Elastic Buckling Strengths of Unbraced Steel Frames Subjected to Variable Loadings
Bibliographic record
Abstract
The problem of determining the elastic buckling strengths of unbraced steel frames subjected to variable loadings can be expressed as a pair of maximization and minimization problems with stability constraints based on the concept of storey-based buckling which accounts for the lateral stiffness interaction among columns in a storey while resisting applied loads. The maximization and minimization problems can be solved by either linear programming method or nonlinear programming method depends on whether an approximation on the column stiffness being applied or not. Compared with the nonlinear programming method, the linear programming method based on Taylor series approximation on column stiffness is considerably simpler and more suitable for engineering practice but the frame buckling strengths may be overestimated in some cases, which may result in unconservative designs. In this study, a secant approximation of the column stiffness is introduced. Then, a modified linear programming method based on the secant approximation is proposed. Four unbraced steel frames are investigated to illustrate that the linear programming method in light of the secant approximation can yield conservative results and maintain simplicity.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".