Improved prediction accuracy for compressive strength of recycled aggregate concrete using optimization-based algorithms and cascade forward neural network

Abstract

This study proposed a data driven approach to predict the compressive strength (CS) of recycled aggregate concrete (RAC) for sustainable construction using an elite single genetic optimization algorithm-based cascade forward neural network (ESGA-CFNN) model. It was applied to 272 RAC samples under different conditions and compositions focusing on key parameters for CS prediction: water-to-cement ratio (WCR), water absorption (WA), recycled coarse aggregate (RCA) density, fine aggregate (FA) density, naturally occurring coarse aggregate (NCA) density and water-to-total material ratio (WTMR). These parameters were used to develop the ESGA-CFNN model which was then evaluated for its performance. To compare the ESGA-CFNN model, two other models were developed and compared: particle swarm optimization-based CFNN (PSO-CFNN) and artificial bee colony-based CFNN (ABC-CFNN). K-fold cross-validation was used during model development to prevent overfitting. Results showed that ESGA-CFNN model performed better with an RMSE (root-mean-squared error) of 1.144, R2 (determination coefficient) of 0.991 and a10-index of 1.000. ABC-CFNN model had an RMSE of 1.434, R2 of 0.987 and a10-index of 0.982 while PSO-CFNN had an RMSE of 1.561, R2 of 0.984 and a10-index of 0.982. Practical validation with 6 RAC samples confirmed the real world applicability of these models. The findings of this study showed that the proposed ESGA-CFNN model is important for quality control in RAC production and optimizing mix designs to achieve required compressive strength to meet standards and reduce cost and increase sustainability in concrete construction. This study introduces a novel hybrid approach combining ESGA-CFNN, PSO-CFNN, and ABC-CFNN algorithms for accurately predicting the compressive strength of RAC. These models outperform traditional methodologies by offering enhanced predictive accuracy and generalization capability, especially in complex, real-world datasets.