Investigation of significant inputs for pavement rehabilitation design in the Pavement-ME
Bibliographic record
Abstract
The Pavement-ME can design different flexible or rigid pavement rehabilitation scenarios. A set of quantitative and qualitative input variables are considered to characterize the new and existing pavement layers and predict the damage accumulation in these layers. To evaluate the impact of the inputs on the predicted performance for the rehabilitation options, sequential analyses were performed to assess the main and interactive effects of each input. Continuous responses surface models for interactive effects were obtained using artificial neural network. Furthermore, a normalized sensitivity index was used to quantify the significance of main and interactive effects. The following rehabilitation options were included in this paper: hot mix asphalt (HMA) over HMA, HMA over jointed plain concrete pavement (JPCP), HMA over fractured JPCP, and JPCP over JPCP. The results of the study can assist the highway agencies’ efforts to implementing the Pavement-ME, especially when the majority of pavement designs are related to rehabilitation of the existing highways.
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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 | 0.002 | 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".