Performance Prediction and Process Optimization of Aging-Resistant Rubber-Modified Asphalt via Enhanced BP Neural Network and Multi-Objective NSGA-II.

Abstract

The complex nonlinear interplay between preparation parameters and macroscopic properties poses challenges for predicting the performance of anti-aging rubber asphalt. To address this, two bio-inspired algorithms-Crested Porcupine Optimizer (CPO) and Dung Beetle Optimizer (DBO)-were integrated with a backpropagation (BP) neural network, forming CPO-BP and DBO-BP hybrid models for multi-target prediction. The CPO-BP model demonstrated superior predictive accuracy, significantly outperforming both the standard BP and DBO-BP models, which is attributed to its adaptive global-local optimization mechanism. Shapley additive explanations (SHAP) analysis identified mixing temperature as the most influential factor, with elevated values enhancing rutting resistance but compromising ductility, while moderate temperatures improved aging resistance. Feature interactions indicated synergistic effects between mixing temperature and shear time, and a strong coupling effect between rubber content and temperature on low-temperature performance. Parameter optimization via Non-dominated Sorting Genetic Algorithm II (NSGA-II) further enhanced high-low temperature stability and aging resistance, confirmed by Atomic Force Microscopy (AFM)-based microstructural characterization. The proposed approach provides a robust framework that integrates data-driven prediction and multi-objective optimization for the rational design of high-performance anti-aging rubber asphalt.