Bibliographic record
Abstract
As one of the major deep foundation types, driven steel pile (DSP) is widely used in all construction projects in Canada. Especially in rural northern Alberta areas where concrete supply is not accessible in a cost-effective manner, DSP foundation is highly preferred by heavy industrial development such as oil and gas related facilities. For driven steel pile set in the fine-grained soils, significant pile-soil setup (pile capacity gain) is expected due to excessive pore water pressure dissipation after the pile installations. In the field, pile appeared to have a much lower capacity at the end of the installation compared to long-term performance. In a fast-paced construction environment, the time cost to wait and verify the pile long-term capacity is not desired. To proceed the upper structure construction without any delay, a reasonable prediction of DSP setup is required. Extensive research has been conducted to explain the mechanism and magnitude of the pile-soil setup effect. However, very limited study has been done on the rate / time of pore water pressure dissipation in clayey soils. This study is aimed to provide a case study of the pile setup effect of DSP set in Edmonton clay till by using dynamic load testing, and wave equation analysis methods. A finite element numerical model is built to illustrate the pore water pressure dissipation and increase in radial effective stress, and allow geotechnical engineers to assess the pile setup behaviour with available soil testing results and reasonable assumptions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".