Experimental testing and comparison with analytical methods for glued-in rods in cross-laminated timber
Bibliographic record
Abstract
With the advent of engineered wood products, timber has evolved into a sustainable material for modern innovative structural systems. This resurgence in the application of timber has also spurred the development of innovative connection systems. Among various timber connection methods, glued-in rods (GiRs) have emerged as a modern solution, offering superior load-carrying capacity, aesthetic integration, and suitability for concealed applications. In this study, sixteen GiR connections in cross-laminated timber (CLT) are experimentally tested to evaluate failure modes, load–displacement behavior, and strain distribution along the rod embedment length. These experimental results, combined with data from previously published studies, are used to assess the predictive accuracy and reliability of existing analytical and empirical design equations developed for the GiR connection design. The study highlights the importance of considering both the embedment length and the grain orientation when designing GiR connections in CLT. While most existing design formulations provide reasonable strength estimates, their accuracy decreases at higher embedment lengths. Moreover, variability in failure behavior across studies underscores the complexity of GiR connections. In summary, the findings of this study provide insight into the performance of current design models and contribute to the advancement of reliable methodologies for GiR connections in CLT 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".