Geotechnical characteristics of recycled crushed brick blends for pavement sub-base applications
Bibliographic record
Abstract
This paper presents the findings of a laboratory investigation on the characterization of recycled crushed brick when blended with recycled concrete aggregate and crushed rock for pavement sub-base applications. The engineering properties of the crushed brick blends were compared with typical state road authority specifications in Australia for pavement sub-base systems to ascertain the potential use of crushed brick blends in these applications. The experimental programme included particle-size distribution, modified Proctor compaction, particle density, water absorption, California bearing ratio (CBR), Los Angeles abrasion, pH, organic content, and repeated load triaxial tests. Laboratory tests were undertaken on mixtures of 10%, 15%, 20%, 25%, 30%, 40%, and 50% crushed brick blended with recycled concrete aggregate or crushed rock. The research indicates that up to 25% crushed brick could be safely added to recycled concrete aggregate and crushed rock blends in pavement sub-base applications. The repeated load triaxial test results on the blends indicate that the effects of crushed brick content on the mechanical properties in terms of permanent deformation and resilient modulus of both the recycled concrete aggregate and crushed rock blends were marginal compared to the effects on dry density and moisture content.
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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".