Assessment and prediction of flexible pavement damage using traffic speed deflectometer and computational modeling

Traffic Speed Deflectometer (TSD) provides a non-destructive means of evaluating pavement conditions at the network level within a pavement management system. However, the relationship between TSD measurements and pavement mechanical responses, material properties, and long-term performance remains insufficiently understood. This dissertation develops an integrated framework for evaluating and predicting the damage performance of flexible pavements using TSD measurements, combining mechanistic modeling, data-driven analysis, and lifecycle damage simulation.A high-efficiency numerical model based on the Semi-Analytical Finite Element Method (SAFEM) is developed to simulate vehicle–pavement–environment interactions during TSD testing. The model explicitly incorporates viscoelastic pavement behavior, moving vehicular loads, realistic tire–pavement contact stresses, and environmental effects. Validation against full-scale pavement experiments and field measurements demonstrates that the SAFEM-based framework accurately predicts pavement responses while offering significantly higher computational efficiency than conventional finite element models. This computational advantage enables large-scale simulations and supports applications requiring time-dependent analysis.Using the validated SAFEM platform, the key mechanisms affecting TSD measurements are systematically investigated. The effects of temperature, vehicle speed, construction variability, and traffic-induced dynamics on TSD responses are quantified through mechanistic simulations and field data analysis. Based on an extensive SAFEM-generated database, machine learning models are developed to normalize TSD measurements under varying external conditions and to directly relate TSD-derived parameters to critical pavement strains and material properties. Artificial neural network (ANN)–based inverse analysis frameworks are also proposed to estimate equivalent viscoelastic properties of asphalt layers from TSD measurements, enabling network-level material characterization in a non-destructive manner.To extend TSD-based evaluation toward long-term performance prediction, a damage-driven time-homogenization framework is developed by coupling SAFEM with a viscoelastic continuum damage model. This approach efficiently links repeated traffic loading to progressive pavement deterioration while accounting for spatial effects such as traffic wander. The results highlight that ignoring spatial variability in damage accumulation may lead to significant inaccuracies in service life estimation.Overall, this dissertation expands the application of TSD from structural monitoring to an integrated tool for performance evaluation and lifecycle-based pavement management. The proposed framework provides a flexible and scalable foundation for non-destructive assessment and informed decision-making at the network level.