Bayesian Analysis and Monte Carlo Simulation for Airport Pavement ACR-PCR Rating

The maintenance of airport pavements has long been a major focus of academic research. To better understand the relationship between wheel loads and pavement bearing capacity, the International Civil Aviation Organization introduced the aircraft classification rating–pavement classification rating (ACR–PCR) system. Owing to its greater flexibility and adaptability, this system is gradually replacing the traditional aircraft classification number–pavement classification number method. This study employs Monte Carlo Gibbs sampling for a Bayesian analysis of PCR values in flexible runways and uses a multiple linear regression (MLR) model to quantify the influence of layer thickness and modulus on PCR. The analysis is based on construction records of flexible pavements, including statistical data on layer thicknesses and modulus. The study compares Bayesian parameter estimates with MLR results to verify model accuracy. Based on these analyses, engineering recommendations for the construction and maintenance of flexible pavements are proposed. The results show that the combination of Bayesian analysis and MLR effectively predicts PCR values for flexible pavements, providing a reliable basis for optimizing pavement design and maintenance strategies.