Reliability-Based Design for Serviceability Limit State of Micropiles in Ontario Soils
Bibliographic record
Abstract
This research is to develop a reliability-based design (RBD) for the serviceability limit state (SLS) of micropiles in Ontario soils according to international and national design codes. A database of 40 static tests conducted on full-scale micropiles is collected and applied in this study. First, a two-parameter hyperbolic model is used to curve fit the load-displacement curves of the micropiles. The hyperbolic parameters are identified through the least squares regression method. Second, the statistical properties including the mean value, standard deviation, and probability distributions of the model parameters are established. Copula theory is implemented to represent the dependence between the model parameters and influencing factors. Third, Monte Carlo simulations are applied to randomly generate the load-displacement curves of micropiles according to the hyperbolic model and the statistical properties of the model parameters. Last, a series of resistance factors are developed for RBD of SLS of micropiles in Ontario soils according to three design codes, including the American Association of State Highway and Transportation Officials, the National Building Code of Canada, and Canadian Highway Bridge Design Code.
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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) | 0.000 | 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 | 0.000 |
| 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".