Estimating pile shaft capacities by use of direct CPT/CPTu-based methods for continuous flight auger piles in silts and sands
Bibliographic record
Abstract
Cone penetrometer (CPT) and piezometric cone (CPTu) test methods are widely used to estimate pile shaft resistances with depth, but these methods are typically derived from empirical correlations to reference load tests. They may not be applicable to all geological conditions and pile types. Thus, developing correlations for specific geographic areas and pile types can be beneficial. In this study, the accuracy and precision of five applicable direct CPT/CPTu methods were evaluated for continuous flight auger (CFA) piles in predominantly silt and sand soils of the Condie aquifer near Regina, Saskatchewan. Six instrumented CFA test piles were evaluated through a combination of CPTu testing and instrumented static axial load testing to determine reference shaft capacities. The direct CPT/CPTu method determined shaft capacity estimates were compared to refence shaft capacities, and the relative performances of the methods were evaluated using statistical and non-statistical quantitative evaluation criteria. The best performing methods (Modified Unicone and KTRI) were then calibrated by minimizing the square root of the residual sum of squares between the predicted and measured shaft capacities. Once calibrated, the methods were re-evaluated using the initial criteria. It was found that all methods overestimated the unit shaft resistance in the upper 1.5 m, but the calibrated KTRI method provided the best overall fit. The calibrated Modified Unicone method was found to be slightly less accurate but more conservative in its predictions of shaft capacity. These findings can be used to improve pile design in the Condie aquifer region and other areas with similar geological conditions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".