Flexural behavior of digitally-fabricated Through-Tenon connections under bending moments
Bibliographic record
Abstract
The implementation of engineered mass timber products in construction and the proliferation of computer numerical control machinery, as a technically feasible and economically justifiable fabrication technique , have given rise to new methods of construction. Inspired by traditional carpentry connections, digital fabrication is harnessed to establish connections between engineered timber panels in freeform systems through interlocking mechanisms, without supplementary metal connectors. This paper characterizes the behavior of digitally-fabricated Through-Tenon connections subjected to bending moments, which are critical in freeform timber plate structures. Six timber specimen groups, made from hardwood Laminated Veneer Lumber , are designed according to material and assembly properties. The moment–rotation behavior of the specimens is documented and evaluated in qualitative (i.e., slip modulus, strength , ductility) and quantitative measures (i.e., damage propagation , failure mode). Overall, the specimens reach their maximum strength soon after yielding occurs regardless of their fiber orientation or assembly pattern. Furthermore, all specimens are classified as having low ductility. The yield and maximum strengths, associated rotations, and joint stiffness depends on the tab assembly vector. For the fiber-perpendicular specimens, the damage modes are independent of the assembly vector. On the other hand, the damage propagation and failure mechanisms strongly depend on the insertion angle for the fiber-parallel specimens.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".