Asphalt Multi-Integrated Rollers and Steel Drum Compactors: Evaluating Effect of Compaction on Permeability of Asphalt Pavements
Bibliographic record
Abstract
Theoretical analysis supported by laboratory investigations and verified by field studies and observations has shown that defects caused by rolling compaction are the main contributors to early deterioration of asphalt surfaces. The conventional and widely used steel drum roller induces hairline cracks, which permit moisture to infiltrate pavement structure, causing the phenomenon known as stripping. In addition to inducing cracks, steel drums do not provide the desired uniformity in terms of density across the compacted width of the asphalt mat. Drummed rollers also produce poor compaction at unsupported edges of paved lanes. The asphalt multi-integrated roller (AMIR), an innovative compaction technology, offers a more effective alternative for overcoming problems of steel drum rollers by reducing permeability and, in turn, improving long-term performance of flexible pavements. A multistaged laboratory and field-testing program that measures permeability in terms of hydraulic conductivity was performed on pavement sections constructed using an AMIR side by side with a conventional steel roller. Asphalt concrete layers compacted by steel drum rollers were found, on average, to be up to 20 times more permeable than those compacted by the AMIR immediately after construction and as much as 10 times more permeable after 1 year. The major steps leading to the understanding of how rolling affects the permeability of asphalt layers and, consequently, the long-term performance of newly compacted pavements are discussed and presented.
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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 | 0.001 |
| 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 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".