Abstract

ABELLAN-GARCIA, Joaquín; GUZMAN-GUZMAN, Juan Sebastian; SANCHEZ-DIAZ, Jairo Alfredo  and  ROJAS-GRILLO, Julián. Experimental for the validation of Artificial Intelligence model energy absorption capacity of UHPFRC. Dyna rev.fac.nac.minas [online]. 2021, vol.88, n.217, pp.150-159.  Epub Nov 18, 2021. ISSN 0012-7353.  https://doi.org/10.15446/dyna.v88n217.86961.

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.

Keywords : UHPFRC; direct tensile test; ANN; modelling; energy absorption capacity.

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