Determination of seismic performance factors for cross-laminated timber shear walls based on FEMA P695 methodology
Bibliographic record
Abstract
Cross-laminated timber (CLT) construction has been gaining popularity in North America. However, CLT-based seismic force resisting systems are not recognized in current U.S. design codes, which is among the many challenges preventing widespread adoption of CLT in the United States. The purpose of this study was to investigate the seismic behavior of CLT-based shear wall systems and to determine seismic performance factors, namely, the response modification factor (R factor), the system overstrength factor (), and the deflection amplification factor (C d ), using the FEMA P695 procedure. Nine index buildings including single-family dwellings, multifamily dwellings, and commercial (including mixed use) midrise buildings were developed, from which 72 archetypes were extracted. Testing performed at the component and subassembly levels included connector tests and isolated shear wall tests. A CLT shear wall design method was developed and used to design the archetypes, which were then assessed with nonlinear pushover analysis and incremental dynamic analysis. Based on the required collapse margin, an R factor of 3 is proposed for CLT shear wall systems with 2:1 or mixed aspect ratio panels up to 4:1, and an R factor of 4 is proposed for CLT shear wall systems made up of only 4:1 aspect ratio panels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".