SEISMIC LOSS AND DOWNTIME ASSESSMENT OF CLT SHEAR-WALL AND GLULAM MOMENT-RESISTING FRAME SYSTEM
Bibliographic record
Abstract
Amidst increasing recognition of the sustainable attributes and reduced carbon footprint of timber-based structural systems, dedicated efforts have been directed towards advancing this modern building practice. The CLT shear-wall and glulam moment-resisting frame (CLTW-GMRF) system is a notable addition in the design and evaluation of timber-based construction. Incorporating CLT balloon shear-walls, moment-resisting glulam frames, ductile beam-column joints, and buckling restrained brace hold-downs, this system demonstrates compliance with current building codes. However, recent seismic events have emphasized the need for earthquake-resilient structures that prioritize safety, minimize post-earthquake interventions, and promote usability. There-hence, the research proposed here explores the system resilience of a typical 10-story CLTW-GMRF system. A two-dimensional numerical model of the system is developed in OpenSees, and nonlinear response history analyses (NLTHA) are conducted using ground motions of various intensity levels selected based on the seismicity of Vancouver, British Columbia, Canada. Through the examination of the NLTHA, engineering demand parameters that capture global and local demand, and consequent system damage are determined. Moreover, incremental dynamic analysis is conducted to determine the probability of collapse of the system. Using FEMA P-58 methodology, the probabilistic seismic loss assessment, in terms of repair cost and time, is quantified. Accordingly, the post-earthquake recovery trajectory of the system is established, the resilience index of the system is predicted, and the key outcomes of the study with respect to the performance of its components are presented. Overall, this study contributes to a deeper understanding of the probabilistic seismic performance of the CLTW-GMRF system, providing engineers and researchers with valuable insights into its resiliency and proposing strategies for increased system performance
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".