Investigation of the parameters influencing progress of concrete carbonation depth by using artificial neural networks




Concrete, Fly ash, Durability, Carbonation, Modelization


Carbonation is a deleterious concrete durability problem which may alter concrete microstructure and yield initiation of corrosion in reinforcing steel bars. Previous studies focused on the use of Artificial Neural Networks (ANN) for the prediction of concrete carbonation depth and to minimize the need for destructive and elaborated civil engineering laboratory tests. This study aims to provide improved accuracy of simulation and prediction of carbonation with an ANN architecture including eighteen input parameters employing alternative Scaled Conjugate Gradient (SCG) function. After ensuring a promising value of the coefficient of correlation as high as 0.98, the influence of proposed input parameters on the progress of carbonation depth was studied. The results of this parametric analysis were observed to successfully comply with the conventional civil engineering experience. Hence, the employed ANN model can be used as an efficient tool to study in detail and to provide insights into the carbonation problem in concrete.


Download data is not yet available.


Houst, Y.F. (1996) The role of moisture in the carbonation of cementitious materials. Int. J. Restor. Build. Monum. 2, 49-66.

Castellote, M.; Andrade, C.; Turrillas, X.; Campo, J.; Cuello, G.J. (2008) Accelerated carbonation of cement pastes in situ monitored by neutron diffraction. Cem. Concr. Res. 38[12], 1365-1373.

Shamsad, A. (2003) Reinforcement corrosion in concrete structures, its monitoring and service life prediction--a review. Cem. Concr. Compos. 25[4-5], 459-471.

Huet, B.; L'Hostis, V.; Miserque, F.; Idrissi, H. (2005) Electrochemical behavior of mild steel in concrete: Influence of pH and carbonate content of concrete pore solution. Electrochim. Acta. 51[1], 172-180.

Malami, S.I.; Akpinar, P.; Lawan, M.M. (2018) Preliminary investigation of carbonation problem progress in concrete buildings of north Cyprus. MATEC Web Conf. 20306007.

Khashman, A.; Akpinar, P. (2017) Non-destructive prediction of concrete compressive strength using neural networks. Procedia. Comput. Sci. 108, 2358-2362.

Khashman, A. (2010) Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. Expert. Syst. Appl. 37[9], 6233-6239.

Akpinar, P.; Uwanuakwa, I.D. (2016) Intelligent prediction of concrete carbonation depth using neural networks. Bull. Transilv. Univ. Bras¸ov. Se.r III Math. Phys. 9[2], 99-108.

Kwon, S.-J.; Song, H.-W. (2010) Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modeling. Cem. Concr. Res. 40[1], 119-127.

Lu, C.; Liu, R. (2009) Predicting carbonation depth of prestressed concrete under different stress states using artificial neural network. Adv Artif Neural Syst. 20091-8.

Taffese, W.Z.; Sistonen, E.; Puttonen, J. (2015) CaPrM: Carbonation prediction model for reinforced concrete using machine learning methods. Constr. Build. Mater. 100, 70-82.

Villain, G.; Thiery, M.; Platret, G. (2007) Measurement methods of carbonation profiles in concrete: Thermogravimetry, chemical analysis and gammadensimetry. Cem. Concr. Res. 37[8], 1182-1192.

Kari, O.P.; Puttonen, J.; Skantz, E. (2014) Reactive transport modelling of long-term carbonation. Cem. Concr. Compos. 5242-53.

Saetta, A. V.; Vitaliani, R. V. (2005) Experimental investigation and numerical modeling of carbonation process in reinforced concrete structures: Part II. Practical applications. Cem. Concr. Res. 35[5], 958-967.

Chang, C.F.; Chen, J.W. (2006) The experimental investigation of concrete carbonation depth. Cem. Concr. Res. 36[9], 1760-1767.

Cui, H.; Tang, W.; Liu, W.; Dong, Z.; Xing, F. (2015) Experimental study on effects of CO2 concentrations on concrete carbonation and diffusion mechanisms. Constr. Build. Mater. 93, 522-527.

Jiang, L.; Lin, B.; Cai, Y. (2000) A model for predicting carbonation of high-volume fly ash concrete. Cem. Concr. Res. 30[5], 699-702.

Balayssac, J.P.; Détriché, C.H.; Grandet, J. (1995) Effects of curing upon carbonation of concrete. Constr. Build. Mater. 9[2], 91-95.

Ati?, C.D. (2003) Accelerated carbonation and testing of concrete made with fly ash. Constr. Build. Mater. 17[3], 147-152.

Rozière, E.; Loukili, A.; Cussigh, F. (2009) A performance based approach for durability of concrete exposed to carbonation. Constr. Build. Mater. 23[1], 190-199.

Hussain, S.; Bhunia, D.; Singh, S.B. (2017) Comparative study of accelerated carbonation of plain cement and fly-ash concrete. J. Build. Eng. 10, 26-31.

Villain, G.; Thiery, M.; V, B.-B.; Platret, G. (2007) Different methods to measure the carbonation profiles in concrete. In: Baroghel-Bouny V, Andrade C, Torrent R, Scrivener K (eds) International RILEM Workshop on Performance Based Evaluation and Indicators for Concrete Durability. RILEM Publications, Madrid, 89-98.

Younsi, A.; Turcry, P.; Aït-Mokhtar, A.; Staquet, S. (2013) Accelerated carbonation of concrete with high content of mineral additions: Effect of interactions between hydration and drying. Cem. Concr. Res. 43[1], 25-33.

Turcry, P.; Oksri-Nelfia, L.; Younsi, A.; Aït-Mokhtar, A. (2014) Analysis of an accelerated carbonation test with severe preconditioning. Cem. Concr. Res. 57, 70-78.

Borges, P.H.R.; Costa, J.O.; Milestone, N.B.; Lynsdale, C.J.; Streatfield, R.E. (2010) Carbonation of CH and C-S-H in composite cement pastes containing high amounts of BFS. Cem. Concr. Res. 40[2], 284-292.

Brouwers, H. (2003) Chemical reactions in hydrated ordinary Portland cement based on the work by powers and brownyard. In: Fisher HB (ed) 15th Ibausil (Internationale Baustofftagung). F.A. Finger Institut für Baustoffkunde, Weimar, Germany, pp. 1-0553-1-0566.

Gartner, E.; Maruyama, I.; Chen, J. (2017) A new model for the C-S-H phase formed during the hydration of Portland cements. Cem. Concr. Res. 97, 95-106.

Drouet, E.; Poyet, S.; Le Bescop, P.; Torrenti, J.-M.; Bourbon, X. (2019) Carbonation of hardened cement pastes: Influence of temperature. Cem. Concr. Res. 115, 445-459.

Ashraf, W. (2016) Carbonation of cement-based materials: Challenges and opportunities. Constr. Build. Mater. 120, 558-570.

Wang, A.; Zhang, C.; Sun, W. (2004) Fly ash effects: II. The active effect of fly ash. Cem. Concr. Res. 34[11], 2057-2060.

Parrott, L.J. (1996) Some effects of cement and curing upon carbonation and reinforcement corrosion in concrete. Mater. Struct. 29, 164-173.

Liu, P.; Chen, Y.; Yu, Z. (2019) Effects of temperature, relative humidity and CO2 concentration on concrete carbonation. Mag. Concr. Res. 1-44.

Li, Z.; Fang, F.; Tang, X.; Cai, N. (2012) Effect of temperature on the carbonation reaction of CaO with CO2. Energy & Fuels. 26[4], 2473-2482.

Park, D.C. (2008) Carbonation of concrete in relation to CO2 permeability and degradation of coatings. Constr. Build. Mater. 22[11], 2260-2268.

Thiery, M.; Villain, G.; Dangla, P.; Platret, G. (2007) Investigation of the carbonation front shape on cementitious materials: Effects of the chemical kinetics. Cem. Concr. Res. 37[7], 1047-1058.

Silva, A.; Neves, R.; De Brito, J. (2014) Statistical modelling of carbonation in reinforced concrete. Cem. Concr. Compos. 50, 73-81.

Sola, J.; Sevilla, J. (1997) Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Trans. Nucl. Sci. 44[3], 1464-1468.

Uwanuakwa, I.D. (2019) ANN Regression MATLAB code.

Taffese, W.Z.; Al-Neshawy, F.; Sistonen, E.; Ferreira, M. (2015) Optimized neural network based carbonation prediction model. In: International Symposium Non-Destructive Testing in Civil Engineering (NDT-CE). Berlin, Germany, pp. 1074-1083.

Kellouche, Y.; Boukhatem, B.; Ghrici, M.; Tagnit-Hamou, A. (2019) Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network. Neural. Comput. Appl. 31[S2], 969-988.

Arandigoyen, M.; Álvarez, J.I.; Álvarez, J.I. (2006) Pore structure and carbonation in blended lime-cement pastes. Mater. Construcc. 56[282], 17-30.

Elmoaty, A.E.M.A. (2018) Four-years carbonation and chloride induced steel corrosion of sulfate-contaminated aggregates concrete. Constr. Build. Mater. 163, 539-556.

Katz, A.; Bentur, A.; Wasserman, R. (2015) Effect of cement content on concrete durability. In: Quattrone M, John VM (eds) XIII International Conference on Durability of Building Materials and Components - XIII DBMC. RILEM Publications, Sao Paulo, Brazil, 1137-1142.

Khunthongkeaw, J.; Tangtermsirikul, S.; Leelawat, T. (2006) A study on carbonation depth prediction for fly ash concrete. Constr. Build. Mater. 20[9], 744-753.

Quan, H.; Kasami, H. (2013) Experimental study on effects of type and replacement ratio of fly ash on strength and durability of concrete. Open. Civ. Eng. J. 7[1], 93-100.

Samenow, J. (2016) Two Middle East locations hit 129 degrees, hottest ever in Eastern Hemisphere, maybe the world. Washington Post.

Chen, S.; Sun, W.; Zhang, Y.; Guo, F. (2008) Carbonation depth prediction of fly ash concrete subjected to 2-and 3-dimensional CO2 attack. Front. Archit. Civ. Eng. China. 2[4], 395-400.



How to Cite

Akpinar, P., & Uwanuakwa, I. D. (2020). Investigation of the parameters influencing progress of concrete carbonation depth by using artificial neural networks. Materiales De Construcción, 70(337), e209.



Research Articles