Use of Artificial Neural Networks (ANNs) for the prediction of the Compressive Strength and Modulus of Elasticity of Concrete
The present investigation indicates the design of an artificial intelligence model based on artificial neural networks (RNA) that allows predicting the Compressive Strength (f'c) and Modulus of elasticity (Ec) of concrete. The methodology was carried out in three stages: The Delta Stage, where...
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2024
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| Online toegang: | https://revistadigital.uce.edu.ec/index.php/INGENIO/article/view/5492 |
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Voeg label toe | _version_ | 1863548009322643456 |
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| author | Machado Salazar, Alejandro |
| author2 | Ganchala Padilla, Enlil Santiago Piñarcaja Rivadeneira, Jonathan Mauricio |
| author2_role | author author |
| author_facet | Machado Salazar, Alejandro Ganchala Padilla, Enlil Santiago Piñarcaja Rivadeneira, Jonathan Mauricio |
| author_role | author |
| collection | Revista Ingenio |
| dc.creator.none.fl_str_mv | Machado Salazar, Alejandro Ganchala Padilla, Enlil Santiago Piñarcaja Rivadeneira, Jonathan Mauricio |
| dc.date.none.fl_str_mv | 2024-01-31 |
| dc.format.none.fl_str_mv | application/pdf text/html |
| dc.identifier.none.fl_str_mv | https://revistadigital.uce.edu.ec/index.php/INGENIO/article/view/5492 10.29166/ingenio.v7i1.5492 |
| dc.language.none.fl_str_mv | spa |
| dc.publisher.none.fl_str_mv | Universidad Central del Ecuador |
| dc.relation.none.fl_str_mv | https://revistadigital.uce.edu.ec/index.php/INGENIO/article/view/5492/8197 https://revistadigital.uce.edu.ec/index.php/INGENIO/article/view/5492/8198 |
| dc.rights.none.fl_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0 info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | INGENIO; Vol. 7 No. 1 (2024): Development, Security and Innovation; 23-46 INGENIO; Vol. 7 Núm. 1 (2024): Desarrollo, Seguridad e Innovación; 23-46 2697-3243 2588-0829 reponame:Revista Ingenio instname:Universidad Central del Ecuador instacron:UCE |
| dc.subject.none.fl_str_mv | redes neuronales artificiales (RNA) neural fitting (nftool) diseño de mezclas resistencia a la compresión (f’c) módulo de elasticidad (Ec) Artificial Neural Networks (RNA) Neural Fitting (nftool) Blend Design compression resistance (f'c) modulus of elasticity (Ec) |
| dc.title.none.fl_str_mv | Use of Artificial Neural Networks (ANNs) for the prediction of the Compressive Strength and Modulus of Elasticity of Concrete Uso de Redes Neuronales Artificiales (RNA) para la Predicción de la Resistencia a la Compresión y Módulo de Elasticidad del Hormigón |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | The present investigation indicates the design of an artificial intelligence model based on artificial neural networks (RNA) that allows predicting the Compressive Strength (f'c) and Modulus of elasticity (Ec) of concrete. The methodology was carried out in three stages: The Delta Stage, where a database was formed consisting of the results of concrete designs (aggregate characterization, dosages, compressive strength and modulus of elasticity) made with GU type cement without additives and aggregates from quarries in the Metropolitan District of Quito, obtained from degree work from various universities in the country and from commercial tests carried out by the Materials and Models Testing Laboratory of the Faculty of Engineering and Applied Sciences. In the following Theta Stage, the design of the ANN was carried out using the Matlab software and the Neural Fitting tool (nftool) for training, validation and testing of the ANN through performance indicators such as the Pearson correlation coefficient (R) in the evaluation stage and the coefficient of determination (R2) to measure the efficiency of the ANN; Finally, in the Gamma stage, the predicted results of the ANN were verified with the actual (f'c) and (Ec) of the concrete obtained through tests carried out on 20 concrete cylinders, designed for resistances of 21, 24 and 28 MPa using aggregates from the Pifo quarry and Type GU cement. Establishing that the RNA satisfactorily predicts the compressive strength and modulus of elasticity of concrete, obtaining a value of R2 for (f'c) equal to 95.12% and for (Ec) of 92.20% between the predicted results with the actual results for mixtures of 21, 24 and 28 MPa; validating its use for the prediction of these properties in concrete. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | REVINGENIO_657810ff2a09116d522c92cc9f01b2dd |
| identifier_str_mv | 10.29166/ingenio.v7i1.5492 |
| instacron_str | UCE |
| institution | UCE |
| instname_str | Universidad Central del Ecuador |
| language | spa |
| network_acronym_str | REVINGENIO |
| network_name_str | Revista Ingenio |
| oai_identifier_str | oai:revistadigital.uce.edu.ec:article/5492 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | Universidad Central del Ecuador |
| reponame_str | Revista Ingenio |
| repository.mail.fl_str_mv | * |
| repository.name.fl_str_mv | Revista Ingenio - Universidad Central del Ecuador |
| repository_id_str | 0 |
| rights_invalid_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0 |
| spelling | Use of Artificial Neural Networks (ANNs) for the prediction of the Compressive Strength and Modulus of Elasticity of ConcreteUso de Redes Neuronales Artificiales (RNA) para la Predicción de la Resistencia a la Compresión y Módulo de Elasticidad del HormigónMachado Salazar, AlejandroGanchala Padilla, Enlil Santiago Piñarcaja Rivadeneira, Jonathan Mauricioredes neuronales artificiales (RNA)neural fitting (nftool)diseño de mezclasresistencia a la compresión (f’c)módulo de elasticidad (Ec)Artificial Neural Networks (RNA)Neural Fitting (nftool)Blend Designcompression resistance (f'c)modulus of elasticity (Ec)The present investigation indicates the design of an artificial intelligence model based on artificial neural networks (RNA) that allows predicting the Compressive Strength (f'c) and Modulus of elasticity (Ec) of concrete. The methodology was carried out in three stages: The Delta Stage, where a database was formed consisting of the results of concrete designs (aggregate characterization, dosages, compressive strength and modulus of elasticity) made with GU type cement without additives and aggregates from quarries in the Metropolitan District of Quito, obtained from degree work from various universities in the country and from commercial tests carried out by the Materials and Models Testing Laboratory of the Faculty of Engineering and Applied Sciences. In the following Theta Stage, the design of the ANN was carried out using the Matlab software and the Neural Fitting tool (nftool) for training, validation and testing of the ANN through performance indicators such as the Pearson correlation coefficient (R) in the evaluation stage and the coefficient of determination (R2) to measure the efficiency of the ANN; Finally, in the Gamma stage, the predicted results of the ANN were verified with the actual (f'c) and (Ec) of the concrete obtained through tests carried out on 20 concrete cylinders, designed for resistances of 21, 24 and 28 MPa using aggregates from the Pifo quarry and Type GU cement. Establishing that the RNA satisfactorily predicts the compressive strength and modulus of elasticity of concrete, obtaining a value of R2 for (f'c) equal to 95.12% and for (Ec) of 92.20% between the predicted results with the actual results for mixtures of 21, 24 and 28 MPa; validating its use for the prediction of these properties in concrete.La presente investigación indica el diseño de un modelo de inteligencia artificial en base a redes neuronales artificiales (RNA) que permita predecir la Resistencia a la Compresión (f’c) y Módulo de elasticidad (Ec) del hormigón. La metodología se realizó en tres etapas: La Etapa Delta donde se conformó una base de datos constituida por resultados de diseños de hormigones (caracterización de agregados, dosificaciones, resistencia a la compresión y módulo de elasticidad) elaborados con cemento tipo GU sin aditivos y agregados procedentes de las canteras del Distrito Metropolitano de Quito, obtenidos de trabajos de titulación de diversas universidades del país y de ensayos comerciales realizados por el Laboratorio de Ensayo de Materiales y Modelos de la Facultad de Ingeniería y Ciencias Aplicadas. En la siguiente Etapa Theta se realizó el diseño de la RNA utilizando el software Matlab y la herramienta Neural Fitting (nftool) para el entrenamiento, validación y testeo de la RNA a través de indicadores de desempeño como el coeficiente de correlación de Pearson (R) en la etapa de evaluación y el coeficiente de determinación (R2) para medir la eficiencia de la RNA; finalmente en la etapa Gamma se comprobó los resultados pronosticados de la RNA con el (f’c) y (Ec) real del hormigón obtenidos a través de ensayos realizados a 20 cilindros de hormigón, diseñados para resistencias de 21, 24 y 28 MPa utilizando agregados de la cantera de Pifo y cemento Tipo GU. Estableciendo que la RNA predice satisfactoriamente la resistencia a la compresión y módulo de elasticidad del hormigón obteniendo un valor de R2 para el (f’c) igual a 95.12% y para el (Ec) de 92.20% entre los resultados pronosticados con los resultados reales para mezclas de 21, 24 y 28 MPa; validando su uso para la predicción de estas propiedades en el hormigón.Universidad Central del Ecuador2024-01-31info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlhttps://revistadigital.uce.edu.ec/index.php/INGENIO/article/view/549210.29166/ingenio.v7i1.5492INGENIO; Vol. 7 No. 1 (2024): Development, Security and Innovation; 23-46INGENIO; Vol. 7 Núm. 1 (2024): Desarrollo, Seguridad e Innovación; 23-462697-32432588-0829reponame:Revista Ingenioinstname:Universidad Central del Ecuadorinstacron:UCEspahttps://revistadigital.uce.edu.ec/index.php/INGENIO/article/view/5492/8197https://revistadigital.uce.edu.ec/index.php/INGENIO/article/view/5492/8198Derechos de autor 2024 Alejandro Machado Salazar, Enlil Santiago Ganchala Padilla, Jonathan Mauricio Piñarcaja Rivadeneirahttp://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccess2025-07-17T17:30:11Zoai:revistadigital.uce.edu.ec:article/5492Portal de revistashttps://revistadigital.uce.edu.ec/Universidad públicahttps://uce.edu.ec/**Ecuador*2697-32432588-0829opendoar:02025-07-17T17:30:11Revista Ingenio - Universidad Central del Ecuadorfalse |
| spellingShingle | Use of Artificial Neural Networks (ANNs) for the prediction of the Compressive Strength and Modulus of Elasticity of Concrete Machado Salazar, Alejandro redes neuronales artificiales (RNA) neural fitting (nftool) diseño de mezclas resistencia a la compresión (f’c) módulo de elasticidad (Ec) Artificial Neural Networks (RNA) Neural Fitting (nftool) Blend Design compression resistance (f'c) modulus of elasticity (Ec) |
| status_str | publishedVersion |
| title | Use of Artificial Neural Networks (ANNs) for the prediction of the Compressive Strength and Modulus of Elasticity of Concrete |
| title_full | Use of Artificial Neural Networks (ANNs) for the prediction of the Compressive Strength and Modulus of Elasticity of Concrete |
| title_fullStr | Use of Artificial Neural Networks (ANNs) for the prediction of the Compressive Strength and Modulus of Elasticity of Concrete |
| title_full_unstemmed | Use of Artificial Neural Networks (ANNs) for the prediction of the Compressive Strength and Modulus of Elasticity of Concrete |
| title_short | Use of Artificial Neural Networks (ANNs) for the prediction of the Compressive Strength and Modulus of Elasticity of Concrete |
| title_sort | Use of Artificial Neural Networks (ANNs) for the prediction of the Compressive Strength and Modulus of Elasticity of Concrete |
| topic | redes neuronales artificiales (RNA) neural fitting (nftool) diseño de mezclas resistencia a la compresión (f’c) módulo de elasticidad (Ec) Artificial Neural Networks (RNA) Neural Fitting (nftool) Blend Design compression resistance (f'c) modulus of elasticity (Ec) |
| url | https://revistadigital.uce.edu.ec/index.php/INGENIO/article/view/5492 |