Advances in concrete construction

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Algorithms to measure carbonation depth in concrete structures sprayed with a phenolphthalein solution

  • Ruiz, Christian C. (Department of Mechatronic Engineering, Universidad Militar Nueva Granada) ;
  • Caballero, Jose L. (Department of Mechatronic Engineering, Universidad Militar Nueva Granada) ;
  • Martinez, Juan H. (Department of Mechatronic Engineering, Universidad Militar Nueva Granada) ;
  • Aperador, Willian A. (Department of Mechatronic Engineering, Universidad Militar Nueva Granada)
  • Received : 2019.09.18
  • Accepted : 2020.01.23
  • Published : 2020.03.25
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Abstract

Many failures of concrete structures are related to steel corrosion. For this reason, it is important to recognize how the carbonation can affect the durability of reinforced concrete structures. The repeatability of the carbonation depth measure in a specimen of concrete sprayed with a phenolphthalein solution is consistently low whereby it is necessary to have an impartial method to measure the carbonation depth. This study presents two automatic algorithms to detect the non-carbonated zone in concrete specimens. The first algorithm is based solely on digital processing image (DPI), mainly morphological and threshold techniques. The second algorithm is based on artificial intelligence, more specifically on an array of Kohonen networks, but also using some DPI techniques to refine the results. Moreover, another algorithm was developed with the purpose of measure the carbonation depth from the image obtained previously.

Keywords

Acknowledgement

Supported by : Universidad Militar Nueva Granada

This study has been supported by Vicerrectoría de Investigaciones de la Universidad Militar Nueva Granada under project number ING-2992, validity 2019.

References

  1. Akcay, B., Sengul, C. and ali Tasdemir, M. (2016), "Fracture behavior and pore structure of concrete with metakaolin", Adv. Concrete Constr., 4(2), 71-88. https://doi.org/10.12989/acc.2016.4.2.071.
  2. Al-Shamri, M.Y.H. (2014), "Power coefficient as a similarity measure for memory-based collaborative recommender systems", Exp. Syst. Appl., 41(13), 5680-5688. https://doi.org/10.1016/J.ESWA.2014.03.025.
  3. Ali Hameed, A., Karlik, B., Salman, M.S. and Eleyan, G. (2019), "Robust adaptive learning approach to self-organizing maps", Knowledge-Bas. Syst., 171, 25-36. https://doi.org/10.1016/J.KNOSYS.2019.01.011.
  4. Antonio, L., Mehul, B., Alessandro, O. and David, V. (2018), "The role of cognitive architectures in general artificial intelligence", Cognit. Syst. Res., 48, 1-3. https://doi.org/10.1016/J.COGSYS.2017.08.003.
  5. Barros, J.A.O., Lourenco, L.A.P., Soltanzadeh, F. and Taheri, M. (2013), "Steel fibre reinforced concrete for elements failing in bending and in shear", Adv. Concrete Constr., 1(1), 1-27. https://doi.org/10.12989/acc.2013.1.1.001.
  6. Cao, V. and Ronagh, H. (2013), "A model for damage analysis of concrete", Adv. Concrete Constr., 1(2), 187-200. https://doi.org/https://doi.org/10.12989/acc.2013.1.2.187.
  7. Cheng, G., Liu, T., Wang, K. and Han, J. (2006), "Soft competitive learning and growing self-organizing neural networks for pattern classification", 2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 378-381. https://doi.org/10.1109/SYNASC.2006.68.
  8. D'Urso, P., De Giovanni, L. and Massari, R. (2020), "Smoothed self-organizing map for robust clustering", Inform. Sci., 512, 381-401. https://doi.org/10.1016/J.INS.2019.06.038.
  9. Emmanuel, R., Ahmed, L. and Francois, C. (2009), "A performance based approach for durability of concrete exposed to carbonation", Constr. Build. Mater., 23(1), 190-199. https://doi.org/10.1016/j.conbuildmat.2008.01.006.
  10. Guedes, R., Sant Ana, R., Goncalves, L., Oliveira, A., Cardoso, B. and Garcez, A. (2018), "Assessment of the durability of grout submitted to accelerated carbonation test", Constr. Build. Mater., 159, 261-268. https://doi.org/10.1016/j.conbuildmat.2017.10.111.
  11. Hikawa, H. and Maeda, Y. (2015), "Improved learning performance of hardware self-organizing map using a novel neighborhood function", IEEE Tran. Neur. Network. Learn. Syst., 26(11), 2861-2873. https://doi.org/10.1109/TNNLS.2015.2398932.
  12. Choi, J.I., Lee, Y., Kim, Y.Y. and Lee, B.Y. (2017), "Image-processing technique to detect carbonation regions of concrete sprayed with a phenolphthalein solution", Constr. Build. Mater., 154, 451-461. https://doi.org/10.1016/j.conbuildmat.2017.07.205.
  13. Jiang, C., Huang, Q., Gu, X. and Zhang, W. (2017), "Experimental investigation on carbonation in fatigue-damaged concrete", Cement Concrete Res., 99, 38-52. https://doi.org/10.1016/J.CEMCONRES.2017.04.019.
  14. Khvorostukhina, E., L'vov, A. and Ivzhenko, S. (2017), "Performance improvements of a Kohonen self-organizing training algorithm", 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 456-458. https://doi.org/10.1109/EIConRus.2017.7910589.
  15. Li, Z., Antao, T. and Yang, L. (2011), "Hand gesture recognition of sEMG based on modified Kohonen network", 2011 International Conference on Electronics, Communications and Control (ICECC), 1476-1479. https://doi.org/10.1109/ICECC.2011.6066477.
  16. Liwu, M., Feng, Z., Min, D., Fei, J., Abir, A.T. and Aiguo, W. (2017), "Accelerated carbonation and performance of concrete made with steel slag as binding materials and aggregates", Cement Concrete Compo., 83, 138-145. https://doi.org/10.1016/j.cemconcomp.2017.07.018.
  17. Makridakis, S. (2017), "The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms", Future., 90, 46-60. https://doi.org/10.1016/J.FUTURES.2017.03.006.
  18. Malerba, P.G., Sgambi, L., Ielmini, D. and Gotti, G. (2017), "Influence of corrosive phenomena on bearing capacity of RC and PC beams", Adv. Concrete Constr., 5(2), 117-143. https://doi.org/10.12989/acc.2017.5.2.117.
  19. Moshfe, S., Khoei, A., Hadidi, K. and Mashoufi, B. (2010), "A fully programmable nano-watt analogue CMOS circuit for Gaussian functions", 2010 International Conference on Electronic Devices, Systems and Applications, 82-87. https://doi.org/10.1109/ICEDSA.2010.5503099.
  20. Otsu, N. (1979), "A threshold selection method from gray-level histograms", IEEE J. Mag., 9(1), 62-66.
  21. Papadakis, V.G. (2013), "Service life prediction of a reinforced concrete bridge exposed to chloride induced deterioration", Adv. Concrete Constr., 1(3), 201-213. https://doi.org/10.12989/acc2013.1.3.201.
  22. Paul, S.C., Panda, B., Huang, Y., Garg, A. and Peng, X. (2018), "An empirical model design for evaluation and estimation of carbonation depth in concrete", Measure., 124, 205-210. https://doi.org/10.1016/J.MEASUREMENT.2018.04.033.
  23. Pu, Q., Yao, Y., Wang, L., Shi, X., Luo, J. and Xie, Y. (2017), "The investigation of pH threshold value on the corrosion of steel reinforcement in concrete", Comput. Concrete, 19(3), 257-262. https://doi.org/10.12989/cac.2017.19.3.257.
  24. Revert, A., De Weerdt, K., Hombostei, K. and Geiker, M. (2018), "Carbonation-induced corrosion: Investigation of the corrosion onset", Constr. Build. Mater., 162, 847-856. https://doi.org/10.1016/j.conbuildmat.2017.12.066.
  25. Rodriguez, G.R., Chaparro, W.A.A. and Aravena, R.V. (2014), "Software para el calculo de la velocidad de deterioro de los hormigones sometidos a carbonatacion", Revista Latinoamericana de Metalurgia y Materiales, 34(1), 45-54.
  26. Shibata, T., Fukuda, T. and Tanie, K. (1993), "Synthesis of fuzzy, artificial intelligence, neural networks, and genetic algorithm for hierarchical intelligent control-top-down and bottom-up hybrid method", Proceedings of 1993 International Conference on Neural Networks, Nagoya, Japan. https://doi.org/10.1109/IJCNN.1993.714321.
  27. Taffese, W.Z., Sistonen, E. and Puttonen, J. (2015), "CaPrM: Carbonation prediction model for reinforced concrete using machine learning methods", Constr. Build. Mater., 100, 70-82. https://doi.org/10.1016/J.CONBUILDMAT.2015.09.058.
  28. Tang, J., Wu, J., Zou, Z., Yue, A. and Mueller, A. (2018), "Influence of axial loading and carbonation age on the carbonation resistance of recycled aggregate concrete", Constr. Build. Mater., 173, 707-717. https://doi.org/10.1016/j.conbuildmat.2018.03.269.
  29. Tetta, C.M. (1986), "AI: What's in it for you? Career pointers that may lead the intelligent to artificial intelligence", IEEE Potent., 5(3), 19-21. https://doi.org/10.1109/MP.1986.6500803.
  30. Thada, V. and Vivek Jaglan, D. (2013), "Comparison of jaccard, dice, cosine similarity coefficient to find best fitness value for web retrieved documents using genetic algorithm", Int. J. Innov. Eng. Technol., 2(4), 202-205.
  31. Wang, X.Y. and Lee, H.S. (2019), "Microstructure modeling of carbonation of metakaolin blended concrete", Adv. Concrete Constr., 7(3), 167-174. https://doi.org/https://doi.org/10.12989/acc.2019.7.3.167.
  32. Wang, X.Y. and Yao, L. (2018), "Evaluation of carbonation service life of slag blended concrete considering climate changes", Comput. Concrete, 21(4), 419-429. https://doi.org/https://doi.org/10.12989/cac.2018.21.4.419.
  33. Woyciechowski, P. and Soko, J. (2017), "Self-terminated carbonation model as an useful support for durable concrete structure designing", Struct. Eng. Mech., 63(1), 55-64. https://doi.org/https://doi.org/10.12989/sem.2017.63.1.055.
  34. Xu, H., Chen, Z., Li, S., Huang, W. and Ma, D. (2010), "Carbonation test study on low calcium fly ash concrete", Appl. Mech. Mater., 34, 327-331. https://doi.org/10.4028/www.scientific.net/AMM.34-35.327.
  35. Zambon, I., Vidovic, A., Strauss, A., Matos, J. and Friedl, N. (2018), "Prediction of the remaining service life of existing concrete bridges in infrastructural networks based on carbonation and chloride ingress", Smart Struct. Syst., 21(3), 305-320. https://doi.org/https://doi.org/10.12989/sss.2018.21.3.305.
  36. Zhou, X., Tu, X., Chen, A. and Wang, Y. (2019), "Numerical simulation approach for structural capacity of corroded reinforced concrete bridge", Adv. Concrete Constr., 7(1), 11-22. https://doi.org/https://doi.org/10.12989/acc.2019.7.1.011.
  37. Zhu, W. and Francois, R. (2013), "Effect of corrosion pattern on the ductility of tensile reinforcement extracted from a 26-year-old corroded beam", Adv. Concrete Constr., 1(2), 121-136. https://doi.org/https://doi.org/10.12989/acc.2013.1.2.121.
  38. Zhuguo, L. and Sha, L. (2018), "Carbonation resistance of fly ash and blast furnace slag based geopolymer concrete", Constr. Build. Mater., 163, 668-680. https://doi.org/10.1016/j.conbuildmat.2017.12.127.

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