Validación experimental de un modelo de Inteligencia Artificial para la capacidad de absorción de energía del UHPFRC

Publicado

2021-05-20

Validación experimental de un modelo de Inteligencia Artificial para la capacidad de absorción de energía del UHPFRC

Experimental validation of Artificial Intelligence model for the energy absorption capacity of UHPFRC

Palabras clave:

UHPFRC, ensayo de tracción directa, ANN, modelación, capacidad de absorción de energía (es)
UHPFRC, direct tensile test, ANN, modelling, energy absorption capacity (en)

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Autores/as

Joaquín Abellán García Universidad Politecnica de Madrid (UPM) https://orcid.org/0000-0002-0353-322X
Juan Sebastián Guzmán Guzmán Escuela Colombiana de Ingeniería Julio Garavito https://orcid.org/0000-0003-4167-0628
Jairo Alfredo Sánchez Díaz Escuela Colombiana de Ingenieria Julio Garavito https://orcid.org/0000-0002-8069-2662
Julian Santiago Rojas Grillo Escuela Colombiana de Ingenieria Julio Garavito https://orcid.org/0000-0002-4581-0176

Resumen (es)
Resumen (en)

El artículo investiga la eficiencia de las redes neuronales artificiales (ANN) para la predicción de la capacidad de absorción de energía (g) del concreto de ultra-altas-prestaciones reforzado con fibras (UHPFRC) sometido a tracción directa. Para mejorar el modelo, se dividieron los datos en datos de entrenamiento y testeo. La red se ajustó usando validación k-fold con los datos de entrenamiento y se evaluó con los datos de testeo. El modelo permitió considerar UHPFRC reforzado con una fibra o con mezcla híbrida de dos fibras, de una amplia gama de fibras, tales como fibras de acero rectas, fibras de acero acabadas en gancho, fibras de acero retorcidas, fibras de PVA, fibras de polietileno y fibras de polipropileno. Adicionalmente se realizó una validación experimental de la red. Los resultados demostraron la eficiencia del modelo de acuerdo con los parámetros estadísticos utilizados, así como su precisión y versatilidad para tratar datos nuevos.

This paper investigates the performance of an artificial neural network (ANN) model in predicting the energy absorption capacity (g) of ultra-high-performance fiber reinforced concrete (UHPFRC) under direct tensile test. To avoid overfitting a data division into test and training datasets was carried out. Thereafter the neural networks were trained on the training dataset by using k-fold validation and the result model was evaluated on the test dataset. The model was capable of consider one-fiber or hybrid-two-fibers-blend as reinforced UHPFRC, of a wide range of fibers such as straight steel fibers, hooked end steel fibers, twisted steel fibers, PVA fibers, polyethylene fibers and polypropylene fibers. Experimental works were performed to validate the accuracy of the model on real data. The results demonstrated the efficiency of the model, according to the statistical parameters used for their evaluation, the accuracy and the versatility of the model when new data in considered.

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