Evaluating the performance AASHTOWare’s mechanistic-empirical approach for roller-compacted concrete roadways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.000 |
| 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 it