Influence of Temperature on Strength and Elastic Stiffness Behaviours of Ottawa Sand under Triaxial Compression Test
Bibliographic record
Abstract
The aim of this study is to investigate the influence of temperature on the shear strength and elastic stiffness of sand under triaxial compression (TC) test. Air-dried Ottawa sand specimens were prepared to avoid pore pressure induced during shearing. Ottawa sand, widely used in geotechnical engineering research, was selected for these TC tests. The sample was first drained and then heated to different target temperatures (i.e., 30, 45 and 60°C), which were maintained constant during the tests. After heating, the sample was sheared under a constant cell pressure and temperature. Small strain-amplitude cyclic loading was applied successively at different shear stress levels to investigate the elastic Young’s modulus (Eeq) behaviour. The results revealed that the peak shear strength increased with increasing temperature. For Eeq values, a clear relationship with temperature was observed, indicating that elastic stiffness of Ottawa sand also increased with temperature. These findings are significant as they demonstrate that temperature variations can markedly affect the mechanical behaviour of sand, which is important for understanding and predicting the performance of geotechnical structures subjected to thermal effects. Moreover, a sudden drop of stress can be observed as a phenomenon commonly observed in round particle shapes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".