Optimizing performance of recycled aggregate materials using BP neural network analysis: A study on permeability and water storage

Abstract

This study investigates the factors influencing the permeability and water storage capabilities of Recycled Graded Crushed Stone Layer (RGCSL) materials, which are crucial for constructing sustainable "sponge cities". The research focuses on how aggregate characteristics, such as particle size and filling sequence, affect the porosity structure of RGCSL and, consequently, its permeability and water storage performance. The findings reveal significant impacts of these factors on material performance, leading to the development of a performance prediction model based on the principle of superposition and backpropagation neural networks. The model's efficacy was validated through simulation experiments, indicating that the water storage capacity of recycled sand is significantly higher than that of coarse aggregates, with the model achieving an accuracy of 89.1%. This study is vital for advancing environmental restoration and sustainable urban development.