Effect of Bentonite Permeation on the Fabric and Strength of Ottawa Sand
Bibliographic record
Abstract
Soil fabric controls the mechanical response of the soil through the interlocking forces between its particles. This is quantified using small strain stiffness modulus Gmax, which is an important soil property that provides an understanding about the elastic behavior of the soil and its response to dynamic vibrations, such as earthquakes. Maintaining Gmax is as important as increasing the cyclic resistance in any proposed mitigation measure against soil liquefaction. Previous studies have shown the effective use of bentonite in loose Ottawa sand samples for liquefaction mitigation using dry-mixing and permeation sample preparation techniques. However, resonant column tests on bentonite-treated dry mixed Ottawa Sand samples showed a decrease of the small strain stiffness modulus (Gmax) and thus a decrease in the shear strength of the treated soil compared to that of clean Ottawa Sand samples. This work investigated the effect of permeation on the small-strain stiffness of bentonite-treated loose Ottawa sand samples. Static triaxial tests and bender element tests on these samples showed that bentonite-treated sand samples at least maintained the same shear strength and small-strain stiffness modulus as those of clean sand samples. This work proves the effectiveness of the use of the permeation method using bentonite as a non-disruptive liquefaction mitigation technique.
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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 | 0.001 |
| 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".