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.