Investigation of shear stiffness and rutting in asphalt concrete mixes
Bibliographic record
Abstract
Field and laboratory testing programs were set up to evaluate the in-situ shear properties of asphalt concrete mixes using the newly developed in-situ shear stiffness testing (InSiSSTTM) facility versus the laboratory evaluation using the resilient modulus and torsion testing. The LTPP SPS-9A 870900 test site, which has six similar pavement sections with different AC surface mix properties, was tested in the field using the InSiSSTTM and core samples were extracted from the site and tested in the laboratory. The results of the testing program were correlated with the rutting of the test sections over a 4-year period. In this paper, the InSiSSTTM facility is briefly introduced and the interpretation of the data collected is presented. The experimental program and analysis procedures are then outlined. The analysis of variance was used to test the significance of the results, and a bivariate analysis was performed for correlating rutting (as a criterion variable) and the different laboratory and field measured material properties (as predictor variables). Finally, a regression analysis between the in-situ shear stiffness and pavement rutting is presented. The results of the study showed that the in-situ shear stiffness had the highest correlation coefficient with rutting rate, and this might be a suitable measure to characterize the asphalt mixes and evaluate the rutting potential of asphalt pavements. This important result should be useful to the pavement engineers interested in the evaluation of rutting using a simple field measure.Key words: in-situ testing, laboratory testing, shear stiffness, shear properties, asphalt concrete, pavements, rutting, long-term performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".