Understanding the effects of temperature on the behaviour of clay
Bibliographic record
Abstract
There is a growing need to better understand the relationship between time, strain rate, and temperature on the load-deformation behaviour of clay soils in engineering applications. These applications may include: infrastructure constructed in northern regions where climate change is a growing concern; disposal of nuclear waste; and, industrial structures, such as furnaces, foundries, and refrigeration plants. Temperature variations may induce changes in internal pressure in the soil, swelling and shrinkage, and affect the mechanical properties of the soil. This thesis presents thermal numerical modeling for two instrumented field sites in northern Manitoba. Thermal conductivity testing on samples from these sites and field data are used to calibrate these thermal numerical models. Various boundary conditions are examined. The capabilities of the models are evaluated to determine if the models adequately simulate and predict changes in temperature in geotechnical structures. A discussion is presented on the strengths and weaknesses in the models and the predictive capabilities of the models. The thesis then shifts into understanding the concepts of thermoplasticity and viscoplasticity and the mathematics relating these concepts. Mathematical models that describe these concepts are examined and compared with traditional soil mechanics approaches. The concepts of thermoplasticity and viscoplasticity are combined in an encompassing elastic thermo-viscoplastic (ETVP) model using a semi-empirical framework. A sensitivity analysis is used to evaluate quantitatively the response of the model. The model is then validated qualitatively against published laboratory data. Applications of the ETVP model are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".