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

P. Akpinar; I. D. Uwanuakwa

doi:10.3989/mc.2020.02019

Autores/as

P. Akpinar Civil Engineering Department, Faculty of Civil and Environmental Engineering, Near East University, TRNC https://orcid.org/0000-0002-6885-8105
I. D. Uwanuakwa Civil Engineering Department, Faculty of Civil and Environmental Engineering, Near East University, TRNC https://orcid.org/0000-0001-5546-0408

DOI:

https://doi.org/10.3989/mc.2020.02019

Palabras clave:

Hormigón, Cenizas volantes, Durabilidad, Carbonatación, Modelización

Resumen

La carbonatación es un problema perjudicial de durabilidad del hormigón que puede alterar la microestructura del hormigón y provocar el inicio de la corrosión en barras de refuerzo. Estudios previos se centraron en el uso de redes neuronales artificiales (RNA) para la predicción de la profundidad de la carbonatación del hormigón y para minimizar la necesidad de pruebas de laboratorio destructivas y elaboradas. Este estudio tiene como objetivo proporcionar una precisión mejorada de la simulación y la predicción de la carbonatación con una arquitectura RNA que incluye dieciocho parámetros de entrada con una función alternativa de Gradiente de Conjugado Escalado. Después de asegurar un valor prometedor del coeficiente de correlación tan alto como 0.98, se estudió la influencia de los parámetros de entrada propuestos en el progreso de la profundidad de carbonatación. Se observó que los resultados de este análisis paramétrico cumplían exitosamente con la experiencia de ingeniería civil convencional. Por lo tanto, el modelo RNA empleado puede ser utilizado como una herramienta eficiente para estudiar en detalle y proporcionar información sobre el problema de carbonatación en el hormigón.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Houst, Y.F. (1996) The role of moisture in the carbonation of cementitious materials. Int. J. Restor. Build. Monum. 2, 49-66. https://doi.org/10.1515/rbm-1996-5083

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. https://doi.org/10.1016/j.cemconres.2008.07.002

Shamsad, A. (2003) Reinforcement corrosion in concrete structures, its monitoring and service life prediction--a review. Cem. Concr. Compos. 25[4-5], 459-471. https://doi.org/10.1016/S0958-9465(02)00086-0

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. https://doi.org/10.1016/j.electacta.2005.04.014

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. https://doi.org/10.1051/matecconf/201820306007

Khashman, A.; Akpinar, P. (2017) Non-destructive prediction of concrete compressive strength using neural networks. Procedia. Comput. Sci. 108, 2358-2362. https://doi.org/10.1016/j.procs.2017.05.039

Khashman, A. (2010) Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. Expert. Syst. Appl. 37[9], 6233-6239. https://doi.org/10.1016/j.eswa.2010.02.101

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. https://doi.org/10.1016/j.cemconres.2009.08.022

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. https://doi.org/10.1155/2009/193139

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. https://doi.org/10.1016/j.conbuildmat.2015.09.058

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. https://doi.org/10.1016/j.cemconres.2007.04.015

Kari, O.P.; Puttonen, J.; Skantz, E. (2014) Reactive transport modelling of long-term carbonation. Cem. Concr. Compos. 5242-53. https://doi.org/10.1016/j.cemconcomp.2014.05.003

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. https://doi.org/10.1016/j.cemconres.2004.06.023

Chang, C.F.; Chen, J.W. (2006) The experimental investigation of concrete carbonation depth. Cem. Concr. Res. 36[9], 1760-1767. https://doi.org/10.1016/j.cemconres.2004.07.025

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. https://doi.org/10.1016/j.conbuildmat.2015.06.007

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. https://doi.org/10.1016/S0008-8846(00)00227-1

Balayssac, J.P.; Détriché, C.H.; Grandet, J. (1995) Effects of curing upon carbonation of concrete. Constr. Build. Mater. 9[2], 91-95. https://doi.org/10.1016/0950-0618(95)00001-V

Ati?, C.D. (2003) Accelerated carbonation and testing of concrete made with fly ash. Constr. Build. Mater. 17[3], 147-152. https://doi.org/10.1016/S0950-0618(02)00116-2

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. https://doi.org/10.1016/j.conbuildmat.2008.01.006

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. https://doi.org/10.1016/j.jobe.2017.02.001

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. https://doi.org/10.1016/j.cemconres.2012.10.008

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. https://doi.org/10.1016/j.cemconres.2014.01.003

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. https://doi.org/10.1016/j.cemconres.2009.10.020

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. https://doi.org/10.1016/j.cemconres.2017.03.001

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. https://doi.org/10.1016/j.cemconres.2018.09.019

Ashraf, W. (2016) Carbonation of cement-based materials: Challenges and opportunities. Constr. Build. Mater. 120, 558-570. https://doi.org/10.1016/j.conbuildmat.2016.05.080

Wang, A.; Zhang, C.; Sun, W. (2004) Fly ash effects: II. The active effect of fly ash. Cem. Concr. Res. 34[11], 2057-2060. https://doi.org/10.1016/j.cemconres.2003.03.001

Parrott, L.J. (1996) Some effects of cement and curing upon carbonation and reinforcement corrosion in concrete. Mater. Struct. 29, 164-173. https://doi.org/10.1007/BF02486162

Liu, P.; Chen, Y.; Yu, Z. (2019) Effects of temperature, relative humidity and CO2 concentration on concrete carbonation. Mag. Concr. Res. 1-44. https://doi.org/10.1680/jmacr.18.00496

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. https://doi.org/10.1021/ef201543n

Park, D.C. (2008) Carbonation of concrete in relation to CO2 permeability and degradation of coatings. Constr. Build. Mater. 22[11], 2260-2268. https://doi.org/10.1016/j.conbuildmat.2007.07.032

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. https://doi.org/10.1016/j.cemconres.2007.04.002

Silva, A.; Neves, R.; De Brito, J. (2014) Statistical modelling of carbonation in reinforced concrete. Cem. Concr. Compos. 50, 73-81. https://doi.org/10.1016/j.cemconcomp.2013.12.001

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. https://doi.org/10.1109/23.589532

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. https://doi.org/10.1007/s00521-017-3052-2

Arandigoyen, M.; Álvarez, J.I.; Álvarez, J.I. (2006) Pore structure and carbonation in blended lime-cement pastes. Mater. Construcc. 56[282], 17-30. https://doi.org/10.3989/mc.2006.v56.i281.88

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. https://doi.org/10.1016/j.conbuildmat.2017.12.128

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. https://doi.org/10.1016/j.conbuildmat.2005.01.052

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. https://doi.org/10.2174/1874149520130708004

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. https://doi.org/10.1007/s11709-008-0046-2