An integrated packing-moisture control approach in bitumen-stabilized materials (BSM) design
Bibliographic record
Abstract
The mechanical performance of bitumen-stabilized materials (BSM) hinges on two interrelated factors, including aggregate packing and moisture control. However, current design practices offer little guidance on how to systematically balance these variables. This study, therefore, proposes an integrated volumetric framework that couples the Bailey packing principles with a liquid-filled-voids (VFL) criterion to identify the optimum aggregate gradation and total water content for mixtures composed entirely of reclaimed asphalt pavement (RAP). Four gradations were manufactured by blending coarse and fine RAP to represent 60 %, 80 %, 90 %, and 100 % of their loose-unit-weight (LUW) packing states. Each blend was stabilized with a 3 % residual bitumen emulsion and total water dosages ranging from 3 % to 6 %. Compaction behaviour was captured through the dry density, voids filled with liquid (VFL), and voids in the mixture (V m ), which were monitored for up to 100 gyrations. In contrast, water loss due to compaction, long-term evaporation, and 56-day indirect tensile strength (ITS) were also assessed. Results indicate that the 80LUW gradation, combined with a total water dosage of 4 %-4.5 %, produced the densest internal structure (V m ≈ 8.4 %) and maintained VFL at the 85 % threshold that prevents liquid seepage. In contrast, finer (60 %LUW) and coarser (90–100 %LUW) gradations exhibited excessive voids at comparable moisture levels. The proposed framework thus offers a practical way for selecting gradation–moisture combinations, reducing experimental repetition and advancing the sustainable use of BSMs.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".