DOI QR코드

DOI QR Code

Practical applicable model for estimating the carbonation depth in fly-ash based concrete structures by utilizing adaptive neuro-fuzzy inference system

  • Aman Kumar (AcSIR-Academy of Scientific and Innovative Research) ;
  • Harish Chandra Arora (AcSIR-Academy of Scientific and Innovative Research) ;
  • Nishant Raj Kapoor (AcSIR-Academy of Scientific and Innovative Research) ;
  • Denise-Penelope N. Kontoni (Department of Civil Engineering, School of Engineering, University of the Peloponnese) ;
  • Krishna Kumar (UJVN Ltd.) ;
  • Hashem Jahangir (Department of Civil Engineering, University of Birjand) ;
  • Bharat Bhushan (Structural Engineering Department, CSIR-Central Building Research Institute)
  • Received : 2022.06.05
  • Accepted : 2023.04.12
  • Published : 2023.08.25

Abstract

Concrete carbonation is a prevalent phenomenon that leads to steel reinforcement corrosion in reinforced concrete (RC) structures, thereby decreasing their service life as well as durability. The process of carbonation results in a lower pH level of concrete, resulting in an acidic environment with a pH value below 12. This acidic environment initiates and accelerates the corrosion of steel reinforcement in concrete, rendering it more susceptible to damage and ultimately weakening the overall structural integrity of the RC system. Lower pH values might cause damage to the protective coating of steel, also known as the passive film, thus speeding up the process of corrosion. It is essential to estimate the carbonation factor to reduce the deterioration in concrete structures. A lot of work has gone into developing a carbonation model that is precise and efficient that takes both internal and external factors into account. This study presents an ML-based adaptive-neuro fuzzy inference system (ANFIS) approach to predict the carbonation depth of fly ash (FA)-based concrete structures. Cement content, FA, water-cement ratio, relative humidity, duration, and CO2 level have been used as input parameters to develop the ANFIS model. Six performance indices have been used for finding the accuracy of the developed model and two analytical models. The outcome of the ANFIS model has also been compared with the other models used in this study. The prediction results show that the ANFIS model outperforms analytical models with R-value, MAE, RMSE, and Nash-Sutcliffe efficiency index values of 0.9951, 0.7255 mm, 1.2346 mm, and 0.9957, respectively. Surface plots and sensitivity analysis have also been performed to identify the repercussion of individual features on the carbonation depth of FA-based concrete structures. The developed ANFIS-based model is simple, easy to use, and cost-effective with good accuracy as compared to existing models.

Keywords

References

  1. Ahmadi-Nedushan, B. (2012), "Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models", Constr. Build. Mater., 36, 665-673. https://doi.org/10.1016/j.conbuildmat.2012.06.002.
  2. Alshimmeri, A.J.H., Konton, D.P.N. and Ghamari, A. (2021), "Improving the seismic performance of reinforced concrete frames using an innovative metallic-shear damper", Comput. Concrete, 28(3), 275-287. https://doi.org/10.12989/cac.2021.28.3.275.
  3. Arora, H.C., Kumar, S., Kontoni, D.P.N., Kumar, A., Sharma, M., Kapoor, N.R. and Kumar, K. (2023). "Axial capacity of FRP-reinforced concrete columns: Computational intelligence-based prognosis for sustainable structures", Build., 12(12), 2137. https://doi.org/10.3390/buildings12122137.
  4. Atis, C.D. (2003), "Accelerated carbonation and testing of concrete made with fly ash", Constr. Build. Mater., 17, 147-152. https://doi.org/10.1016/S0950-0618(02)00116-2.
  5. Balayssac, J.P., Detriche, C.H. and Grandet, J. (1995), "Effects of curing upon carbonation of concrete", Constr. Build. Mater., 9, 91-95. https://doi.org/10.1016/0950-0618(95)00001-V.
  6. Burden, D. (2006), "The durability of concrete containing high levels of fly ash", Master Thesis, University of New Brunswick, Fredericton, NB, Canada.
  7. Cao, Y., Fan, Q., Mahmoudi Azar, S., Alyousef, R., Yousif, S.T., Wakil, K., Jermsittiparsert, K., Si Ho, L., Alabduljabbar, H. and Alaskar, A. (2020), "Computational parameter identification of strongest influence on the shear resistance of reinforced concrete beams by fiber reinforcement polymer", Struct., 27, 118-127. https://doi.org/10.1016/j.istruc.2020.05.031.
  8. Chang, C.F. and Chen, J.W. (2006), "The experimental investigation of concrete carbonation depth", Cement Concrete Res., 36, 1760-1767. https://doi.org/10.1016/j.cemconres.2004.07.025.
  9. Chen, Y., Liu, P. and Yu, Z. (2018), "Effects of environmental factors on concrete carbonation depth and compressive strength", Mater., 11, 2167. https://doi.org/10.3390/ma11112167.
  10. Chung, L., Hur, M.W. and Park, T. (2018), "Performance evaluation of CFRP reinforced concrete members utilizing fuzzy technique", Int. J. Concrete Struct. Mater., 12, 78. https://doi.org/10.1186/s40069-018-0313-0.
  11. Concha, N.C. and Oreta, A.W. (2021), "Investigation of the effects of corrosion on bond strength of steel in concrete using neural network", Comput. Concrete, 28(1), 77-91. https://doi.org/10.12989/cac.2021.28.1.077.
  12. Cui, H., Tang, W., Liu, W., Dong, Z. and Xing, F. (2015), "Experimental study on effects of CO2 concentrations on concrete carbonation and diffusion mechanisms", Constr. Build. Mater., 93, 522-527. https://doi.org/10.1016/j.conbuildmat.2015.06.007.
  13. Das, B.B. and Pandey, S.P. (2011), "Influence of fineness of fly ash on the carbonation and electrical conductivity of concrete", J. Mater. Civil Eng., 23, 1365-1368. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000298.
  14. Ebadi-Jamkhaneh, M., Homaioon-Ebrahimi, A. and Kontoni, D.P.N. (2021), "Numerical finite element study of strengthening of damaged reinforced concrete members with carbon and glass FRP wraps", Comput. Concrete, 28(2), 137-147. https://doi.org/10.12989/cac.2021.28.2.137.
  15. FIB (2009), Structural concrete: Textbook on Behaviour, Design and Performance, Federation Internationale du Beton (fib), Lausanne, Switzerland.
  16. Fu, C., Ye, H., Jin, X., Jin, N. and Gong, L. (2015), "A reaction-diffusion modeling of carbonation process in self-compacting concrete", Comput. Concrete, 15(5), 847-864. https://doi.org/10.12989/cac.2015.15.5.847.
  17. Gagg, C.R. (2014), "Cement and concrete as an engineering material: An historic appraisal and case study analysis", Eng. Fail. Anal., 40, 114-140. https://doi.org/10.1016/j.engfailanal.2014.02.004.
  18. Hussain, S., Bhunia, D. and Singh, S.B. (2017), "Comparative study of accelerated carbonation of plain cement and fly-ash concrete", J. Build. Eng., 10, 26-31. https://doi.org/10.1016/j.jobe.2017.02.001.
  19. Jahangir, H. and Esfahani, M.R. (2020), "Investigating loading rate and fibre densities influence on SRG-concrete bond behaviour", Steel Compos. Struct., 34(6), 877-889. https://doi.org/10.12989/scs.2020.34.6.877.
  20. Jiang, L., Lin, B. and Cai, Y. (2000), "A model for predicting carbonation of high-volume fly ash concrete", Cem. Concrete Res., 30, 699-702. https://doi.org/10.1016/S0008-8846(00)00227-1.
  21. Jiang, L., Liu, Z. and Ye, Y. (2004), "Durability of concrete incorporating large volumes of low-quality fly ash", Cement Concrete Res., 34, 1467-1469. https://doi.org/10.1016/j.cemconres.2003.12.029.
  22. Kapoor, N.R., Kumar, A., Kumar, A., Kumar, A., Mohammed, M.A., Kumar, K., Kadry, S. and Lim, S. (2022), "Machine learning-based CO2 prediction for office room: A pilot study", Wirel. Commun. Mob. Comput., 2022, 1-16. https://doi.org/10.1155/2022/9404807.
  23. Kellouche, Y., Boukhatem, B., Ghrici, M. and Tagnit-Hamou, A. (2019), "Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network", Neural. Comput. Appl., 31, 969-988. https://doi.org/10.1007/s00521-017-3052-2.
  24. Khunthongkeaw, J., Tangtermsirikul, S. and Leelawat, T. (2006), "A study on carbonation depth prediction for fly ash concrete", Constr. Build. Mater., 20, 744-753. https://doi.org/10.1016/j.conbuildmat.2005.01.052.
  25. Kumar, A., Arora, H.C., Kapoor, N.R. and Kumar, K. (2022a), "Prognosis of compressive strength of fly-ash-based geopolymer-modified sustainable concrete with ML algorithms", Struct. Concrete, 2022, 1. https://doi.org/10.1002/suco.202200344.
  26. Kumar, A., Arora, H.C., Kapoor, N.R., Mohammed, M.A., Kumar, K., Majumdar, A. and Thinnukool, O. (2022b), "Compressive strength prediction of lightweight concrete: Machine learning models", Sustainab., 14, 2404. https://doi.org/10.3390/su14042404.
  27. Kumar, K. and Saini, R.P. (2022), "Development of correlation to predict the efficiency of a hydro machine under different operating conditions", Sustain. Energy Technol. Assess., 50, 101859. https://doi.org/10.1016/j.seta.2021.101859.
  28. Kumar, A., Arora, H.C., Kumar, K., and Garg, H. (2023), "Performance prognosis of FRCM-to-concrete bond strength using ANFIS-based fuzzy algorithm", Expert Syst. Appl., 216, 119497. https://doi.org/10.1016/j.eswa.2022.119497.
  29. Lammertijn, S. and Belie, N.D. (2008), "Porosity, gas permeability, carbonation and their interaction in high-volume fly ash concrete", Mag. Concrete Res., 60, 535-545. https://doi.org/10.1680/macr.2008.60.7.535.
  30. Liu, P., Yu, Z. and Chen, Y. (2020), "Carbonation depth model and carbonated acceleration rate of concrete under different environment", Cement Concrete Compos., 114, 103736. https://doi.org/10.1016/j.cemconcomp.2020.103736.
  31. Lye, C.Q., Dhir, R.K. and Ghataora, G.S. (2015), "Carbonation resistance of fly ash concrete", Mag. Concrete Res., 67, 1150-1178. https://doi.org/10.1680/macr.15.00204.
  32. Mccarthy, M.J. and Dhir, R.K. (2005), "Development of high volume fly ash cements for use in concrete construction", Fuel, 84, 1423-1432. https://doi.org/10.1016/j.fuel.2004.08.029.
  33. Murad, Y.Z. (2020), "Prediction model for concrete carbonation depth using gene expression programming", Comput. Concrete, 26, 497-504. https://doi.org/10.12989/cac.2020.26.6.497.
  34. Naderpour, H. and Mirrashid, M. (2020), "Moment capacity estimation of spirally reinforced concrete columns using ANFIS", Complex Intell. Syst., 6, 97-107. https://doi.org/10.1007/s40747-019-00118-2.
  35. Papadakis, V.G. and Fardis, M.N. (1989), "A reaction engineering approach to the problem of concrete carbonation", Am. J. Chem. Eng., 35(10), 1639-1651. https://doi.org/10.1002/aic.690351008
  36. Paul, S.C., Van Zijl, G.P.A.G., Babafemi, A.J. and Tan, M.J. (2016), "Chloride ingress in cracked and uncracked SHCC under cyclic wetting-drying exposure", Constr. Build. Mater., 114, 232-240. https://doi.org/10.1016/j.conbuildmat.2016.03.206.
  37. Phoo-Ngernkham, T., Hanjitsuwan, S., Damrongwiriyanupap, N. and Chindaprasirt, P. (2017), "Effect of sodium hydroxide and sodium silicate solutions on strengths of alkali activated high calcium fly ash containing Portland cement", KSCE J. Civil Eng., 21, 2202-2210. https://doi.org/10.1007/s12205-016-0327-6.
  38. 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, 257-262. https://doi.org/10.12989/cac.2017.19.3.257.
  39. Qiao, G., Hong, Y. and Ou, J. (2015), "Corrosion monitoring of the RC structures in time domain: Part I. Response analysis of the electrochemical transfer function based on complex function approximation", Measure., 67, 78-83. https://doi.org/10.1016/j.measurement.2014.12.018.
  40. Roziere, E., Loukili, A. and Cussigh, F. (2009), "A performance based approach for durability of concrete exposed to carbonation", Constr. Build. Mater., 23, 190-199. https://doi.org/10.1016/j.conbuildmat.2008.01.006.
  41. Sharafati, A., Naderpour, H., Salih, S.Q., Onyari, E. and Yaseen, Z.M. (2021), "Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system optimized by nature-inspired algorithms", Front. Struct. Civil Eng., 15, 61-79. https://doi.org/10.1007/s11709-020-0684-6.
  42. Shariati, M., Mafipour, M.S., Mehrabi, P., Shariati, A., Toghroli, A., Trung, N.T. and Salih, M.N.A. (2021), "A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques", Eng. Comput., 37, 2089-2109. https://doi.org/10.1007/s00366-019-00930-x.
  43. Sharma, S., Arora, H.C., Kumar, A., Kontoni, D.P.N., Kapoor, N.R., Kumar, K. and Singh, A. (2023), "Computational intelligence-based structural health monitoring of corroded and eccentrically loaded reinforced concrete columns", Shock Vib., 2023, 9715120. https://doi.org/10.1155/2023/9715120.
  44. Sisomphon, K. and Franke, L. (2007), "Carbonation rates of concretes containing high volume of pozzolanic materials", Cement Concrete Res., 37, 1647-1653. https://doi.org/10.1016/j.cemconres.2007.08.014.
  45. Sulapha, P., Wong, S.F., Wee, T.H. and Swaddiwudhipong, S. (2003), "Carbonation of concrete containing mineral admixtures", J. Mater. Civil Eng., 15, 134-143. https://doi.org/10.1061/(ASCE)0899-1561(2003)15:2(134).
  46. Suparta, W. and Alhasa, K.M. (2016), "Adaptive neuro-fuzzy interference system", Modeling of Tropospheric Delays Using ANFIS, Springer International Publishing, Cham, Switzerland.
  47. Surajudeen-Bakinde, N.T., Faruk, N., Popoola, S.I., Salman, M.A., Oloyede, A.A., Olawoyin, L.A. and Calafate, C.T. (2018), "Path loss predictions for multi-transmitter radio propagation in VHF bands using adaptive neuro-fuzzy inference system", Int. J. Eng. Sci. Technol., 21, 679-691. https://doi.org/10.1016/j.jestch.2018.05.013.
  48. Takagi, T. and Sugeno, M. (1985), "Fuzzy identification of systems and its applications to modeling and control", IEEE Trans. Syst. Man Cybern., 15, 116-132. https://doi.org/10.1109/TSMC.1985.6313399.
  49. Turcry, P., Oksri-Nelfia, L., Younsi, A. and Ait-Mokhtar, A. (2014), "Analysis of an accelerated carbonation test with severe preconditioning", Cement Concrete Res., 57, 70-78. https://doi.org/10.1016/j.cemconres.2014.01.003.
  50. Van Den Heede, P. and De Belie, N. (2014), "A service life based global warming potential for high-volume fly ash concrete exposed to carbonation", Constr. Build. Mater., 55, 183-193. https://doi.org/10.1016/j.conbuildmat.2014.01.033.
  51. Wei, Y., Han, A. and Xue, X. (2021), "A data-driven study for evaluating the compressive strength of high-strength concrete", Int. J. Mach. Learn. Cybern., 12, 3585-3595. https://doi.org/10.1007/s13042-021-01407-4.
  52. Xu, H., Zhanqing, C., Subei, L., Wei, H. and Dan, M. (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.
  53. Yoon, I.S. (2009), "Simple approach to calculate chloride diffusivity of concrete considering carbonation", Comput. Concrete, 6(1), 1-18. https://doi.org/10.12989/cac.2009.6.1.001.
  54. Younsi, A., Turcry, P., Ait-Mokhtar, A. and Staquet, S. (2013), "Accelerated carbonation of concrete with high content of mineral additions: Effect of interactions between hydration and drying", Cem. Concrete Res., 43, 25-33. https://doi.org/10.1016/j.cemconres.2012.10.008.
  55. Younsi, A., Turcry, P., Roziere, E., Ait-Mokhtar, A. and Loukili, A. (2011), "Performance-based design and carbonation of concrete with high fly ash content", Phys. Eng. Sci., 33, 993-1000. https://doi.org/10.1016/j.cemconcomp.2011.07.005.
  56. Zhang, P. and Li, Q. (2013), "Effect of fly ash on durability of high performance concrete composites", Res. J. Appl. Sci., 6, 7-12. http://doi.org/10.19026/rjaset.6.4026.
  57. Zhu, A.M. (1992), "Concrete carbonation and durability of reinforced concrete", Concrete, 6, 18-21.