Time-Dependent Reliability-Based Methodology for Assessing Fracture Toughness Requirements for Highway Bridges
Bibliographic record
Abstract
The provisions for avoidance of brittle fracture in various bridge design codes vary in complexity, from the simple tables in North American codes, which present impact energy requirements as a function of steel grade, climate zone, and member type, to the more involved methods presented in the Eurocodes, which allow factors such as plate thickness, demand-to-capacity ratio, and strain rate to be considered. While these provisions generally appear to be meeting the needs of the code users, two issues are noteworthy. The first is that the North American provisions offer less flexibility and guidance for handling unusual situations than the Eurocode methods. The second is that very few studies can be found in the literature attempting to assess the level of reliability against brittle fracture provided by any of the existing design provisions. The current paper presents a study that attempts to make a first step in addressing both issues, using the Canadian design provisions as an example. Specifically, this paper describes a time-dependent Monte Carlo simulation (MCS)-based probabilistic model and then uses it to assess the extent to which the Canadian provisions provide consistent and adequate levels of reliability against brittle fracture over a range of steel grades, plate thicknesses, and climates. Based on the analysis results, areas of potential improvement of these requirements are identified.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".