Evolution of Pile Shaft Capacity over Time in Soft Clays (Case Study: Leda Clay)
Bibliographic record
Abstract
This thesis presents a comprehensive experimental investigation to examine the evolution of pile shaft capacity over time.This phenomenon is observed in pile foundations that are driven into soft clays, and is referred to as pile set-up and/or freeze.This research consists of two phases which the first phase investigates the behavior of pile-soil interface over time using a modified direct shear test at two different loading rates of slow (0.05 mm/min.)and fast (2.5 mm/min.).These laboratory tests were used to understand the concept of pile-soil interface in relation with pile set-up.The second phase of this research involved a series of pile load testing which was performed on steel and concrete piles driven into Leda clay in a test site located in southeast of Ottawa region, called the Canadian Geotechnical Research Site No.1.The piles were tested immediately after driving to measure their initial bearing capacities, and were tested repeatedly over different elapsed time to study the change in pile shaft capacity over time.Meanwhile, the excess pore water pressure around the pile was also monitored by a pore water pressure sensor.The average pile capacity measurements for both steel and concrete piles indicated that there is approximately 4.5-5.5 times increase in the pile capacity after 30 days from the initial day depending on the type of the piles used. III Dedicated To My Parents
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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 | 0.001 | 0.000 |
| 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".