Numerical Modelling Analysis of Angle Bracket Connections Used in Cross Laminated Timber Constructions
Bibliographic record
Abstract
Connections are arguably one of the most critical components controlling the structural performance and failure modes of mass timber structures. Over the last two decades, demands for stronger and energy dissipative connections have been raised with increased application of mass timber products in larger and taller buildings. This paper presents numerical analyses of novel mass timber connections used in cross laminated timber structures. The connections are developed by MyTiCon with BB Stanz- und Umformtechnik GmbH angle bracket. Despite being relatively thin, these angle brackets could show comparable load resistance with thicker ones due to the reinforced web and folded edges. The commercially available finite element software ABAQUS was used to develop three dimensional (3D) numerical models to simulate the performance of angle bracket connections under different load combinations. The modelling analysis involves two phases: (1) to determine the most efficient fastener type and setup for the angle brackets connected to CLT wall and floor panels, and (2) to evaluate the capacity of angle brackets connected to CLT wall and floor panels in various loading scenarios. The findings of this study provide an insight into the behaviour of this new angle bracket connections and will be used in the design of the experimental tests in the next phase.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".