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Properties prediction of environmentally friendly ultra-highperformance concrete using artificial neural networks
dc.contributor.author | Abellán-García, Joaquín | |
dc.contributor.author | Torres Castellanos, Nancy | |
dc.contributor.author | Fernández Gómez, Jaime | |
dc.date.accessioned | 2023-06-08T23:15:59Z | |
dc.date.available | 2023-06-08T23:15:59Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 1964-8189 | spa |
dc.identifier.uri | https://repositorio.escuelaing.edu.co/handle/001/2401 | |
dc.description.abstract | Ultra-high-performance concrete (UHPC) results from the mixture of several constituents, leading to a highly complex material in both, fresh and hardened state. The higher number of constituents, together with a higher number of possible combinations, relative proportioning and characteristics, makes the behavior of this type of concrete more difficult to predict. The objective of the research is to build four analytical models, based on artificial neural networks (ANN), to predict the 1-day, 7-day, and 28-day compressive strengths and slump flow. Recycled glass powder milled to different particle size, fluid catalytic cracking residue (FCC) and different particle size limestone powder was used as partial replacements for Portland cement and silica fume. The ANN models predicted the 1-day, 7-day, and 28-day compressive strengths and slump flow of the test set with prediction error values (RMSE) of 2.400 MPa, 2.638 MPa, 2.064 MPa and 7.245 mm respectively. The results indicated that the developed ANN models are an efficient tool for predicting the slump flow and compressive strengths of UHPC while incorporating silica fume, limestone powder, recycled glass powder and FCC. | eng |
dc.description.abstract | El hormigón de ultra altas prestaciones (UHPC) es el resultado de la mezcla de varios constituyentes, lo que da lugar a un material muy complejo tanto en estado fresco como endurecido. El mayor número de constituyentes, junto con un mayor número de posibles combinaciones, proporciones relativas y características, hace que el comportamiento de este tipo de hormigón sea más difícil de predecir. El objetivo de la investigación es construir cuatro modelos analíticos, basados en redes neuronales artificiales (RNA), para predecir las resistencias a compresión a 1 día, 7 días y 28 días y el flujo de asentamiento. y 28 días. Como sustitutos parciales del cemento Portland y el humo de sílice se utilizaron polvo de vidrio reciclado molido a diferentes tamaños de partícula, residuo de craqueo catalítico fluido (FCC) y polvo de piedra caliza de diferentes tamaños de partícula. Los modelos RNA predijeron las resistencias a la compresión de 1, 7 y 28 días y el flujo de asentamiento del conjunto de pruebas con valores de error de predicción (RMSE) de 2,400 MPa, 2,638 MPa, 2,064 MPa y 7,245 mm respectivamente. Los resultados indicaron que los modelos RNA desarrollados son una herramienta eficaz para predecir el flujo de asentamiento y las resistencias a la compresión de los UHPC al incorporar humo de sílice, polvo de piedra caliza, polvo de vidrio reciclado y FCC. | spa |
dc.format.extent | 25 páginas | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | spa |
dc.publisher | Taylor and Francis | spa |
dc.source | https://www.tandfonline.com/toc/tece20/current | spa |
dc.title | Properties prediction of environmentally friendly ultra-highperformance concrete using artificial neural networks | eng |
dc.type | Artículo de revista | spa |
dc.type.version | info:eu-repo/semantics/publishedVersion | spa |
oaire.accessrights | http://purl.org/coar/access_right/c_14cb | spa |
oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | spa |
dc.contributor.researchgroup | Grupo de Investigación Estructuras y Materiales - Gimeci | spa |
dc.identifier.doi | https://doi.org/10.1080/19648189.2020.1762749 | |
dc.identifier.eissn | 2116-7214 | spa |
dc.identifier.url | https://www.tandfonline.com/doi/abs/10.1080/19648189.2020.1762749 | |
dc.relation.citationendpage | 2343 | spa |
dc.relation.citationissue | 6 | spa |
dc.relation.citationstartpage | 2319 | spa |
dc.relation.citationvolume | 26 | spa |
dc.relation.indexed | N/A | spa |
dc.relation.ispartofjournal | European Journal of Environmental and Civil Engineering | eng |
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dc.rights.accessrights | info:eu-repo/semantics/restrictedAccess | spa |
dc.subject.proposal | UHPC | eng |
dc.subject.proposal | ANN | eng |
dc.subject.proposal | Compressive strength | eng |
dc.subject.proposal | Slump flow | eng |
dc.subject.proposal | SCM | eng |
dc.subject.proposal | Virtual packing density | eng |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
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