Strength of welded joints under combined shear and out-of-plane bending
Bibliographic record
Abstract
An experimental and analytical research program was conducted with the objective of investigating the response of welded joints loaded under combined out-of-plane bending and shear. A database of test results, including 60 tests from the University of California in Davis, eight tests from an early research program at the University of Alberta in Edmonton, and 24 tests from Université Laval in Ste.-Foy, was used to evaluate several strength prediction models and the current North American design approaches. This work was complemented by a reliability analysis to assess the level of safety provided by these design approaches. It was determined that both the Canadian Institute of Steel Construction (CISC) and the American Institute of Steel Construction (AISC) approaches provide remarkably conservative predictions of the test results, especially for cases where the welded plate thickness is large. Although a modified version of an approach proposed by earlier investigators in 1972 and based on the method of instantaneous centre of rotation provides an accurate prediction of test results, a simpler strength calculation model that does not require an iterative approach is proposed as a substitute for the current design approaches. The proposed approach provides the desired level of safety for the design of welded joints loaded in shear and out-of-plane bending.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".