Performance-Based Capacity Design of Steel Plate Shear Walls. I: Development Principles
Bibliographic record
Abstract
This is Part I of two companion papers on performance-based capacity design of steel plate shear walls. Most previous research has been conducted with the primary aim of maximizing ductility and robustness under severe cyclic loading, without any explicit consideration of the costs of achieving this behavior. This has resulted in onerous capacity design rules in current codes and standards for achieving highly ductile systems, and has effectively discouraged their use in low and moderate seismic regions. These companion papers aim to provide a holistic and sound basis for capacity design to any of three explicit performance levels. In this paper, Part I, two target yield mechanisms associated with the two extreme performance levels (ductile and limited-ductility) are identified and justified, and the capacity design principles applicable to these performance levels are discussed. The limited-ductility mechanism departs from conventional treatment and is established based on finite element simulations and experimental observations. Two complementary new concepts for designing moderately ductile walls are also proposed and verified. Because design is an iterative process, modeling efficiencies for use with the performance-based approach are suggested and validated. Inconsistencies between current capacity design methods for evaluating the demands imposed by the infill plates on the boundary elements and the true infill plate behavior are identified and discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".