Experimental Investigation on Self-Centering Steel-Timber Hybrid Beam-Column Connections
Bibliographic record
Abstract
Self-centering heavy timber frames rely on the self-centering beam-column timber connections to limit damage and provide the recentering capability. However, low compressive strength and stiffness perpendicular to the grain of the timber columns have long been a design challenge, which yields low initial post-tensioning forces and possibly a significant loss of post-tensioning forces over the building service life. To address this issue, this paper proposes a new design solution for the self-centering steel-timber hybrid beam-column connections, in which the timber column is replaced by a steel-timber composite column. Cyclic tests were conducted on four beam-column connection specimens with post-tensioning. During the gap-opening, the composite column provided a stiff foundation to the timber beam. The hysteretic curves of all four connections were in a flag shape. Compared with self-centering beam-column timber connections that had the same geometry and material properties, the proposed connection had a larger connection stiffness and an improved efficiency of dissipating energy. After the cyclic loading, the loss of post-tensioning force of the proposed connection was also lower than that of the counterpart timber connections.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".