Limit states and reliability-based design for a non-codified problem of aqueduct buoyancy
Bibliographic record
Abstract
Limit states design methods and engineering judgement have been used to assess buoyancy issues for remediation of the 85 year old Shoal Lake Aqueduct in Manitoba. The study demonstrates how these methods can be applied to non-codified engineering problems. Four separate buoyancy analyses were completed using (i) partial safety factors from the Ontario Highway Bridge Design Code, (ii) project-specific partial safety factors, (iii) Monte Carlo simulation techniques, and (iv) working stress design (WSD) methods. Engineering judgement was required to develop a buoyancy model, interpret data for modeling parameters, and provide meaningful values for parameters that could not be measured. Results from the analyses show that more uniform reliability is provided when measured variability of the model parameters is accounted for. The reliability is not quantifiable when working stress design methods are used. Key words: limit states, probability, non-codified problem, aqueduct, buoyancy.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".