Aluminum Waste in Road Pavement Subgrade
Bibliographic record
Abstract
This paper aims to investigate the use of spiral aluminum computer numerical control milling waste (CNC-W) in the construction of road pavement subgrade. The soil (CL) was mixed with CNC-W spirals with ratios of between 0% and 20%, and 5 percent increments by dry weight with different water contents. California Bearing Ratio (CBR), Unconfined Compressive Strength (UCS), and consolidation tests were conducted. The experimental results indicated that the inclusion of CNC-W spirals increased the CBR value of clay up to the 15% mixture ratio, then decreased it. Similarly, the UCS value of clay was increased to the same ratio, whilst the UCS was not able to be determined due to the failing of all specimens with a mixture ratio higher than 15%. The permeability and swelling values, as well as the consolidation characteristics of the mixtures, were defined. The swelling percentages decreased from 1,15 cm/sec to 0,81 cm/sec with an increment in the CNC-W spiral content. A reduction was observed in the coefficient of permeability (k) values up to 15% mixture ratio, whilst it remained constant with change in CNC-W spiral content with a 20% mixture ratio. Coefficient of consolidation demonstrated a similar pattern of behavior to the permeability changes
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".