Evaluation of model uncertainties in reliability-based design of steel H-piles in axial compression
Bibliographic record
Abstract
To account for uncertainties of load and resistance in a more rational way, reliability-based design (RBD) concepts have been increasingly applied to design bridge foundations. One of critical elements in the geotechnical RBD process is the characterization of model uncertainties. This paper compiles 126 and 23 reliable static load tests for steel H-piles in axial compression from two databases: Pile-Load Tests (PILOT) and Deep Foundation Load Test Database (DFLTD), respectively. The Davisson offset limit is adopted to define the measured resistance in clay, sand, and layered soil, which is verified with the L1–L2 method developed for drilled shafts. A hyperbolic model with two parameters is chosen to fit the measured load–settlement curves. The uncertainties in resistance calculations and the load–settlement curves are captured by a ratio (or model factor) of measured to calculated resistance and the hyperbolic parameters. The mean values, coefficients of variation, and the probability distributions of the model factors are established from 149 load tests. The statistics of the resistance model factor are applied to calibrate the resistance factors (for the ultimate limit state) in load and resistance factor design of steel H-piles in axial compression. In future, the statistics of the hyperbolic parameters can be incorporated into the development of RBD of steel H-piles at the serviceability limit state.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".