Effect of Asphalt Pavement Compaction on Interlayer Bonding and Tensile Strength of Asphalt Concrete under Low Temperatures and Temperature Cycling
Bibliographic record
Abstract
The maintenance and rehabilitation of highway network systems are major road agency expenditures.The combined effects of environmental conditions, traffic loading, moisture, construction quality, and maintenance contribute significantly to rates of pavement deterioration and the length of asphalt pavement life.One of the important performance parameters for asphalt concrete pavements, particularly in cold regions, is the capability to withstand cyclic temperature changes.Such temperature cycles, especially those that alternate above and below freezing, lead to mechanical stresses that can cause failure.This research presents the results and major findings of an experimental investigation performed on rectangular asphalt slab samples extracted from newly constructed pavements of actual highway projects, rather than gyratory-compacted samples that are created in the laboratory.The first phase (Test Series A) studied the effects of temperature cycles and compaction equipment on interlayer bonds by using a custom-made temperature cycling testing machine that allows the reproduction of several temperature cycles over a relatively short time (7-10 days).The second phase (Test Series B) focused on resistance to tensile stresses under cold temperatures.During Test Series B, a custom-made direct tensile strength test was employed to estimate the tensile strength of slab samples at different temperatures (i.e., 0°C, -10°C, or -20°C) for single-and double-layer samples.Test Series B included 171 asphalt samples 300mm long by 100mm wide, with thicknesses of 50mm for single layers and 100mm for double layers.I would like to express my great appreciation to Professor A.
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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 | 0.000 | 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) | 0.001 | 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".