Shear Behavior of Corrugated Web Bridge Girders
Bibliographic record
Abstract
Results from many shear strength tests conducted on steel I-girder specimens with corrugated webs are available in the literature wherein both local and global shear buckling modes have been observed. A systematic analysis of these data has revealed that previously proposed equations based on plate buckling theories can overestimate the shear strength of corrugated webs by a considerable margin. The results of finite element analyses conducted as part of this investigation suggest that the strength is overestimated, at least in part, because of the sensitivity of the shear behavior to the presence of initial imperfections in the web. Shear tests reported in the literature were conducted primarily on relatively small-scale specimens with dimensions and web thicknesses substantially smaller than would be used in actual bridge girders. Therefore, two full-scale corrugated web girders made of HPS 485W steel were tested. The shear strength and failure mode of the girders are reported and the effect of web initial geometric imperfections is assessed through measurements of the out-of-plane displacements of the web. Since web imperfections due to the fabrication process, as well as residual stresses and material nonlinearities, are expected to be present in varying degrees, a lower bound equation is proposed for design that accounts for both local and global buckling of the web in the elastic and inelastic domains.
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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 | 0.001 |
| 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) | 0.002 | 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".