The Netherlands boasts an extensive road network that requires meticulous maintenance and preservation. Dutch asphalt pavements are assessed through functional properties such as mix stiffness and resistance to fatigue. However, current testing practice for these properties is intensive in time and resources, leading to the exploration of alternative methods for performance prediction. Recently, Artificial Intelligence (AI) has emerged as a tool for performance prediction in pavement engineering. Despite its potential, the application of AI is constrained by its limited interpretability and inconsistency with known physical laws. To enhance consistency and interpretability in AI predictive models, Physics Informed AI (PIAI) emerges as a promising approach.

This research develops a PIAI framework for physics infusion in pavement performance predictions. This infusion is accomplished through a Physics-Informed Loss Function balancing data and physics components in model training. The data component assures model predictions approximate the targets, whereas the physics component enforces a subset of features to follow a preset physical model. These components are also present during feature selection, where the physical model is used to guide the inclusion of important features in a PIAI model.

Using the developed framework, this research presents two PIAI prediction models based on the NL-LAB datasets. These models infuse homogenization theory and energy dissipation theory to enhance interpretability and consistency in stiffness and fatigue predictions. The results obtained on both models suggest that physics infusion is feasible without compromising prediction accuracy, balancing physical and statistical knowledge when predicting pavement performance. These findings also indicate that the PIAI framework is a promising approach for infusing physics into AI prediction models. Physics infusion can potentially enhance the acceptance and trust of AI within the pavement engineering community. Furthermore, the developed framework has the potential to accelerate pavement performance assessments by reducing the need for extensive material testing. Its flexibility also supports the incorporation of new physical models, fostering innovation and sustainability in pavement engineering.