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Comparación de modelación por Inteligencia Artificial y Regresión Multivariable del comportamiento a flexión del UHPFRC
Comparison of Artificial Intelligence and Multivariate Regression in modeling the flexural behavior of UHPFRC
DOI:
https://doi.org/10.15446/dyna.v87n214.86172Palabras clave:
concreto de ultra altas prestaciones, regresión tipo LASSO, ANN, modelación, comportamiento a flexión (es)ultra-high performance concrete, LASSO regression, ANN, modelling, flexural behavior (en)
El estudio presentado tiene como objetivo modelar el comportamiento a flexión de los concretos de ultra alto desempeño reforzados con fibras (UHPFRC), incluyendo límite de proporcionalidad (LOP), módulo de rotura (MOR) y sus deflexiones asociadas δLOP y δMOR, utilizando análisis de regresión multivariable y algoritmos de inteligencia artificial (AI). Se construyeron cuatro modelos redes neuronales artificiales (ANN), uno para cada respuesta, con una capa de entrada y una capa oculta, y cuatro modelos de regresión tipo LASSO (least absolute shrinkage and selection operator). Los resultados demostraron la eficiencia de los modelos, evaluados mediante los estadísticos error absoluto medio (MAE), raíz del error cuadrático medio (RMSE), error de sesgo medio normalizado (NMBE) y coeficiente de determinación (R2). Los modelos de redes neuronales mostraron mayor precisión, con valores de R2 de 0.982, 0.969, 0.978 y 0.978, en la predicción de los parámetros (δLOP, LOP, δMOR y MOR) del comportamiento a flexión de los UHPFRC.
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 δMOR and δLOP, 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 (δLOP, LOP, δMOR and MOR).
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