Influence of compaction and paving methods on the interlayer structure and mechanical properties of double-layer bitumen mixtures
Bibliographic record
Abstract
Double-layer bitumen mixtures are increasingly used in pavement construction for their improved structural performance and interlayer bonding. However, the effects of paving and compaction methods on internal structure and mechanical behavior remain insufficiently understood, particularly under environmental loading. Most studies emphasize macroscopic mechanical properties, with limited attention to meso-scale structure-performance relationships. In this study, digital image processing techniques were applied to evaluate the interlayer structure of mixtures produced with four methods: continuous/discontinuous paving combined with single/double-sided compaction. Structural uniformity and continuity were quantified using the particles (pores) and cracks analysis system and image processing and analysis in Java (Image J) software. Mechanical behavior was examined through splitting shear, direct shear, splitting tensile, and uniaxial compression tests under three conditions: room temperature (25 °C), low temperature (−10 °C), and freeze–thaw cycles. Results showed that continuous paving with double-sided compaction substantially improved vertical aggregate distribution, mesostructural uniformity, and interlayer bonding. Compared with discontinuous paving or single-sided compaction, shear strength increased up to 4.0 times, splitting tensile strength 1.8 times, and compressive resilience modulus 15%–20%. Under freeze–thaw cycles, mixtures with weak bonding lost up to 70% strength, while optimized mixtures demonstrated markedly enhanced durability. These findings clarify the role of construction methods in governing mesostructure and mechanical response of double-layer bitumen mixtures, offering practical guidance for reliable design and quality control in pavement engineering.
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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) | 0.001 | 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".