Evaluating axial compressive capacity of helical piles installed in clay tills
Bibliographic record
Abstract
To support power generation infrastructure in northern Manitoba, 1,100 helical piles were installed. Fifty-seven axial compressive load tests from this work were evaluated for this study. Six of the tests were instrumented with strain gauges to evaluate the contribution from shaft adhesion. Several theoretical methods to predict the ultimate capacity of helical piles have been adopted from common shallow or deep foundations formulas and are investigated. Empirical methods to predict capacity from torque measurements obtained during install are also common and used to compare to other capacities, predicted or measured. Pile load tests are often completed to refine estimates of capacity and many interpretation methods are available to estimate a failure loads from the test results. Several methods are explored and the interpreted capacity is compared to theoretical and empirical methods. Using the theoretical methods, the influence of bearing capacity factors and shear strength of the soil were found to have the largest influence on capacity. Based on the ultimate capacity obtained from load tests and failure criteria, the theoretical methods over predicted capacity in every case. This is attributed to the selection of unrepresentatively high shear strengths, high Nc factors or a combination thereof. The selection of shear strengths is further scrutinized based on variability in testing data and it was found that further conservatism in the selection of mean shear strength data is pertinent. Capacity to torque correlations predicted capacities from 400 kN to 7,000 kN, owing to the large range of KT factors available. From site specific data, an average KT value of 11.9 was back-calculated. Several failure criterion were reviewed and three were used to estimate capacity from load-deflection curves. From this exercise it was noticed that methods which were applicable in most cases underestimated the capacity whereas methods which predicted higher capacities were applicable is less cases. Based on the De Beer failure criteria, a modified method, denoted the Creep limit, was developed which utilizes graphical and mathematical approaches to interpret a failure load. The Creep limit was found to be applicable in more cases while interpreting relatively high capacities.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".