With the advancement of China’s carbon peaking and carbon neutrality targets and the low-carbon upgrading of the construction industry, steel fiber recycled aggregate concrete (SFRAC) has attracted increasing attention as a sustainable construction material due to its advantages in resource recycling and enhanced mechanical performance. However, its compressive strength is influenced by multiple interacting factors, making accurate prediction challenging when using conventional empirical or regression-based methods. To enhance predictive performance, a compressive strength database was established based on published experimental data. The input layer included seven mixture parameters: water content, cement content, fine aggregate content, natural coarse aggregate content, recycled coarse aggregate content, steel fiber content, and superplasticizer dosage, with the 28-day compressive strength serving as the output variable. Using this database, four prediction models were developed, including a back-propagation (BP) neural network and three optimized variants—GA–BP, PSO–BP, and GA–PSO–BP, optimized by genetic algorithm (GA) and particle swarm optimization (PSO)—were developed. Their performance was evaluated using the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Among the four models, GA–PSO–BP produced the best predictive performance, with a best-run R2 of 0.9308 on the validation set, exceeding the BP, GA–BP, and PSO–BP neural networks by 0.0642, 0.0326, and 0.0512, respectively. Over 10 independent runs, it attained an average R2 of 0.8822 and consistently delivered the lowest RMSE and MAE with small standard deviations, confirming its superior predictive accuracy and stability. These findings suggest that integrating GA and PSO can effectively enhance the predictive accuracy and stability of the BP neural network, thereby providing a dependable reference for compressive strength prediction and mix proportion optimization of steel fiber recycled aggregate concrete.
Zhang et al. (Wed,) studied this question.