The mechanisms underlying long-term shaft resistance enhancement of energy pile in clays
Bibliographic record
Abstract
Although there are several studies indicating that heating increases the long-term shaft resistance of energy piles, the mechanisms by which heating causes this increase have not been adequately evaluated yet. This article presents a comprehensive analysis and discussion to assess the important factors contributing to this increase by integrating the findings from three recently published papers studying the thermo-mechanical behavior of clay and the clay–pile interface. In these three studies, reconstituted kaolin clay was used, and cyclic and monotonic heat ranging between 24 and 34 °C were applied to the clay and interface. The interface was sheared under two stiffness boundary conditions: constant normal stiffness (CNS) and constant normal load (CNL), where normal stresses varied between 100 and 300 kPa. The analysis presented in this article reveals that the increase in strength of the interface under the CNL condition is primarily attributed to clay stiffening at the interface. However, the increase in shaft resistance under the CNS condition is primarily attributed to the heating-induced increase of effective lateral stress, although clay stiffening at the interface also partially contributes to the total increase of shaft resistance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".