Strength Prediction for Rounded Dovetail Connections Considering Size Effects
Bibliographic record
Abstract
The strength prediction of rounded dovetail connections (RDCs), a relatively new connection for structural timber members, is difficult due to the anisotropic and brittle nature of the material, the complex stress distribution as well as the uncertainties regarding the associated material resistance. Experimental investigations were carried out to provide input and benchmark data for developing a method to predict RDC strength. Numerical analyses confirmed that the experimentally observed failure location was also the highest stressed part of the model. A probabilistic method is presented to predict the strength of RDC. The method, rather than being stress-based, incorporates size effect for the combined action of tension perpendicular to grain and shear parallel to grain stresses in timber by comparing computed stress volume integrals to unit volume strength thresholds. Therefore not only the magnitude of the stress distributions is considered but also the volume over which they act. The capacities of RDC configurations were predicted and successfully validated with experimental tests. The presented strength prediction method has immediate application for the improvement of RDC design.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".