Shear Strength Assessment of Moist Sands Using Direct Shear Tests
Bibliographic record
Abstract
Abstract Granular materials are commonly used to backfill buried structures due to its free-draining property and higher shearing resistance. Conventional analysis of soil-structure interaction is performed assuming soil parameters based on typical values available in published literature for the standard and natural soils. Engineers often require replacing natural sand with locally manufactured sand as a backfill material for buried structures due to the scarcity of material and environmental considerations. This thesis presents a laboratory investigation of a locally manufactured sand which is classified as well-graded clean sand. Considering the various factors on which the strength parameters of soil depend, a series of direct shear tests are performed with varying density, normal stress, moisture content, shear displacement rate. As the soil used as a backfill for the buried structure is usually moist (unsaturated), the entire test program focuses on investigating the behavior of moist sand. The conventional test apparatus is used in this study as the special apparatus typical used in the research with unsaturated soil is not readily available to the practicing engineer. The study reveals that the conventional test apparatus can reasonably be used to estimate the design parameters for moist sand. For the manufactured sand used in this study, the effect of capillary suction on the shear strength parameters is found to be less significant. While the strength parameters depend on the degree of saturation, these depend extensively on the dry density of the soil with a higher angle of internal friction for the soil with higher dry density.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".