A fibre-based modelling technique for the seismic analysis of steel–concrete composite shear walls
Bibliographic record
Abstract
Steel–concrete composite shear wall offers a favourable lateral strength and deformation ductility for seismic applications while significantly shortening the project schedule through eliminating the use of formworks and taking advantage of modular construction methodology. This paper presents a fibre-based modelling technique for simulation of the cyclic nonlinear response of composite walls by taking advantage of existing reinforced concrete and steel plate shear wall models. The improved modelling technique for cyclic analysis of composite walls that benefits from the macro models available for steel and concrete shear walls is introduced. The model is validated using experimental test data from 20 wall specimens. A sensitivity analysis is performed to examine the influence of various geometrical and material properties using the proposed modelling technique. A step-by-step modelling recommendation is finally proposed. The results show that the proposed modelling technique can efficiently be used to reproduce the nonlinear cyclic response of composite walls.
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How this classification was reachedexpand
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".