ABSTRACT
Accurate pavement performance prediction is critical to any pavement management system. Current deterministic pavement performance prediction models, may not capture the uncertainties in pavement deterioration. This research develops a probabilistic-based model to predict pavement performance and remaining service life in Germany. Data from the German Federal Highway Research Institute (BASt), covers SMA (stone mastic asphalt) and MA (mastic asphalt) pavements, with condition measurements taken every six months from 2011 to 2021. Three pavement distresses: rutting, longitudinal evenness, and cracking are analyzed. This study combines Germany’s deterministic approach and a probabilistic Markov-based model. Physical measurements are standardized into dimensionless condition grades (1: very good to 5: bad). Discrete-time Markov processes are used to predict the condition changes via transition matrices for each pavement type and distress. By integrating these methods, this research improves the accuracy of service life predictions, enabling efficient maintenance planning and infrastructure management.
