Upscaling Asphalt Performance: A Multiscale Energy Framework and Artificial Neural Network Prediction

The macroscopic resistance of asphalt mixtures to permanent deformation is fundamentally governed by the mechanical properties of the constituent asphalt mortar; however, a unified evaluation system that quantitatively links the energy evolution between these two scales is currently lacking. This study aims to bridge this gap by establishing a multiscale framework to characterize and predict the recoverable and dissipated energy behaviors of asphalt materials. To achieve this, Multi-Stress Creep Recovery (MSCR) tests and Multi-Sequence Repeated Loading (MSRL) tests were conducted on asphalt mortar and mixtures, respectively, to capture energy evolution under varying stress, temperature, and gradation conditions. Subsequently, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were developed to correlate mesoscopic mortar parameters with macroscopic mixture performance. Experimental results reveal that energy indicators are significantly influenced by loading stress and aggregate skeleton, with finer gradations exhibiting greater responsiveness to stress changes. A strong cross-scale dependency was identified, evidenced by a correlation coefficient of 0.86 between the recoverable energy of the mixture (\(U_{(r-mix)}\)) and that of the mortar (\(U_{(r-mortar)}\)). Furthermore, the developed ANN model demonstrated exceptional predictive accuracy (\(R^2 \geq 0.99\)) in upscaling energy indicators. This study develops a multiscale energy framework that integrates experimentally derived energy indicators from asphalt mortar and asphalt mixture, enabling the prediction of macroscopic mixture performance from mesoscopic mortar energy evolution rather than relying solely on empirical machine-learning correlations.