Cross‐jurisdictional collaborative deterioration modeling via hierarchical Bayesian transfer learning
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".