Predicción de las Resistencias a la Compresión del Concreto con Agregado Fino Parcial de Plástico Utilizando Redes Neuronales Artificiales y Revisiones
DOI:
https://doi.org/10.15332/iteckne.v19i1.2548Palabras clave:
Plástico, agregados finos, resistencia a la compresión, red neuronal artificialResumen
En los últimos años, los desechos plásticos han sido una amenaza para el medio ambiente. La utilización de desechos plásticos como sustitución de agregados finos podría reducir la demanda y los impactos negativos de la extracción de arena al tiempo que aborda los desafíos de los desechos plásticos.
Este estudio tiene como objetivo evaluar modelos de predicción de resistencias a la compresión para concreto con plástico, principalmente plástico reciclado, como reemplazo parcial o adición de agregados finos, mediante el uso de redes neuronales artificiales (ANN), desarrollado en OCTAVE 5.2.0 y conjuntos de datos de revisiones. Se utilizaron 44 conjuntos de datos de 8 fuentes diferentes, que incluían cuatro variables de entrada, a saber: - relación agua: aglutinante; controlar la resistencia a la compresión (MPa); % de reemplazo o aditivo de plástico por peso y tipo de plástico; y la variable de salida fue la resistencia a la compresión del hormigón con agregados plásticos parciales.
Se ejecutaron varios modelos y el modelo seleccionado, con 14 nodos en la capa oculta y 320.000 iteraciones, indicó el error cuadrático medio general (RMSE), el factor de varianza absoluto (R2), el error absoluto medio (MAE) y el error porcentual absoluto medio (MAPE). ) valores de 1,786 MPa, 0,997, 1,329 MPa y 4,44%. Tanto los valores experimentales como los predichos mostraron un% de reducción generalmente creciente de las resistencias a la compresión con el aumento del% de agregado fino plástico.
El modelo mostró errores razonablemente bajos, precisión razonable y buena generalización. El modelo ANN podría utilizarse ampliamente en el modelado de hormigón verde, con áridos finos de plástico de desecho parcial. El estudio recomienda la aplicación de modelos ANNs como posible alternativa para el diseño de mezclas de prueba de concreto verde. Deben fomentarse las técnicas sostenibles, como los superplastificantes de bajo costo a partir de material reciclado y las tecnologías rentables para dimensionar y dar forma adecuada al plástico para la aplicación de agregados finos, a fin de mejorar la resistencia del hormigón con agregados plásticos parciales.
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