Contribution of Partition Walls in Lateral Load–Resisting Systems of Low-Rise Light Wood Frame Buildings
Bibliographic record
Abstract
Prescriptive design is a common practice in North America for low-rise light wood frame buildings. The prescriptive wood-based shear wall details provided in building codes to resist lateral loads were largely derived based on a combination of historical performance and engineering analysis. Inherent within the historical performance assumption is the presence of certain elements such as internal partitions that contribute to direct load resistance and structural redundancy. This inherent assumption is sometimes challenged by design engineers and proponents of alternative lateral load–resisting systems. In an attempt to showcase the contributions of the partition walls, a number of archetypes with various quantities of partitions were analyzed using a finite-element program. In the finite-element models, the wood-based structural shear walls and partition walls with gypsum wallboard were modeled as macroelements, with mechanical properties obtained from previous tests. Assumptions were made for the diaphragm and connection stiffness values. The results show a significant contribution of the partition walls to the lateral load resistance of light wood frame buildings. Partition walls accounted for 55%–71% of the overall lateral resistance of the evaluated buildings. Furthermore, diaphragms and wall-to-diaphragm connections contribute significantly to the overall behavior of the structures.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".