Chemical Stabilization of a Collapsible Soil
Bibliographic record
Abstract
In the event of soils collapsing under a specific load, their volumes can be reduced rapidly and dramatically following wetting.A collapsible soil beneath the foundation of an industrial or residential building can result in irreversible and significant damage to its supporting structures as a result of settlement.Stabilizing soil with chemicals improves its engineering properties by changing its characteristics.The objective of this experimental investigation is the development of a new stabilization method for collapsible soils using a mixture of chemical additives.The new method consists of mixing collapsible soils with a mixture composed of non-metallic by-product material and a non-traditional additive.The chemical additives consist of a mixture of ground granulated blast furnace slag (GGBS), Magnesium Oxide (MgO), and a Geopolymer (with a molar ratio z 3).The results revealed that the new stabilization method is capable to dispose the collapse potential of soils, to reduce considerably soil settlement, and hence to improve soil strength and soil bearing capacity.The superlative of the chemical agents was noted to consist on a mixture of 10% ground granulated blast furnace slag (GGBS), 5% of magnesium oxide (MgO), and 20% of a Geopolymer.The magnesium oxide (MgO) is added or combined to the mix in order to activate the GGBS and consequently to achieve or acquire high performance of the stabilized soil.Furthermore, recycling of waste materials, used in the present stabilization technique, is one of the main ways of preserving the environment with a lower economic value.
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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) | 0.002 | 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".