Micro-mechanical behavior of nanosilica-treated high-sulfate soils
Bibliographic record
Abstract
The addition of calcium (Ca)-based stabilizers to sulfate-rich expansive soils is associated with the formation of ettringite, a deleterious reactant that can cause moderate-to-severe swell-related damage to overlying lightweight infrastructures. This research study was conducted to understand the effects of combining nanosilica admixtures with a traditional Ca-based stabilizer to effectively treat high-sulfate soils with an intent to suppress the ettringite formation. Engineering and microstructural studies were thus performed to gain a comprehensive understanding of the behavior of sulfate-bearing soils treated with lime in the presence of amorphous nanosilica. The engineering studies on treated and untreated soils included strength tests before and after capillary soaking, free swell strain tests, and resilient moduli studies that were performed to study and understand the macrostructural behavior of these soils at different curing periods. Supplemental studies using scanning electron microscope imaging and energy dispersive X-ray spectroscopy, thermal analyses using differential scanning calorimetry, and X-ray diffraction studies were also conducted to determine the microstructural changes that occur within these sulfate-rich soils. The results showed that additional silica phases furnished from nanosilica suppressed the precipitation of ettringite and correspondingly increased the formation of cementitious phases. This study also provided ample evidence that the application of amorphous siliceous nanomaterials positively impacts chemical treatments and reduces the precipitation of ettringite in sulfate-rich soils, thus enhancing their engineering performance.
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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".