Numerical analysis of perforated steel fuse in timber-braced frames
Bibliographic record
Abstract
The inherently brittle nature of timber limits its effectiveness in seismic applications, particularly in high-rise structures where ductility is essential for energy dissipation. Although timber-braced frames (TBFs) offer a potential solution, current design standards lack specific provisions for their implementation. Unlike steel bracing, which relies on buckling in compression and yielding in tension to improve system ductility, timber bracing requires well-designed connections that yield at the brace ends to dissipate energy, while the brace itself remains elastic. One practical approach to achieving this involves integrating yielding fuses, such as perforated steel plates, to enhance ductility and localize damage in replaceable components during seismic events. This study investigates the role of perforated steel plates as seismic fuses in TBFs, focusing on flexural yielding mechanisms, particularly through long slot-shaped perforations. A comprehensive numerical parametric study was conducted to assess key factors influencing the performance of these plates. Results indicate that increasing the perforation length to 100 mm improves the ultimate deformation up to 26 mm but reduces load capacity. However, this reduction can be mitigated by increasing the number of link elements. By optimizing both slot length and the number of links, a 50 % increase in ultimate deformation was achieved without compromising load capacity compared to previous studies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".