Composite Pavement Systems – A Sustainable Approach for Long-Lasting Concrete Pavements
Bibliographic record
Abstract
This paper summarizes work performed under the Strategic Highway Research Program project R21 on developing MEPDG-compatible mechanistic-empirical design procedures, test methods, and construction guidelines and specifications, for two-lift PCC/PCC composite pavements and HMA/PCC composite pavements. Composite pavements consisting of high quality top layer(s) have been proven in Europe and in the United States to provide long lives with excellent surface characteristics and rapid renewal when needed. The lower PCC layer is a sustainable JPC or CRC pavement that utilizes recycled and lower cost locally available materials, thus reducing the need to haul aggregates over long distances. As part of this research, three full-scale instrumented test sections on the MnROAD mainline roadway (I-94, west of Minneapolis) were constructed in Spring 2010. The sections include one HMA/JPC and two two-lift PCC sections subject to real highway traffic. Accelerated Pavement Tests (APT) using the Heavy Vehicle Simulator (HVS) were used at the University of California Pavement Research Center (UCPRC) at Davis to evaluate various HMA/PCC test sections. These test sections were constructed in Fall 2009. The research also includes evaluation of in-service composite pavement across the U.S., Canada, and Europe. The results from these experiments were used to develop performance models and procedures for designing HMA/PCC and PCC/PCC composite pavements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".