Construction factors affecting as-built roughness of Portland cement concrete pavement construction
Bibliographic record
Abstract
This paper investigates the significant construction factors affecting the as-built roughness of Portland cement concrete (PCC) pavement. The panel data analysis uses as-built roughness measurements and related construction factors for reconstructed, replaced, and resurfaced PCC pavement projects in Wisconsin from 1998 to 2002. Construction factors are divided into two categories in this analysis: (1) pavement characteristics and (2) contractor's quality-based performance. The analysis utilizes the fixed effects and random effects modeling techniques to identify the significant variables in the model. The research shows that the fixed effects model, of all proposed models, provides the best estimate on the basis of Akaike's information criterion (AIC). The results indicate that pavement characteristics and contractor's quality-based past performance significantly affect as-built roughness. The findings also show that geographic locations are strongly significant.Key words: panel (longitudinal) data analysis, as-built roughness, international roughness index (IRI), Akaike's information criterion (AIC), construction factor, Portland cement concrete (PCC) pavement, highway construction.
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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 | 0.000 | 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".