Determination of Price Reduction Factors for Density-Deficient Asphalt Pavements
Bibliographic record
Abstract
Abstract This paper presents the research undertaken for the development of price reduction factors for density-deficient asphalt pavements. Performance characteristics included in this study are fatigue cracking and rutting. The following laboratory tests were performed on two North Carolina Superpave mixtures with varying air void contents: (1) axial compression dynamic modulus tests for modulus determination; (2) indirect tension tests for fatigue performance evaluation; (3) triaxial repeated load permanent deformation tests for rutting evaluation; and (4) accelerated pavement tests on laboratory pavement slabs for fatigue and rutting evaluation using the third-scale Model Mobile Loading Simulator (MMLS3). Air void models for the dynamic modulus, fatigue cracking, and rutting were developed using the laboratory test data. These models and the results from the MMLS3 testing were used to develop the price reduction factors for density-deficient asphalt mixtures. In order to determine the effect of deficient density of hot-mix asphalt (HMA) on the performance of asphalt pavement as a system, a computer program called AP4 (Asphalt Pavement Performance Prediction Program) was developed. The algorithm adopted in AP4 for the damage calculation is based on the incremental damage concept and is very similar to that used in the National Cooperative Highway Research Program (NCHRP) 1-37A Mechanistic-Empirical Pavement Design Guide. This program allows the determination of the service life for fatigue cracking and rutting based on the inputs of air void contents in all the HMA layers. Case studies of five density-deficient pavements were conducted, and the price reduction factors were determined.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".