Improving the Reliability of Damage Characteristic Curves in the Simplified Viscoelastic Continuum Damage Model
Bibliographic record
Abstract
One of the major advantages of the cyclic fatigue test (AASHTO TP 107) is that the results can be used to calibrate the Simplified Viscoelastic Continuum Damage (S-VECD) model, which is used for mechanistic pavement performance predictions. The crux of the S-VECD model is the damage characteristic curve, which has been shown to be independent of mode of loading, loading history, and temperature. Consequently, a model can be fitted to the damage characteristic curve and used to predict the damage response for any given loading history of interest using limited test results. AASHTO TP 107 currently lacks a specific procedure for fitting a model to the damage characteristic curve and evaluating the repeatability of test replicates. In this study, a robust and practical method is proposed for fitting a power law model to the damage characteristic curve. The proposed fitting method was verified using cyclic fatigue test results of 19 mixtures sourced from the United States, Canada, and South Korea. In addition, a means to evaluate the specimen-to-specimen variability of damage characteristic curves using a shape factor is proposed. Thresholds for acceptable variability in the shape factor were derived using confidence interval analysis and verified through FlexPAVE™ pavement performance predictions. The findings of this study can be used to improve the reliability of the damage characteristic curves derived from cyclic fatigue tests for pavement performance predictions.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".