Influence of the ANN Hyperparameters on the Forecast Accuracy of RAC’s Compressive Strength

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorAlmeida, Talita Andrade da Costa-
Autor(es): dc.creatorFelix, Emerson Felipe-
Autor(es): dc.creatorde Sousa, Carlos Manuel Andrade-
Autor(es): dc.creatorPedroso, Gabriel Orquizas Mattielo-
Autor(es): dc.creatorMotta, Mariana Ferreira Benessiuti-
Autor(es): dc.creatorPrado, Lisiane Pereira-
Data de aceite: dc.date.accessioned2025-08-21T16:45:29Z-
Data de disponibilização: dc.date.available2025-08-21T16:45:29Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/ma16247683-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307525-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307525-
Descrição: dc.descriptionThe artificial neural networks (ANNs)-based model has been used to predict the compressive strength of concrete, assisting in creating recycled aggregate concrete mixtures and reducing the environmental impact of the construction industry. Thus, the present study examines the effects of the training algorithm, topology, and activation function on the predictive accuracy of ANN when determining the compressive strength of recycled aggregate concrete. An experimental database of compressive strength with 721 samples was defined considering the literature. The database was used to train, validate, and test the ANN-based models. Altogether, 240 ANNs were trained, defined by combining three training algorithms, two activation functions, and topologies with a hidden layer containing 1–40 neurons. The ANN with a single hidden layer including 28 neurons, trained with the Levenberg–Marquardt algorithm and the hyperbolic tangent function, achieved the best level of accuracy, with a coefficient of determination equal to 0.909 and a mean absolute percentage error equal to 6.81%. Furthermore, the results show that it is crucial to avoid the use of overly complex models. Excessive neurons can lead to exceptional performance during training but poor predictive ability during testing.-
Descrição: dc.descriptionDepartment of Civil Engineering School of Science and Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of Civil Engineering School of Science and Engineering São Paulo State University (UNESP)-
Idioma: dc.languageen-
Relação: dc.relationMaterials-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial neural networks-
Palavras-chave: dc.subjectcompressive strength prediction-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectrecycled aggregate concrete-
Título: dc.titleInfluence of the ANN Hyperparameters on the Forecast Accuracy of RAC’s Compressive Strength-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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