Parametric analysis of carbonation process in reinforced concrete structures through Artificial Neural Networks
Abstract
The aim of this paper is parametrically analyze the main factors that influence on the progress of concrete carbonation front. Therefore, a numerical model was developed using Artificial Neural Networks (ANNs), considering the Multi-Layer Perceptron class, designed in a C++ object-oriented program. The software was fed by experimental degradation data available in the current literature. The results obtained in the parametric analysis, besides adding knowledge to the building pathology area, reinforce concepts already known in the literature, demonstrating the efficiency of ANNs in the investigation of concrete carbonation.
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References
Andrade C. (1992), “Manual para diagnóstico de obras deterioradas por corrosão de armadurasâ€, Tradução de Antônio Carmona e Paulo Helene, São Paulo: PINI, p. 104.
Andrade J. J. O., Possan E., Dal Molin D. C. C. (2017), “Considerations about the service life prediction of reinforced concrete structures inserted in chloride environmentsâ€, Journal of Building Pathology and Rehabilitation 6, pp. 1–8.
Aranha P. M. (1994), “Contribuição ao estudo das manifestações patológicas em estruturas de concreto armado na região Amazônicaâ€, Dissertação de Mestrado em Engenharia, Escola de Engenharia, Escola de Engenharia, Universidade Federal do Rio Grande do Sul, Porto Alegre.
Bob C., Afana E. (1993), “On-site assessment of concrete carbonationâ€, Proceedings of the International Conference Failure of Concrete Carbonation, RILEM, Bratislava, pp. 84–87.
Comission Permanente del Hormigón, EHE (2008), “Instrucción de Hormigón Estructural. Ministério de obras públicas e urbanismoâ€. Madrid, Espanha.
Dal Molin D. C. C. (1988), “Fissuras em estruturas de concreto armado: análise das manifestações tÃpicas e levantamento de casos ocorridos no Estado do Rio Grande do Sulâ€, Dissertação de Mestrado em Engenharia, Universidade Federal do Rio Grande do Sul, Porto Alegre.
Felix, E. F. (2016), “Desenvolvimento de software para a estimativa da profundidade de carbonatação, vida útil e captura de CO2 de estruturas de concreto empregando RNA’sâ€, Trabalho de conclusão de curso, Universidade Federal da Integração Latino-Americana, Foz do Iguaçu.
Güneyisi E. M., Mermerdas K., Güneyisi E., Gesoglu M. (2014), “Numerical modeling of time to corrosion induced cover cracking in reinforced concrete using soft-computing based methodsâ€, Materials and Structures 48, pp. 1739–1756.
Hamada M. (1969), “Neutralization (carbonation) of concrete and corrosion reinforcing steelâ€, proceeding of the 1969 International Symposium on the Chemistry of Cement, Part III, v. II/4, pp. 343–369.
Helene P. R. L. (1993), “Contribuição ao estudo da corrosão em armaduras de concreto armadoâ€, Tese Livre Docência, Escola Politécnica da Universidade de São Paulo, São Paulo.
Houst Y. F., Wittmann F. H. (2002), “Depth profiles of carbonates formed during natural carbonationâ€, Cement and Concrete Research 32, pp. 1923–1930.
Ishida T., Maekawa K. (2001), “Modeling of pH profile in pore water based on mass transport and chemical equilibrium theoryâ€, Concrete Library of JSCE 37, pp. 151–166.
Izumi I., Kita D., Maeda H. (1986), “Carbonationâ€, Kibodang Publication, pp. 35–88.
Jiang L., Lin B., Cai Y. (2000), “A model for predicting carbonation of high-volume fly ash concreteâ€, Cement and Concrete Research 30, pp. 699–702.
Kari O. P., Puttonen J., Skantz E. (2014), “Reactive transport modelling of long-term carbonationâ€, Cement and Concrete Composites 52, pp. 42–53.
Kobayashi K., Uno Y. (1990), “Mechanism of carbonation of concreteâ€, Concrete Library of JSCE 16, pp. 139–151.
Kwon S. J., Song H. W. (2010), “Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modelingâ€, Cement and Concrete Research 40, pp. 119–127.
Lu C., Liu R. (2009), “Predicting carbonation depth of prestressed concrete under different stress states using artificial neural networkâ€, Advances in Artificial Neural Systems 2009, pp. 1–8.
Maekawa K., Ishida T., Kishi T. (2003), “Multi-scale modeling of concrete performanceâ€, Journal of Advanced Concrete Technology 1, pp. 1–126.
Neville A. M. (1997), “Propriedades do concretoâ€, São Paulo: PINI, p. 828.
Papadakis V. G., Vayenas C. G., Fardis M. N. (1991), “Fundamental modeling and experimental investigation of concrete carbonationâ€, ACI Materials Journal 88, pp. 363–373.
Parrot L. J. (1987), “A review of carbonation in reinforced concreteâ€. Cement and concrete Association report.
Pauletti C., Possan E., Dal Molin D. C. C. (2007), “Carbonatação acelerada: estudo da arte das pesquisas no Brasilâ€, Ambiente ConstruÃdo 7, pp. 7–20.
Possan E. (2010), “Modelagem da carbonatação e previsão de vida útil de estruturas de concreto em ambiente urbanoâ€, Tese de Doutorado em Engenharia Civil, Programa de Pós-Graduação em Engenharia Civil, Universidade Federal do Rio Grande do Sul, Porto Alegre. http://www.lume.ufrgs.br/handle/10183/28923.
Possan E., Thomaz W. A., Aleandri G., Felix, E. F., Santos, A. P. S. (2017), “CO2 uptake potential due to concrete carbonation: A case studyâ€, Case Studies in Construction Materials 6, pp. 147–161.
Smolczyk H. G. (1969), “Written Discussionâ€, proceeding of the 1969 International Symposium on the Chemistry of Cement, Part III, v. II/4, pp. 369–384.
Taffese W. Z., Sistonen E. (2013), “Service life prediction of repaired structures using concrete recasting method: state-of-the-artâ€, Procedia Engineering 45, pp. 1138–1144.
Taffese W. Z., Sistonen E., Puttonen J. (2015), “CaPrM: Carbonation prediction model for reinforced concrete using machine-learning methodsâ€, Construction and Building Materials 100, pp. 70–82.
Talukdar S., Banthia N., Grace J. R. (2012), “Carbonation in concrete infrastructure in the context of global climate change – Part 1: Experimental results and model developmentâ€, Cement and Concrete Composites 34, pp. 924–930.
Vesikari E. (1988), “Service life prediction of concrete structures with regard to corrosion of reinforcementâ€. Technical Research Centre of Finland, report n. 553, Finland p. 53.
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