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

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Abellán García, Joaquín
DSánchez Díaz, Jairo A.
Ospina Becerra, Victoria Eugenia
Abstract (Spanish)
El 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.
Abstract (English)
Ultra-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.
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URI: https://repositorio.escuelaing.edu.co/handle/001/3147
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