Fragility Assessment of Combined Heavy Timber-Steel Bracing System Through Hybrid Simulation
Bibliographic record
Abstract
This study performs a fragility assessment of a combined heavy timber-steel friction braced framing system through hybrid testing.Hybrid simulation is an innovative testing method that combines analytical modeling with experimental testing utilizing the unique advantages that each has to offer.In a hybrid test a large portion of the structure is modeled analytically while critical structural elements, whose behaviour is expected to be highly nonlinear and thus difficult to capture analytically, are physically tested in the laboratory.In this study, one bottom storey bay of a 7 storey prototype structure is physically tested in the laboratory while the remaining lower bays and the upper 6 storeys are modeled numerically in OpenSees.One hundred and sixty five hybrid tests are performed through incremental dynamic analysis and fragility curves are developed to estimate the probability of damage at varying seismic intensities.The prototype structure is assessed at various performance levels including design and maximum credible hazard levels.The results from the hybrid test are compared with the results from the purely analytical simulation to assess the accuracy of the current finite element modeling techniques.Results show good agreement up to 2.0 percent maximum interstorey drift.The combined heavy-timber steel seismic force resisting system containing friction braces as the primary source of lateral resistance is an effective means of reducing seismic damage to this type of structure.The modeling system exhibits predictable behaviour and experimental results indicate the model is conservative.iii
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".