Bibliographic record
Abstract
C and Z sections are two of the most common cold-formed steel shapes in use today. Accurate prediction of the bending performance of these sections is important for reliable and efficient cold-formed steel structures. Recent analytical work has highlighted discontinuities and inconsistencies in the American Iron and Steel Institute (AISI) and Canadian Standards Association (S136) design provisions for stiffened elements under a stress gradient (i.e., the web of C or Z sections). New methods have been proposed for design, and an interim method has been adopted in the North American Specification (NAS). However, existing tests on C and Z sections do not provide a definitive evaluation of the design expressions, due primarily to incomplete restriction of the distortional buckling mode. Described in this paper is a series of flexural tests with details selected specifically to insure that local buckling is free to form, but distortional buckling and lateral-torsional buckling are restricted. The members selected for the tests provide systematic variation in the web slenderness (h/t) while varying other relevant nondimensional parameters (i.e., h/b,b/t,d/t,d/b). Initial analysis of the completed testing indicates that overall test-to-predicted ratios for AISI, S136, NAS, and the direct strength method are all adequate, but systematic differences are observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".