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Neural network-based optimization of fibres for seismic retrofitting applications of UHPFRC

dc.contributor.authorAbellán García, Joaquín
dc.contributor.authorDSánchez Díaz, Jairo A.
dc.contributor.authorOspina Becerra, Victoria Eugenia
dc.date.accessioned2024-07-05T21:19:53Z
dc.date.available2024-07-05T21:19:53Z
dc.date.issued2021
dc.identifier.issn2116-7214spa
dc.identifier.urihttps://repositorio.escuelaing.edu.co/handle/001/3147
dc.description.abstractUltra-high-performance fibre reinforced concrete (UHPFRC) is an advanced construction material that provides new opportunities in the future of the construction industry around the world. Among those new applications, rehabilitation, and seismic retrofitting of existing damaged or non-ductile concrete structures can be highlighted. The main objective of this paper is to optimise the hybrid blend of fibres that allows a previously optimised eco-friendly ultra-high performance cementitious paste to achieve the ductility requirements for seismic retrofitting applications at lower costs. To meet this goal, two artificial neural network models (ANNs) were created to predict the energy absorption capacity (g) and maximum post-cracking strain (epc). A total of 50 own experimental campaign data added to 550 published works throughout the world data were used for training purposes by using the R-code language. Once the models were trained and validated, a multi-objective optimisation was used to select the combination of fibres that achieved the limit values of g 50kJ/m3 and epc 0.3% considering cost constraints. The experimentally validated results indicated that the adequate blend of high strength steel micro-fibres and hooked end normal strength steel fibres fulfil the threshold values at a lower cost.eng
dc.description.abstractEl hormigón reforzado con fibra de ultra alto rendimiento (UHPFRC) es un material de construcción avanzado que ofrece nuevas oportunidades en el futuro de la industria de la construcción en todo el mundo. Entre esas nuevas aplicaciones se pueden destacar la rehabilitación y la adaptación sísmica de estructuras de hormigón existentes dañadas o no dúctiles. El objetivo principal de este artículo es optimizar la mezcla híbrida de fibras que permite que una pasta cementosa de ultra alto rendimiento, previamente optimizada y ecológica, alcance los requisitos de ductilidad para aplicaciones de modernización sísmica a costos más bajos. Para lograr este objetivo, se crearon dos modelos de redes neuronales artificiales (RNA) para predecir la capacidad de absorción de energía (g) y la deformación máxima post-fisura (epc). Un total de 50 datos de campañas experimentales propias sumados a 550 datos de trabajos publicados en todo el mundo se utilizaron con fines de capacitación mediante el uso del lenguaje R-code. Una vez entrenados y validados los modelos, se utilizó una optimización multiobjetivo para seleccionar la combinación de fibras que alcanzó los valores límite de g 50kJ/m3 y epc 0,3% considerando restricciones de costos. Los resultados validados experimentalmente indicaron que la combinación adecuada de microfibras de acero de alta resistencia y fibras de acero de resistencia normal con extremos en forma de gancho cumple con los valores umbral a un costo menor.spa
dc.format.extent28 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherTaylor & Francisspa
dc.sourcehttps://www.tandfonline.com/action/journalInformation?journalCode=tece20spa
dc.titleNeural network-based optimization of fibres for seismic retrofitting applications of UHPFRCeng
dc.typeArtículo de revistaspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_14cbspa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.contributor.researchgroupCTG - Informáticaspa
dc.identifier.eissn2116-7214spa
dc.identifier.instnameUniversidad Escuela Colombiana de Ingeniería Julio Garavitospa
dc.identifier.reponameRepositorio Digitalspa
dc.identifier.repourlhttps://repositorio.escuelaing.edu.co/spa
dc.publisher.placeReino Unidospa
dc.relation.citationeditionNo. 13 Vol. 26 2022spa
dc.relation.citationendpage6333spa
dc.relation.citationissue13spa
dc.relation.citationstartpage6305spa
dc.relation.citationvolume26spa
dc.relation.ispartofjournalEuropean journal of environmental and civil engineeringeng
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.subject.armarcConstrucción
dc.subject.armarcBuilding
dc.subject.armarcHormigón
dc.subject.armarcConcrete
dc.subject.armarcDiseño sismorresistente
dc.subject.armarcEarthquake resistant design
dc.subject.proposalUHPFRCeng
dc.subject.proposalArtificial neural networkseng
dc.subject.proposalRedes neuronales artificialesspa
dc.subject.proposalDuctilityeng
dc.subject.proposalDuctilidadspa
dc.subject.proposalMultiobjective optimisationeng
dc.subject.proposaloptimización multiobjetivospa
dc.subject.proposalEnergy absorption capacityeng
dc.subject.proposalcapacidad de absorción de energíaspa
dc.subject.proposalseismic retrofitting applicationseng
dc.subject.proposalAplicaciones de modernización sísmicaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa


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