Hierarchical Bayesian Model for Joint Prediction of Runway Pavement Metrics Considering Measurement Uncertainty

Accurate prediction of runway pavement conditions is critical for aviation safety and maintenance planning. This study presents a Hierarchical Bayesian Joint Model with latent variables to simultaneously forecast key performance metrics—the International Roughness Index (IRI) and Surface Macrotexture Depth (SMTD)—while explicitly accounting for measurement uncertainties. The proposed model incorporates nonlinear quadratic relationships among axial loads, SMTD, and IRI, effectively capturing both direct and indirect load effects. Model performance was rigorously evaluated through a stratified 5-fold cross-validation, achieving mean absolute errors (MAE) as low as 0.98 for IRI and 0.88 for SMTD, outperforming traditional methods by approximately 15%. Posterior diagnostics confirmed robust convergence and accurate uncertainty quantification. Overall, the hierarchical Bayesian model demonstrated superior predictive accuracy, highlighting its practical utility for data-driven pavement management decisions.