Nonlinear Mixed-Effects Model for the Evaluation and Prediction of Pavement Deterioration
Bibliographic record
Abstract
Pavement deterioration models are important inputs for pavement management systems (PMS). These models are based on the study of performance data, and they provide the evolution law of pavement deterioration. Performance data consist of observations of pavement section conditions, and are collected through several follow-up campaigns on road networks. To characterize the pavement deterioration process, several statistical methods have been developed at the Laboratoire Central des Ponts et Chaussées (LCPC). However, these methods are suboptimal for modeling the evolution of pavement deterioration, as they ignore unit-specific random effects and potential correlation among repeated measurements. This paper presents a nonlinear mixed-effects model enabling accounting for the correlation between observations on the same pavement section. On the basis of this nonlinear mixed-effects modeling, we investigate and identify structural and climatic factors that explain differences in the parameters between pavement sections, and quantify the impact of these factors on pavement evolution. The proposed model provides a good fit for describing the evolution law of different pavement sections. The performance of this model is assessed using simulated and real data.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".