Design Expressions for Elastic Lateral Torsional Buckling Capacity of I-Beams Strengthened While under Loading
Bibliographic record
Abstract
Starting from a recently developed variational principle for the lateral torsional buckling analysis of steel I-beams strengthened with cover plates, this study formulated an energy-based solution that quantifies the lateral torsional buckling resistance of simply supported strengthened I-beams. The solution captures the detrimental effect of loads that may act on the beam prior to strengthening through an interaction relation combining the prestrengthening and poststrengthening peak moments. Additionally, it captures the effects of moment gradient and load height for pre and poststrengthening loads, as well as prebuckling deformation effects through a series of design-oriented coefficients. A systematic comparison of the solution to finite-element analysis (FEA) predictions demonstrated its accuracy for a wide range of cross sections, strengthening plate geometries, spans, pre and poststrengthening load distributions, and load heights. The use of the proposed solution in typical strengthening design scenarios was illustrated through two examples. The simplicity of the solution compared with the FEA, the universality implied by its dimensionless format, and its predictive accuracy make it attractive in a design environment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".