Investigation of Different Methods for Obtaining Asphalt Mixture Creep Compliance for Use in Pavement Mechanistic–Empirical Design Software
Bibliographic record
Abstract
Thermal cracking is a major distress type observed in flexible pavements, especially in regions with cold climates (e.g., northern United States and Canada). The Pavement ME Design Guide software developed under NCHRP Project 1-37A is the pavement analysis and design tool most widely used by state highway agencies for pavement designs. For thermal cracking predictions in flexible pavements, the most important material properties are the creep compliance and indirect tensile strength of an asphalt mixture in Level 1 and Level 2 analyses. Level 3 analyses use material properties and mixture volumetrics to predict creep compliance and indirect tensile strength. This study was part of a comprehensive research effort to characterize asphalt mixtures commonly used in the state of Michigan for implementation of the Pavement ME Design software. The main objective of the study presented in this paper was the investigation of methods of obtaining asphalt mixture creep compliance [D(t)] for use in flexible pavement analysis and design using the Pavement ME Design software. In this study, numerical interconversion between dynamic modulus (|E*|) and the creep compliance of asphalt mixtures with the Prony series method was investigated and validated. Then, the creep compliance of numerous asphalt mixtures was computed from the measured |E*| data. With these data, the creep compliance predictive equation used in Pavement ME Design software was evaluated and locally calibrated. In addition, an analytical model as well as an artificial neural network–based model was developed for improved prediction of D(t) from the mixture volumetrics.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".