NUMERICAL STUDY ON PERFORMANCE OF PRECAST SHEAR WALL CONNECTION UNDER SHEAR-TENSION INTERACTION LOAD
Bibliographic record
Abstract
Precast concrete shear walls have attracted many engineers and researchers because of various advantages, such as enhanced quality control, shortened construction time, and reduced environmental impact compared to conventional construction methods. However, their connection method, which must provide adequate strength, ductility, and structural integrity under various loads, is an essential concern in these systems. In addition, the seismic performance of precast concrete shear wall structures depends on the behavior and continuity of the connections between the walls. This paper investigated an existing vertical connection of precast shear walls in Canada using welded plates designed for a low seismic zone. The primary purpose is to improve the nonlinear behavior of these types of connections by adding some steel components like end plates and bent rebars. The numerical modeling using DIANA FEA is calibrated based on an experimental study on welded plate connection between two precast concrete panels under shear and tension loads. Then, the pushover curves of the connections are evaluated under pure tension, pure shear, and shear-tension loads to suggest some methods for enhancing structural response, including ultimate capacity, ductility, energy dissipation, and failure mode. The results showed that using end plates and bent rebars in these connections increased the ductility and post-yield deformation. However, the stiffness and strength did not change considerably.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".