Elastic Buckling Strengths of Unbraced Steel Frames Subjected to Variable Loadings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it