Seismic Performance Assessment of Wood Light-Frame Shearwalls Using the Performance-Based Unified Procedure
Bibliographic record
Abstract
This study examines the seismic force modification factors related to overstrength and ductility of wood light-frame shearwalls. Several archetypes were developed to meet different design criteria. Nonlinear static procedure, nonlinear time-history analysis, and nonlinear incremental dynamic analysis were performed. The numerical analyses were based on 2D representations of the designed archetypes. The nonlinear static analysis results revealed an overstrength factor of 2.2, which is greater than the current value of 1.7 for wood light-frame shearwalls. The findings also indicated that existing design requirements for hold-downs and the overcapacity requirement for the first and second stories may not always ensure satisfactory performance. The archetype detailed screening step proved valuable as a preliminary assessment prior to the incremental dynamic analysis in identifying critical archetypes. The results of the performance margin ratios indicated that the archetypes marginally met the life safety performance level. Overall, this study suggests a need for potential adjustments in design standards and considerations for a more comprehensive evaluation of the seismic performance of wood light-frame shearwalls. This study also found that adhering to design requirements related to certain irregularities decreased the probability of collapse in those archetypes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".