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

SUBMITTER: Li S 

PROVIDER: S-EPMC12692807 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature