Effect of triaxial specimen size on engineering design and analysis
Bibliographic record
Abstract
Background Triaxial tests are often used to determine the behavior and strength characteristics of soils. Specimen size can have a significant impact on the measured shear strength. Accordingly, the selected parameters affect the related geotechnical engineering analysis and design. Methods We tested three different specimen sizes of loose Ottawa sand in triaxial compression tests. The measured shear strength and friction angle are used to explain some of the observed scale effects in engineering design and analysis. Critical state parameter and shear strength from the laboratory tests are employed to assess the static and seismic slope stability of an embankment dam, to calibrate a critical state soil constitutive model, to study the soil behavior under shallow foundations, and to evaluate liquefaction triggering and failure of retaining structures. Results We show that all of these analyses can be significantly affected by the choice of the specimen size used to determine shear strength parameters. Conclusion While using small size samples for determining shear strength parameters might result in un-conservative design, a large sample size is consequently a more accurate representation of soil strength conditions and field deformations.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".