Influence of Tile Waste on the Mechanical and Microstructural Properties of Clay-Bentonitic Soil
Bibliographic record
Abstract
Expansive soils constitute a significant problem in geotechnical engineering, as their volume variations cause major structural damage and considerable economic costs.This work aims to evaluate the effectiveness of incorporating crushed tile waste to improve the physical and mechanical properties of a reconstituted swelling soil containing 75% clay and 25% bentonite, in order to reduce its plasticity and swelling potential.Geotechnical laboratory tests were carried out on mixtures containing 0%, 5%, 10%, 20%, and 30% tile waste, including methylene blue Index (MBI), Atterberg limits, normal Proctor test, direct shear, uniaxial compression strength (UCS), and oedometer test.Microstructural analyses (XRD, XRF, EDX, and SEM) were also conducted to characterize the samples.Results show a decrease in MBI from 8.16 to 4.0 and a decrease in Atterberg limits with increasing waste content.The maximum dry density (MDD) reached 1.62 g/cm³ at 5%, and the optimum moisture content (OMC) increased to 25.8% at 10%.The uniaxial compressive strength (UCS) and shear strength are highest at 30%, while the friction angle and cohesion increase with curing time.The addition of 20 to 30% tile waste reduces the swelling index.These findings indicate that the use of tile waste is a promising and sustainable solution for stabilizing expansive soils.
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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) | 0.000 | 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".