Calibration of reliability-based safety factors for sand boiling in excavations
Bibliographic record
Abstract
This study calibrated the required factors of safety of five analysis methods for sand boiling using reliability theory. The factors of safety computed by the five analysis methods were compared with the results of a series of sand boiling model tests. The comparison shows that rigorous methods (Terzaghi’s and Harza’s methods) were more accurate in predicting the factors of safety compared to the simplified methods (Harr’s, simplified Terzaghi’s, and simplified Harza’s methods). The statistics of the model factor for each method, defined as the actual factor of safety divided by the computed one, was calibrated by the model test results. These statistics were then used to establish the relationship between the target probability of failure and the required factor of safety by reliability theory. Verification using a full-scale sand boiling case history shows that the required factor of safety calibrated by the reliability theory was more reasonable than the required factors of safety in references and design codes.
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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".