Calibrating MEPDG inputs prediction models for asphalt mixes containing reclaimed asphalt pavement
Bibliographic record
Abstract
In this thesis, the pavement sustainability practices were implemented by using recycled asphalt shingles (RAS) and reclaimed asphalt pavement (RAP) in asphalt pavements. Laboratory performance of mixes containing RAS and RAP were evaluated and characterized for a cold climate such as Manitoba, Canada. In addition, pavement sustainability practices were implemented by generating a database of measured values from laboratory test results to develop and perform local calibration alternatives on dynamic modulus and creep compliance predictive models used in Pavement ME Design software, and to assess the impact of locally calibrated MEPDG models on long-term performance of mixes. Laboratory results showed that 15% RAP can be used in an asphalt mix without changing the virgin asphalt binder grade when the design binder is PG 58-28. It was found that the globally calibrated MEPDG creep compliance and dynamic modulus models are not able to accurately predict values, particularly for mixes used in cold climates, in part because these mixes constituted only a small fraction of the mixes used to develop these models. It was found that the nonlinear multiple regression is the preferred technique for local calibration of NCHRP 1-37A and NCHRP 1-40D E* models. It was noted that the existence of high RAP mixes in calibration of the E* predictive model causes an adverse effect on the reliability of calibrated models. In addition, it was found that nonlinear regression and Artificial Neural Network (ANN) models can be used as two alternatives to reliably predict creep compliance values. Results of the predicted distresses of mixes containing RAP using MEPDG software for Manitoba default Level 3, Manitoba calibrated Level 3, and Manitoba Level 1 demonstrated that the calibrated Level 3 Manitoba asphalt mix input data can be used for the design and analysis of the Manitoba mixes with comparable accuracy of the Manitoba Level 1 input data. As conducting laboratory tests for individual mixes is expensive and time consuming, utilizing locally calibrated reliable models to predict E* and creep compliance can tremendously reduce operating and testing expenses.
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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 | 0.001 |
| 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".