Adaptive stochastic deterioration modeling for evaluating pavement survey schedules in relation to performance and management cost

Pavement performance impacts transportation mobility, safety, and comfort, with timely maintenance relying on field survey data. However, frequent surveys are costly and impractical. This study examines how survey timing and frequency influence maintenance events and pavement deterioration to optimize management strategies. Using the pavement condition index as a performance indicator, historical data and maintenance records from the long-term pavement performance database were analyzed, and regional deterioration curves were derived using shifting factor methods. The proposed adaptive stochastic deterioration modeling method incorporates survey timing through sequential probabilistic events, capturing the stochastic nature of pavement deterioration. Mixture density networks predicted survey outcome distributions, while Monte Carlo simulations analyzed system behavior. Results show that longer survey intervals increase uncertainty, while uniform schedules reveal local cost minima, highlighting survey frequency ranges of over-survey and under-survey. Non-uniform schedules have been validated to offer potential for greater cost-effectiveness. These contributions provide road agencies with a new perspective for optimizing management strategies.