Mechanistic Design Framework for Evaluating the Potential for Concrete Pavement Growth and Blowup
Bibliographic record
Abstract
Pavement growth (PG) refers to the expansion of the integrated slab length when all the contraction joints of the concrete pavement are close within the expansion joint (EJ). It results from the complex interactions of numerous factors, such as climatic conditions (extremely high temperature and moisture levels), material, and the quantity of incompressible particles infiltrating the pavement joints structure. Currently, the methods and guidelines available for evaluating the annual PG and predictions of concrete pavement blowup are considerably limited. In this study, a mechanistic design framework was developed for estimating the PG and predicting pavement blowup potential. Moreover, a computer tool called Pavement Growth and Blowup Analysis (PGBA) was developed using MATLAB. This tool considers factors such as the pavement configuration, climatic conditions, configuration of EJs, and design reliability. It was used to evaluate the effectiveness of EJs and predict the blowup occurrence times for concrete pavements. To evaluate the PGBA tool, PG data from field measurements conducted by the Maryland DOT were compared with the results obtained using the PGBA tool. The PG predicted by the PGBA tool shows good agreement with the field measurements. However, because concrete pavement blowup data corresponding to PG measurement were not available, a comparison of the PGBA blowup results was not possible. The PGBA tool can be used to predict the service life of EJs and the blowup occurrence time according to the climatic data, pavement structure and materials, joint design, base friction characteristics, and engineering judgment.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".