Using numerical procedures to quantify seismic reliability of wood shear walls and braced frames
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Bibliographic record
Abstract
This article discusses the use of numerical procedures to quantify the probabilistic seismic behaviour of panel-sheathed wood shear walls and diagonally braced frames made with three species including Japanese sugi, Canadian hemlock and European whitewood. Three probabilistic methods were implemented in the analysis, based on the assumption that ground motion records follow a uniform distribution of representing earthquake characteristics. The first method is the traditional one in seismic reliability analysis. The second method calculates the exceeding probability from conditional distributions at given ground motions. The third method is the Monte Carlo simulation method. Confidence curves from two different methods were also used to visualize the comparison among structural configurations and species. The results showed that panel-sheathed walls exhibit better performance than braced frames. The results also show that systems built with hemlock are better than other systems with whitewood or sugi.
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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) | 0.000 | 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) | 0.000 | 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 it