Fatigue Reliability Analysis of Steel Girder Bridges
Bibliographic record
Abstract
This study is focused on the fatigue reliability and calibration of the required fatigue design factor to achieve a selected target reliability level. A review of the previous bridge design code calibration analyses shows that the target safety level employed for the calibration of several versions of the design codes are inconsistent. However, the target reliability level employed for the calibration of the CHBDC is set equal to 3.5 for a design life of 75 years. This target reliability level and design life period is adopted in this study. For calibrating the required fatigue design factor to achieve a target reliability level, firstly, a simple equation relating the reliability index to the fatigue design factor is developed. Also, MATLAB scripts were implemented and used to carry out dynamic analysis considering the bridge-pavement-vehicle interactions and to assess the statistics of the stress cycle and stress range. For the analysis, bridge with span ranging from 12 to 36 (m) are considered. Two categories of pavement roughness and different truck speeds are employed as well. The analysis results show that in almost all cases, except for the cases of the bridge with a span of 35.36 (m) and truck speed less than or equal to 100 km/hr, the use of the fatigue design factor of 0.52 is not conservative. This conclusion is based on the consideration that the traffic volume used for the design represents the actual traffic volume. Based on the findings of this study, it is suggested that the fatigue design factor is to be recalibrated for the next edition of the Canadian bridge design code. Also it is recommended that the truck survey task should be extended for many locations including the collections of the statistics of traffic volumes, and that comparison of the in situ measurement and numerical analysis of the stress cycle and range to be conducted to assess the modeling error.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".