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Machine Learning-Based prediction of flexural strength in recycledaggregate concrete: a K-Nearest Neighbors approach
Predicción de la resistencia a la flexión del concreto con agregadoreciclado mediante aprendizaje automático: un enfoque basado en K-Nearest Neighbors
DOI:
https://doi.org/10.15446/dyna.v93n243.126051Palabras clave:
Recycled Aggregate Concrete, Flexural Strength, Machine Learning, K-Nearest Neighbors, Sustainable Construction, Mix Design Optimization (en)Concreto con Agregado Reciclado, Resistencia a la Flexión, Aprendizaje Automático, K-Nearest Neighbors, Construcción Sostenible, Optimización de Diseño de Mezclas (es)
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Predicting the flexural strength of recycled aggregate concrete (RAC) is challenging because recycled constituents introduce variability and nonlinear interactions. This study develops a K-nearest neighbors (KNN) model to predict 90-day flexural strength using 366 mix designs collected from peer-reviewed studies published between 2010 and 2024. Eighteen mixture, supplementary cementitious material, and chemical-modulus variables were considered. After preprocessing and hyperparameter optimization, the model achieved R2 = 0.812 on the independent test set. SHAP analysis identified the water-to-binder ratio, natural coarse aggregate content, and maximum aggregate size as the most influential predictors. Partial dependence plots further revealed nonlinear effects and interactions among key variables. Most predictions were within ±10% of experimental values. The results show that KNN can support preliminary RAC mixture screening and provide interpretable guidance for optimizing flexural performance while incorporating recycled aggregates.
La predicción de la resistencia a la flexión del concreto con agregado reciclado (RAC) es compleja por la variabilidad de sus constituyentes y sus interacciones no lineales. Este estudio desarrolla un modelo K-nearest neighbors (KNN) para predecir la resistencia a la flexión a 90 días usando 366 diseños de mezcla publicados entre 2010 y 2024. Se consideraron 18 variables de dosificación, materiales cementantes suplementarios y módulos químicos. Tras el preprocesamiento y la optimización de hiperparámetros, el modelo alcanzó R2 = 0.812 en el conjunto de prueba. El análisis SHAP identificó la relación agua/material cementante, el contenido de agregado grueso natural y el tamaño máximo del agregado como los predictores más influyentes. La mayoría de las predicciones quedó dentro de ±10 % de los valores experimentales. Los resultados muestran que KNN puede apoyar la selección y optimización preliminar de mezclas RAC.
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