Analytical study of special girder moment frames using a mixed shear–flexural link element
Bibliographic record
Abstract
The present paper introduces a mixed shear–flexural (VM) link element that is capable of modelling shear yielding, flexural yielding, and their interaction under monotonic and cyclic loadings. The inelastic deformations are modelled using the multisurfaces approach with dissimilar yield surfaces and a stiffness matrix with nonzero off-diagonal components in shear–flexural space. A new kinematic hardening and new non-associated flow rules are employed. A special girder, which has an open web in the middle, is introduced and modelled using the developed VM link element. It is shown that the results of analyses using the VM link element are in good agreement with those from a finite element analysis. Nonlinear dynamic analyses are performed on a benchmark ordinary moment frame (OMF) and its improved versions with special girders. The special girder moment frame (SGMF), which consists of special girders at the lower storeys and ordinary girders at the roof level, has better seismic performance.Key words: mixed shear–flexural (VM) link element, inelastic zone, shear–flexural interaction, cyclic loading, multisurface, special girder, special girder moment frame (SGMF), ordinary moment frame (OMF).
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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 | 0.000 |
| 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 | 0.000 |
| 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".