Hierarchical Bayesian model for joint prediction of runway pavement metrics considering measurement uncertainty
Bibliographic record
Abstract
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 five-fold cross-validation, achieving mean absolute errors 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".