A physics-informed neural network (DM-PINN-FP): Incorporating cracking preference index for asphalt mixture fatigue prediction

Reliable fatigue prediction is critical to asphalt pavement design. However, most models fail to account for the dual nature of fatigue cracking, which comprises both cohesive and adhesive failure modes. To address this gap, this study presents a Dual Mode Physics-Informed Neural Network Fatigue Prediction (DM-PINN-FP) model tailored for asphalt mixtures. The adhesive failure cracking preference index ( β ) is quantitatively determined through pull-off tests. A Fatigue Cracking Sensitivity Index (FCSI), derived from this cracking preference index, is incorporated as a control parameter within the physics-informed network to represent the effects of aggregate type, temperature, and asphalt film thickness. Grounded in the viscoelastic continuum damage (VECD) theory, the overall damage is decoupled into adhesive and cohesive failure components, leading to coupled evolution equations for dual-mode damage. Building on these mechanisms, a dual-branch physics-informed prediction model is developed, integrating physical constraints and monotonicity conditions within a deep learning framework enhanced by multi-head attention and Long Short-Term Memory (LSTM) networks. Results show that the DM-PINN-FP model successfully mitigates cumulative error issues common in standard neural networks and substantially improves fatigue prediction accuracy. Further analysis reveals that temperature elevation predominately influences the cracking mode and accelerates stiffness degradation. Moreover, the proposed model effectively captures the impacts of different aggregate types and temperature conditions on damage progression. These findings validate the cracking preference index as a valuable link connecting mesoscopic material design parameters with macroscopic fatigue behaviors.