DNN Surrogate-Assisted Multi-Objective Optimization for Inverse Process Design of SBS-Modified Asphalt Gels

Styrene–butadiene–styrene (SBS)-modified asphalt is a typical physical polymer gel whose macroscopic performance is governed by the coupled effects of processing parameters, material composition, and microstructural characteristics. While machine learning has enabled accurate forward prediction of gel properties, a systematic method to inversely determine optimal processing conditions from desired performance targets remains lacking. This study proposes a surrogate-assisted multi-objective optimization framework that bridges forward prediction and inverse design for SBS-modified asphalt gels. A single multi-output deep neural network (DNN) surrogate was trained to predict penetration, softening point, ductility, and viscosity at 135 °C. After correcting the data-splitting procedure, the independent test-set R2 values ranged from approximately 0.981 to 0.994 across the four outputs. NSGA-II was then used to identify scenario-specific Pareto-optimal processing conditions. Three representative Pareto solutions, including the balanced, high-softening-point, and high-ductility solutions, were experimentally validated. Across the 12 prediction–measurement comparisons, the relative deviations ranged from approximately 0.86% to 2.62%. The framework provides a bounded-domain approach for converting forward property prediction into experimentally testable processing recommendations for SBS-modified asphalt gels.