Predicting the Mechanical Properties of Super Large Aggregate Asphalt Mixture from Volumetric Parameters Using Back Propagation Neural Networks

Super-large aggregate asphalt mixtures (SLAM-50) are expected to improve rutting resistance while reducing asphalt demand, but the quantitative relationships between volumetric parameters and key performance indicators remain unclear, limiting performance-oriented mixture design. This study experimentally evaluated the volumetric properties and key performance indices of SLAM-50 across four aggregate gradations and seven asphalt contents. The measured performance indices included compressive strength, splitting strength, compressive resilient modulus, fracture energy, and dynamic stability. With increasing asphalt content, the air voids (VV) decreased, the voids in mineral aggregate (VMA) decreased initially and then increased, and the voids filled with asphalt (VFA) increased monotonically. All performance indices exhibited a non-monotonic trend, increasing first and then decreasing, with a balanced overall performance at 3.0–3.2% asphalt content. Linear regression models showed limited predictive capability (R2 = 0.5944–0.8845). To address this gap, a Backpropagation (BP) neural network was developed using asphalt content, volumetric parameters, mixture density, and gradation type as inputs, and the measured performance indices as outputs. This framework enables simultaneous multi-output prediction and captures the nonlinear, coupled relationships among variables. The model achieved R2 > 0.91 for both training and testing datasets, demonstrating its ability to accurately predict SLAM-50 performance. These findings provide a practical, data-driven basis for performance-oriented mixture design and optimization, addressing the current scientific gap and offering guidance for engineering practice.