Development of a new interconversion tool for hot mix asphalt (HMA) linear viscoelastic functions
Bibliographic record
Abstract
The relaxation modulus E(t), creep compliance D(t), and complex modulus E*(ω) are functions often used to characterize the linear viscoelastic (LVE) behavior of hot mix asphalt (HMA). Interconversions among these LVE functions are often required. To perform an interconversion, one of the key steps is to express both the source and target LVE functions in Prony series representations. To obtain the corresponding Prony series coefficients, the collocation method and linear least squares method were often used in the past. However, the problem encountered with these two methods is in manually assigning part of the Prony series coefficients; resulting in unrealistic or negative Prony coefficients and big discrepancies between the fitting data and the original data. To address this problem, this paper developed a new algorithm by incorporating the Levenberg–Marquardt method. This new algorithm has four unique features, it (1) allows all the Prony series coefficients to be freely adjustable, (2) guarantees all positive Prony series coefficients, (3) determines all Prony series coefficients automatically and simultaneously, and (4) ensures very accurate interconversion through the fact that the fitting curve almost completely coincides with the original curve. Furthermore, to facilitate the implementation of practical applications of this new algorithm, it was incorporated into a stand-alone, windows-based software named “LVEmaster”. The simplicity and accuracy of this new interconversion software was demonstrated through a series of interconversions among HMA LVE functions.
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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".