The Potential of Using Rubberchips as a Soft Clay Stabilizer Enhancing Agent
Bibliographic record
Abstract
Soft clays generally display extremely low yield stresses, high compressibility, low strength, low permeability and consequently low quality for construction. Soil stabilization like soil-cement mixing can be effectively adopted to improve the strength and deformation characteristics of the soft clays. To incorporate a ‘green’ element in the existing stabilization technique, rubber chips derived from waste rubber tyres were used together with cement to stabilized kaolin in the laboratory, exploring the feasibility of the innovative stabilizer. A series of laboratory tests were carried out to study the fundamental mechanical and chemical properties of the cement-rubber chip stabilized kaolin. The mechanical properties examined included bender element and unconfined compressive strength, while the chemical properties included pH values, conductivity and the percentage of oxide concentration. The overall test results indicated that cement is effective in stabilizing the soils, where significant improvement of unconfined compressive strength (qu) and P- and S- wave velocities (vp and vs) were observed. Increasing the percentage of rubber chips alone did not contribute much to strength improvement of the kaolin specimens but are able to increase the percentage of axial strain at failure compared to those specimens without rubber chips. Also, curing time was found to have a significant positive influence on qu, vp and vs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".