Modeling the Creep Compliance of Asphalt Concrete Using the Artificial Neural Network Technique
Bibliographic record
Abstract
The new mechanistic-empirical pavement design guide developed under the NCHRP project 1-37A adopted the creep compliance parameter to characterize the low-temperature behavior of bituminous materials. It is used to predict thermal cracking of roads. However, determination of the creep compliance at three temperatures (–20, –10 and 0°C) involves elaborate laboratory testing and special training of technical staff, a capability that the majority of road jurisdictions in Canada lack today. This paper presents a scheme to estimate the needed parameter by taking advantage of the wealth of field information available from long term pavement performance (LTPP) sites. The proposed technique is based on the use of artificial neural network technique to have a good estimation of the creep compliance of asphalt concrete mixes. Several ANN models were trained and tested using simple parameters collected over the years from LTPP sites. Results of ANN simulations showed the good potential that proposed model has to predict the creep compliance (at different low temperatures) of mixes prepared with different binders. Such a model represents an attractive alternative to testing for small jurisdictions with limited budget and personnel.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".