Evaluating the performance AASHTOWare’s mechanistic-empirical approach for roller-compacted concrete roadways
Bibliographic record
Abstract
The Federal Highway Administration (FHWA) has recommended the use of AASHTOWare Pavement Mechanistic-Empirical Design (PMED) software for Roller-Compacted Concrete (RCC) pavement design, but specific calibration for RCC is missing. This study investigates the software’s capacity to predict the long-term performance of RCC roadways within the framework of conventional concrete pavement calibration. By reanalyzing existing RCC projects in several U.S. states: Colorado, Arkansas, South Carolina, Texas, and Illinois, the study highlights the need for specific calibration tailored to the unique characteristics of RCC. Field observations have emphasized occurrence of early distresses in RCC pavements, particularly transverse-cracking and joint-related issues. Despite data challenges, the AASHTOWare PMED software exhibits notable correlation between its long-term predictions and actual field performance in RCC roadways. This study stresses that RCC applications with insufficient joint spacing and thickness are prone to premature cracking. To enhance the accuracy of RCC pavement design, it is essential to discuss the inclusion of RCC as a dedicated rigid pavement option in AASHTOWare PMED. This becomes particularly crucial when the rising popularity of RCC roadways in the U.S. and Canada is considered. Such an inclusion would solidify RCC as a viable third option alongside Jointed Plain Concrete Pavements (JPCP) and Continuously Reinforced Concrete Pavements (CRCP) for design and deployment of rigid pavements. The research presents a roadmap for future calibration endeavors and advocates for the integration of RCC pavement as a distinct pavement type within the software. This approach holds promise for achieving more precise RCC pavement design and performance predictions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".