Fatigue Testing and Structural Health Monitoring of Retrofitted Web Stiffeners on Steel Highway Bridges
Bibliographic record
Abstract
Numerous steel highway bridges, still in use today, were built during the construction boom between the late 1950s and the late 1970s. Fatigue cracking can be considered a main source of deterioration for these bridges. The largest category of observed fatigue cracks is caused by out-of-plane distortion. The most susceptible locations are those at which transverse structural components (such as diaphragms or cross frames) are framed into longitudinal girders through web stiffeners that are not attached to the flanges. In the current study, a web stiffener detail is fatigue tested under different cyclic loading conditions. As-welded specimens are tested, along with specimens retrofitted by grinding and rewelding, needle peening, or the adhesive bonding of fiber-reinforced polymer attachments. Direct strain and deflection measurements are compared with finite element analysis predictions, and local (hot-spot) stresses are compared with hot-spot stress design curves. A time series–based method for damage detection is also explored for the prediction of fatigue crack depth with strain data. The method is validated through the use of small- and large-scale specimen strain data. It is found that damage measures based on strains in the vicinity of the critical hot spot are closely correlated with the true crack depth.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".