AASHTO 1993 Plus: an alternative procedure for the calculation of structural asphalt layer coefficients
Bibliographic record
Abstract
Many road agencies worldwide still use the empirical AASHTO 1993 pavement design method. Since the release of the AASHTO 1993 design guide, many aspects of pavement design have changed, including asphalt material properties, traffic, environmental factors, etc. However, many agencies use a single layer coefficient for asphalt mixtures and pavements are currently designed with inaccurate values, as proved by many. This paper presents a four-step procedure (called AASHTO 93 Plus method) to determine structural layer coefficients of asphalt mixtures. The AASHTO 93 Plus method considers viscoelasticity and temperature-dependency of asphalt mixtures to estimate the layer coefficient. Five-hundred-eighteen (518) flexible pavements from USA and Canada roads and 1599 HMAs from the Long-Term Pavement Performance (LTPP) database were analyzed. The results showed that the new material-specific layer coefficients were well above the typical value of 0.44. The total number of 3,726 IRI data records of these 518 pavements were converted to PSI and then compared to those obtained from the traditional AASHTO 93 and AASHTO 93 Plus methods for validation. Results confirmed that the conventional AASHTO 93 method predicts faster deterioration of pavement conditions over time, while the results of AASHTO 93 Plus method better aligned with the field data.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".