Geotechnical Performance of Recycled Glass-Waste Rock Blends in Footpath Bases
Bibliographic record
Abstract
Laboratory and field experiments were undertaken to investigate the possible application of recycled crushed glass blended with crushed basaltic waste rock as a footpath base material. The laboratory experimental program included basic and specialized geotechnical tests including particle size distribution, modified Proctor compaction, particle density, water absorption, California bearing ratio (CBR), Los Angeles abrasion, pH, organic content, and triaxial shear tests. A field demonstration footpath comprising two sections of recycled glass-waste rock blends with 15% and 30% recycled glass content and a third control section with only waste rock was subsequently constructed on the basis of the outcomes of the initial laboratory tests. Subsequently field tests with a nuclear density gauge and Clegg impact hammer were undertaken, as well as laboratory testing of field samples to assess the geotechnical performance of the trial sections. The field and laboratory test results indicated that adding crushed glass may improve the workability of the crushed waste rock base material but subsequently results in lower shear strength. The blend with 15% glass content was found to be the optimum blend, in which the material presented good workability and also had sufficiently high base strength. Higher recycled glass content (30%) resulted in borderline, though still satisfactory, performance. The research findings indicate that recycled crushed glass in blends with crushed waste rock is a potential alternative material to be used in footpath bases. A separate study is recommended to evaluate the environmental risks associated with the usage of these recycled materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".