SEISMIC FRAGILITY OF A NEW MASS TIMBER-STEEL HYBRID BUILDING SYSTEM EQUIPPED WITH CLT FLOOR DIAPHRAGMS
Bibliographic record
Abstract
An essential step in analysing a Seismic Force-Resisting System (SFRS) is to ascertain how lateral loads distribute within the structure in-between horizontal systems, such as roof and floor diaphragms, and vertical members, such as shear walls and bracings.Clear identification of load paths requires a proper assessment of the in-plane stiffness of floor diaphragms.Nevertheless, in the context of designing SFRS with diaphragms made of wood, neither comprehensive code provisions nor accurate computational methods exist to account for its in-plane flexibility.This shortage of knowledge becomes even more obvious if compared to the common reinforced concrete flooring systems.To investigate influences of the actual in-plane stiffness of diaphragms on the global response of the SFRS, this research performs incremental dynamic analysis (IDA) on a new mass timber-steel hybrid building system via the OpenSees platform and compares its collapse fragility to that of an ideal building model with rigid diaphragms.The SFRS of hybrid building entails concentrically X-braced steel frames, whose nonlinear responses are explicitly simulated, including global buckling, tensile yielding, and post-buckling behaviours.Overall, the fragility analysis concluded that the adoption of the proposed hybrid floor system, with the reinforcement of panel-to-panel connections that contributes to sufficient in-plane stiffness, can facilitate a comparable seismic performance as the building with the fully rigid diaphragm.
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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 | 0.000 | 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) | 0.001 | 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".