Resumen

ABELLAN-GARCIA, Joaquín. Comparison of artificial intelligence and multivariate regression in modeling the flexural behavior of UHPFRC. Dyna rev.fac.nac.minas [online]. 2020, vol.87, n.214, pp.258-267.  Epub 30-Oct-2020. ISSN 0012-7353.  https://doi.org/10.15446/dyna.v87n214.86172.

The study presented aims to model the flexural behavior of ultra-high-performance fiber reinforced concrete (UHPFRC), i.e. limit of proportionality (LOP), modulus of rupture (MOR) and theirs associated deflections δ MOR and δ LOP , using multivariable regression analyses and artificial intelligence (AI) techniques. Four Artificial Neural Network (ANN), one for each response, with an input layer and one hidden layer and four least absolute shrinkage and selection operator (LASSO) regression model were built to yield the most accurate models. The results demonstrated the efficiency of the models, according to the statistical parameters used for their evaluation, i.e., mean absolute error (MAE), root of the mean square error (RMSE), normalized mean bias error (NMBE) and coefficient-coefficient of determination (R2). Neural network models showed the highest precision, with R2 values of 0.982, 0.969, 0.978 and 0.978, in predicting the parameters of flexural behavior of the UHPFRC (δ LOP , LOP, δ MOR and MOR).

Palabras clave : ultra-high-performance concrete; LASSO regression; ANN; modelling; flexural behavior.

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