Improving the prediction of the dynamic modulus of fine-graded asphalt concrete mixtures at high temperatures
Bibliographic record
Abstract
Predictive techniques for estimating the asphalt concrete dynamic modulus, |E*|, from mixture volumetric properties are widely used in pavement design and evaluation. The reliability of these techniques is lowest at high service temperatures above 35 °C. Rutting is one of the major distresses in asphalt pavements and is considered highly sensitive to |E*| at high temperatures. The objective of this paper is to improve the reliability in the prediction of |E*| at high temperatures using parameters that reflect the gradation of aggregates. A linear model is proposed to adjust the predicted |E*| for fine-graded mixtures using a new gradation parameter. The analysis was performed on 24 mixtures prepared from various aggregate gradations and types, and several binder grades. Reasonable results were predicted for the calibration data and a validation dataset from the literature. The correlation between |E*| at high temperatures and the gradation parameter improved when it was carried out independently on fine- and coarse-graded mixtures.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".