Comparison Of Mechanical Properties Of Clayey Soil Stabilized With GGBS And Flyash Using Geopolymerisation Process
Bibliographic record
Abstract
There has always been a need to stabilize the soil to make it stable, induce strength, and be durable.Physical and chemical methods are followed for this purpose, followed by mechanical means to stabilize the soil.Much research in this regard has been done in the past, but the results were never too satisfactory as it was not possible to entirely alter/convert the soil crystal structure, or in other words, to recrystallize the silicates aluminates and lime present in the soil.Geopolymerisation, a comparatively new technology, comes as an aid towards this problem.Recent research on this process has proven beneficial for stabilization, but most studies have stated their results are based on a change in one or two parameters at a time.The present study has examined at least two parameters in detail that significantly influence the geopolymerisation of soil.These are alkali concentration and binder composition.For a given sodium silicate to sodium hydroxide ratio, i.e., 2.5, compacted soil samples have been prepared at 10% alkali solution by weight of dry soil mass and tested after 7 & 28 days.Water has been used beyond the alkali solution to achieve a maximum dry density of soil mass.It has been found that GGBS is much beneficial in the presence of flyash as their combined synergic effect improves both the gradation and polymerization potential in the soil mixture.About 40% soil substitution is appreciated as more substitution will encourage more alkali requirements which will not be cost-effective.
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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 | 0.001 | 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".