Elastic Lateral Torsional Buckling of I-Beams Strengthened While under Loading
Bibliographic record
Abstract
The present study investigates the elastic lateral torsional buckling resistance for steel I-beams that are strengthened with steel cover plates while being subjected to loading. A variational principle was developed for the problem by accounting for the full sequence of prestrengthening load application, strengthening process, poststrengthening load application, up to the buckling response. The variational principle was then used to develop a finite-element formulation for the problem leading to a quadratic eigenvalue problem. The formulation successfully captures the effects of prestrengthening loads, the beneficial effects due to prebuckling deformation, and the interactions between both effects. The study documents the benefit of providing transverse stiffeners to maximize the critical moment capacity attained when cover plates are used for strengthening. The study determines the locations of cover plates that would maximize the gain in critical moments in strengthening scenarios where only part of the beam span is to be strengthened. A systematic parametric study was developed to characterize the gain in elastic critical moment strength attained by cover plate strengthening and associated prebuckling deformation effects, in terms of dimensionless parameters that characterize the effects of prestrengthening load magnitude, beam span, cross-sectional geometry, length of cover plates, and cover plate cross section. Other parameters investigated include the distributions of prestrengthening and poststrengthening loading.
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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 | 0.000 | 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".