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Abstract
Pavement performance prediction is vital for infrastructure management, traditionally relying on destructive testing and empirical models. The emergence of Ground Penetrating Radar (GPR) offers a non-destructive alternative, enabling the integration of spatial data with advanced rheological models for more accurate and localized predictions. This systematic literature review provides a comprehensive overview of the progress in this interdisciplinary field. It categorizes research into key themes: the evolution of rheological modeling of asphalt concrete (from Linear Viscoelastic to Non-Linear Viscoelastic and multi-scale constitutive models), advancements in GPR for pavement characterization (from qualitative assessment to 3D imaging and property estimation), and the integration of GPR data with pavement performance prediction (from empirical correlations to direct rheological model inputs). The review highlights important studies, discusses conflicting viewpoints regarding direct vs. indirect correlations, GPR accuracy, computational complexity, and validation challenges. It identifies critical gaps in current literature, including the lack of generalizable GPR-rheological constitutive relationships, limited multi-scale integration, inadequate environmental factor consideration, insufficient long-term field validation, and absent uncertainty quantification. Finally, it suggests future research directions focusing on developing mechanistic-empirical relationships, multi-physics models, advanced GPR inversion, robust environmental corrections, large-scale validation, uncertainty quantification, physics-informed machine learning, and standardization of protocols. Addressing these areas is crucial for realizing the full potential of integrated GPR-rheological modeling in efficient pavement management.