Integrating machine learning and Monte Carlo Simulation for probabilistic assessment of durability in RC structures affected by carbonation-induced corrosion

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorInfrastructure and Territory-
Autor(es): dc.creatorFelix, Emerson F.-
Autor(es): dc.creatorLavinicki, Breno M.-
Autor(es): dc.creatorBueno, Tobias L. G. T.-
Autor(es): dc.creatorde Castro, Thiago C. C.-
Autor(es): dc.creatorCândido, Renan A.-
Data de aceite: dc.date.accessioned2025-08-21T20:19:18Z-
Data de disponibilização: dc.date.available2025-08-21T20:19:18Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s41024-024-00491-7-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308793-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308793-
Descrição: dc.descriptionThis study introduces an original approach that integrates a machine-learning algorithm and a Monte Carlo simulation technique to evaluate the durability of reinforced concrete (RC) structures subjected to carbonation-induced corrosion. The study commences by forecasting the carbonation depth of concrete samples subjected to natural conditions, employing Artificial Neural Networks (ANNs) with the backpropagation algorithm. A database was created by gathering information from 870 literature sources, and it was utilized to build 100 ANN models with different topologies. A rigorous evaluation was conducted to identify the most efficient ANN architecture. Subsequently, the approach was applied in a case study to evaluate the design life of structures in a real scenario, thereby demonstrating its tangible value in real-world applications. In addition, a parametric study was undertaken to examine the material’s compressive strength and the thickness of the concrete cover, which influences its durability. The design life was determined using the Monte Carlo Simulation technique coupled with the ANN model, in which the probability of depassivation due to carbonation was forecasted. Findings indicate that decreasing the concrete cover by 25% would lead to a 48% decrease in the structure’s design life, highlighting the influence of accurately determining and implementing the thickness of the concrete cover for RC structures.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionUniversidade Estadual Paulista-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionSchool of Science and Engineering Department of Civil Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionFederal University of Latin American Integration (UNILA) Latin American Institute of Technology Infrastructure and Territory-
Descrição: dc.descriptionSchool of Science and Engineering Department of Civil Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionCAPES: 001-
Descrição: dc.descriptionUniversidade Estadual Paulista: 06/2023-
Descrição: dc.descriptionFAPESP: 2023/04364-9-
Idioma: dc.languageen-
Relação: dc.relationJournal of Building Pathology and Rehabilitation-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectConcrete carbonation-
Palavras-chave: dc.subjectDurability-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectReinforcement depassivation-
Título: dc.titleIntegrating machine learning and Monte Carlo Simulation for probabilistic assessment of durability in RC structures affected by carbonation-induced corrosion-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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