Bibliographic record
Abstract
This paper describes a design procedure proposed to determine the moment capacities for the distortional and local buckling of wide-flange stainless steel sections influenced by flange curling.Experimental tests and theoretical analysis, conducted by the authors, of commercially available wide-flange stainless steel sections in pure bending have shown that flange-curling, where the wide-flange cross-section moves towards the neutral axis, reduces the cross-sectional section modulus, produces nonlinear stress distributions and increases the critical elastic buckling stresses.For the sections investigated, the section modulus is reduced by approximately 6% to 16.9%, while the critical elastic buckling stress is increased by a factor of 1.10 to 3.41.Overall, it was found that flange curling produced a net increase of up to 10.6% for the distortional buckling moment capacity but a net decrease of up to 12.2% for the local buckling moment capacity.Based on this data, it is recommended that the effects of flange curling should be ignored for distortional buckling but that it would be necessary to consider them for local buckling.This paper investigates whether the recently proposed Direct Strength Method (DSM) for the distortional buckling of stainless steel sections developed by Lecce and Rasmussen [1], the Winter curve for local buckling, and the North American Specification for the Design of Cold-Formed Steel Structural Members [2] DSM formulations for cold-formed carbon steel are applicable to wide-flange stainless steel sections in bending.It is concluded that the recently proposed DSM for stainless steel sections in compression can also be used, as presented herein, for wide-flange stainless steel sections in 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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".