Recalibration of LRFD Resistance Factors for Driven Steel Piles at End of Drive Conditions in Alberta, Canada
Bibliographic record
Abstract
The geotechnical resistance factor (GRF) is an important parameter used as a part of the implementation of the load and resistance factor design (LRFD) method. This paper presents the improvement of the GRF used in the design procedure for axially loaded driven piles in Alberta, Canada. To obtain this goal, an extensive database of in situ pile load tests, including the results of 28 static load tests (SLT) and 623 pile driving analyzer (PDA) tests was collected from different locations in Alberta. Various known static analysis methods were used for the prediction of pile bearing capacity based on laboratory and in situ geotechnical tests. The GRFs for the static analysis methods were calibrated using a well-known probabilistic technique, called Monte Carlo simulation (MCS). Calibrated GRF values have been recommended for the design of pile bearing capacity based on the soil type, cohesive fine content along the pile length, different empirical methods, and geological region of Alberta. The results showed that regional calibration of GFR based on the local database resulted in higher values of resistance factors than those recommended in national codes, leading to a more accurate and cost-effective design procedure.
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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 | 0.001 |
| 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 | 0.000 |
| 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".