Bayesian Regression in Pavement Deterioration Modeling: Revisiting the AASHO Road Test Rut Depth Model
Bibliographic record
Abstract
Traditional pavement deterioration modeling is normally based on historical condition data alone without incorporating the fundamental relationships between the causal factors and the response. Also, typical approaches do not quantify the uncertainty of the predicted response. This paper uses Bayesian regression for pavement deterioration modeling. This method is applied to an existing model for the prediction of rut depth progression from the AASHO Road Test. A classical regression model developed elsewhere is herein summarized and its results are then compared with those from the Bayesian regression in order to validate. A second model based on the entire dataset of the AASHO road test is used to demonstrate the advantages of such approach. The models are capable of employing expert criteria combined with historical knowledge and current observations in order estimate posterior probabilistic distributions for the regression coefficients of the mechanistic equation. The predictive model calibrated to local conditions is able to forecast within pre-specified confidence intervals the range of values for the expected deterioration. Bayesian regression modeling produces more reliable predictions for deterioration performance, which in turn, can be used to improve decision-making on road management systems
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".