Tests and finite element models of wood light‐frame shear walls with openings
Bibliographic record
Abstract
Abstract Shear walls are the primary means by which low and medium rise wood light‐frame buildings resist effects of lateral loads caused by wind, seismic or other events. Traditionally, adequacy of shear walls has been a minor concern because wood light‐frame buildings tended to be small, their shapes were regular, the amount of external and interior walls available to resist lateral forces was considerable, and the number and extent of openings in walls was limited. In modern times however, changes in the range of wood‐based construction products used, changes in construction detailing, increased geometric irregularity of buildings, more open interiors, and more numerous and larger wall openings, has raised concerns about the lateral resistance of wood frame buildings. Shear walls and how they interact with the rest of the system needs to be properly understood. They should be analysed based on generalized engineering principles that are comprehensively validated. This paper addresses proper understanding of wood light‐frame shear walls and engineering models for their analysis. Focus of analysis is on detailed finite element models suitable for representing shear walls with openings. Predictive abilities of models are verified against results of specially designed laboratory tests on shear wall segments containing a window or door opening and different hold‐down construction detailing. There is no intent that finite element models need be adopted by structural designers. Rather, the purpose of such models is that once rigorously verified they provide a benchmark against which the acceptability of simpler ‘design level’ models can be rationally assessed. Very good agreement exists between finite element models presented here and test results for shear walls loaded to destruction, with correct prediction of failure mechanisms for different configurations. Both experiments and modelling clearly demonstrate that both stiffness and strength of shear walls reduces disproportionately in relation to the extent of openings in them. This suggests that neither simplistic conceptualization of how shear walls behave nor simple design practices will lead to solutions that are both economic and safe, across a broad range of situations. However, comparison of test results and model predictions with state‐of‐the‐art design code rules indicates that the code rules quite accurately predict actual strengths of the shear walls tested by the authors. Further work is required to elucidate fully reliable design practices for buildings containing wood light‐frame shear walls. Emphasis within this will need be on the question of how to apportion lateral loads between various shear walls within a complete building.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".