Investigating structural interactions in one-story segmented CLT shear walls: The role of floors, parapets, and lintels
Bibliographic record
Abstract
Many numerical and experimental investigations have explored the behavior of CLT shear walls under lateral loads; however, there remains a gap in understanding the effect of structural interactions between wall panels and other structural elements, such as floors, parapets, and lintels in case of segmented CLT shear walls subjected to lateral loads. This study investigates the influence of these interactions on the lateral response of single-story multi-panel segmented CLT shear walls. For this purpose, various detailed numerical models that include floors, lintels and parapets are employed. The nonlinear behavior of connections is calibrated against existing test data to validate the accuracy of the finite element models in reproducing the hysteretic behavior of previously tested segmented CLT shear walls. These models are used to analyze various segmented CLT shear walls with different geometries, wall panel aspect ratios, floor panel bending stiffnesses, and vertical stiffnesses of wall-to-floor connections above to explore the key characteristics of their response such as lateral strength, inelastic deformation capacity, yielding hierarchy and failure modes. The results indicated that excluding the contribution of secondary structural elements (i.e., floor panels, parapets, and lintels) to the lateral response of segmented CLT shear walls can lead to overestimating deformation capacity, especially for high-aspect-ratio panels, while underestimating lateral strength and stiffness. These findings offer important insights for the seismic design and detailing of segmented CLT shear walls, highlighting the need to account for these interactions to achieve more reliable and efficient performance in practice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".