Enhancing Dynamic Shear Resistance and Efficient Micro-Void Reduction of Expansive Soil Using Activated Nano-Desilicated Fly Ash
Bibliographic record
Abstract
Abstract Excessive swelling and high compressibility of soil have posed countless challenges such as poor dynamic shear strength for engineers dealing with cyclic loading in pavement structure. To address this challenge, the durability and mechanical properties of the investigated subgrade were treated with 10%, 20%, and 30% of activated nano-disilicate fly ash (NDFA) to the dry mass of the subgrade soil. A series of swelling pressure tests, One-dimensional compression tests, and dynamic triaxial (DT) tests were conducted on the samples fabricated using altered moisture content. The findings demonstrated that the swelling pressure and compressibility of the NDFA-treated specimens significantly decreased to an average of 15.3% and 18.4% respectively upon 20% inclusion of NDFA beyond which the swelling stress and compressibility values further decreased signifying the impact of NDFA on the expansive subgrade. The consolidation results also revealed that the treated soil’s consolidation parameters greatly decreased compared to the untreated soil with a high void ratio and compression index ( $${C}_{c}$$ C c ). The dynamic shear resistance of the subgrade soil increased by 36% upon the addition of 10% NDFA content compared to the untreated soil which portrayed a very low resistance against dynamic shear modulus ( $${G}_{max}$$ G max ) with a corresponding high damping ratio. The investigation revealed a strong correlation between $${C}_{c}$$ C c and dynamic shear modulus which is closely correlated to a coefficient determination of 0.99 due to the parameter’s dependency on porosity, particle shape, and stiffness.
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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".