Distortional lateral torsional buckling for simply supported beams with web cleats
Bibliographic record
Abstract
Critical moment expressions for steel beams based on elastic lateral torsional buckling as given in classical solutions and present standard provisions are based on assumed fully restrained support conditions and simplifying kinematic assumptions that neglect distortional effects. The present study carefully examines the applicability of such assumptions in the case of simply supported beams with double angle cleat connections. A parametric study based on shell finite element analysis (FEA) is first conducted on steel beams and end connection details of common configurations. It is shown that, throughout buckling: (a) typical cleat angles provide only partial twist restraints to beam ends and (b) beams undergo some distortion not captured in the classical solution, both phenomena resulting in a reduced critical moment capacity. The associated critical moments are then quantified by applying three modifiers to existing design provisions: (1) a partial twist restraint factor based on a potential energy formulation, (2) a distortional coefficient based on regression analysis of FEA results, and (3) a standard dependent factor that ensures consistency in buckling predictions based on various design standards. The modified procedure is shown to yield critical moments that are consistent with those based on FEA buckling simulations. Comparative design examples are then provided to illustrate the merits and applicability of the proposed procedure in practical design situations.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".