Predicting Dynamic Modulus of Florida Hot Mix Asphalt Mixtures
Bibliographic record
Abstract
The dynamic modulus is one of the fundamental properties defining the response of asphalt mixtures in a flexible pavement system. It is an essential input of the Mechanistic-Empirical Pavement Design Guide (MEPDG) software for asphalt pavement design. The dynamic modulus can be evaluated by several existing direct testing procedures, but the tests are difficult and costly, proving complicated for engineers to carry out during the pavement design process. Many studies have been done concerning the predictive model of dynamic modulus. This presentation presents a comprehensive study about developing a predictive model of dynamic modulus for characterizing the asphalt concrete mixtures used in Florida. A laboratory experimental program was developed to evaluate the dynamic modulus of selected Florida Superpave asphalt concrete mixtures. Based on the test results and analyses, a dynamic modulus prediction model was constructed. The results showed that the variables related to aggregate gradation, mix volumetric, percent weight of asphalt content, loading frequency and temperature had influence on the dynamic modulus of the hot mix asphalt mixtures. The predicted results of the developed model were very comparable with the test data. The predictive model could be more suitable for predicting dynamic modulus of the hot mix asphalt mixtures used in Florida.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".