Recycling of Construction Waste Concrete as a Stabilizer for Gypseous Soils
Bibliographic record
Abstract
This study presents the possibility of recycling Crushed Waste Concrete resulting from the demolition of buildings, and making practical use of these abundantly available materials, by grinding them and adding them in different proportions to gypseous soils to increase their maximum bearing capacity and reduce compressibility. A laboratory model with dimensions (300*300 *600mm) of galvanized steel, 4 mm thick, was used to study the effect of mixing (0%, 2%, 4%, 6%, and 8%) Crushed Waste Concrete with three types of water-flooded natural gypseous soils with different percentages of gypsum (30%, 46%, and 66%). Loading tests were carried on square steel footing (70*70mm) and 9mm thick, placed on these soils. More than 15 tests were conducted on the laboratory model, in addition to the usual classification tests on the soils used in the study. All tests were carried after submerging gypseous soils due 24 hours. The study showed a clear improvement in the susceptibility of the three gypseous soils using all the addition percentages of concrete powder, the best percentage was 8%, while the improvement rates were less using 2%, 4%, and 6%. As the bearing capacity of the soil increased after mixing it with this ratio due to filling the voids formed as a result of melting gypsum during the water immersion process, which compensated for it at this stage. Mixing gypseous soil with crushed waste concrete by 8% increases ultimate bearing stress about 8 times, while it is 2.5 times for model mixed with 2% of this additive, compared with the untreated one.
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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".