Visco-Elastic Portrayal of Bituminous Materials: Artificial Neural Network Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a scheme to circumvent the need for extensive laboratory testing to determine the dynamic modulus of asphalt concrete materials. It calls for using existing complex modulus test results and applying an analytical tool to expand this data. This study presents the artificial neural network (ANN) technique as a promising method that can help designers have a good estimation of the dynamic modulus based on data accumulated over the years. The study highlights the use of ANN method, which utilizes simple physical parameters as input, to predict the dynamic modulus of asphalt concrete. Results of ANN simulations showed the ability of the ANN technique to predict the dynamic modulus of mixes prepared to different air voids and with different gradations and binders. Such a tool represents an attractive alternative to testing for small jurisdictions with limited budget and personnel.
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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 it