repair using push-out test", Constr. Build. Mater., 164, 206-216. https://doi.org/10.1016/j.conbuildmat.2017.12.148.
  58. Wroblewski, L., Hristozov, D. and Sadeghian, P. (2016), "Durability of bond between concrete beams and FRP composites made of flax and glass fibers", Constr. Build. Mater., 126, 800-811. https://doi.org/10.1016/j.conbuildmat.2016.09.095.
  59. 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.
  60. Yartsev, V.P., Nikolyukin, A.N. and Korneeva, A.O. (2019), "Neural network modeling of concrete bond strength to reinforcement", IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing. https://doi.org/10.1088/1757-899X/687/3/033011.
  61. Yaseen, Z.M., Deo, R.C., Hilal, A., Abd, A.M., Bueno, L.C., Salcedo-Sanz, S. and Nehdi, M.L. (2018), "Predicting compressive strength of lightweight foamed concrete using extreme learning machine model", Adv. Eng. Softw., 115, 112-125. https://doi.org/10.1016/j.advengsoft.2017.09.004.
  62. Zhao, Y.X. and Jin, W.L. (2002), "Test study on bond behavior of corroded steel bars and concrete", J. Zhejiang Univ., 36(4), 352-356. https://doi.org/10.3785/j.issn.1008-973X.2002.04.002