Cross‐jurisdictional collaborative deterioration modeling via hierarchical Bayesian transfer learning

Infrastructure performance deterioration models are a critical component in asset management. While many jurisdictions have begun collecting more reliable asset condition data, an effective data‐sharing mechanism is still lacking that enables cross‐jurisdictional knowledge transfer for developing more reliable deterioration models, particularly for jurisdictions with limited or even no historical data. To bridge this gap, this study proposes a parametric transfer learning framework for collaborative deterioration modeling across jurisdictions by integrating a stochastic process model with a hierarchical Bayesian approach. Transfer learning is realized in two aspects to capture both intra‐ and inter‐jurisdictional heterogeneity: by incrementally updating the learned globally shared information when data from a new jurisdiction become available, and by supporting parameter estimation even when some covariates are partially missing. The proposed framework is quantitatively compared with a jurisdiction‐specific modeling strategy in terms of model uncertainty through simulation studies. Furthermore, case studies using a real‐world historical bridge condition database collected from nine jurisdictions with different inventory sizes in Canada are conducted to compare independent and collaborative modeling approaches in two aspects: their ability to capture inter‐jurisdictional heterogeneity and their impact on model uncertainty on lifecycle decision making. Results confirm the effectiveness and significance of the proposed collaborative modeling approach in infrastructure asset management.