Distortion-Induced Fatigue Cracking of Bridge Girders—Design Issues
Bibliographic record
Abstract
Distortion-induced fatigue cracks are common occurrences in older bridges where details were used that are now known to be fatigue prone. These details generally consist of transverse stiffeners cut short of the girder flange to which a diaphragm or cross bracing is connected. The issues related to the assessment of existing bridge with this poor fatigue detail include the evaluation of the remaining fatigue life, the susceptibility of the damaged girders to brittle fracture as the fatigue crack grows, and the repair of these fatigue cracks. A study that included both experimental work and numerical analysis was conducted to assess an existing railway bridge in which over 300 cracks had been detected before it was replaced. The experimental work, which included both field measurements on the bridge before it was taken down and controlled laboratory fatigue tests, was conducted to assess the behavior of the bridge in-situ and the behavior of the web gap. The results of the field work were used to design the laboratory experiment to replicate the field conditions. Finite element analyses provided useful information for the design of the laboratory test program and to evaluate the fatigue susceptibility of web gaps in steel plate girders. Extensive analysis of web gap areas was used to validate simple procedures for the calculation of web gap stresses. This paper presents the results of this analysis and presents an assessment of commonly used web gap stress calculation method.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".