Enhancing energy dissipation in glued-laminated timber assemblies using boundary connections
Bibliographic record
Abstract
Mass-timber assemblies rely on connections and proper yielding hierarchy to provide energy-dissipation and ductility under extreme dynamic loading events. Steel Energy-Absorbing Connections (EAC) have been shown to provide significant energy dissipation when subjected to blast loads . However, whether full-scale behaviour can be estimated entirely based on known performance of individual EAC and glulam beam information, determined from component testing or design provisions, is still unclear. To address this gap in the current knowledge, EACs were tested at the connection-level, and the results were used to investigate a total of eleven full-scale glulam beam specimens , with various EACs as boundary connections. The results of the full-scale testing showed that a proper hierarchy of failure was achieved, where the EACs yielded and deformed completely prior to attaining ultimate failure in the glued-laminated beam, allowing for significant energy-dissipation in the connections and improved ductility. An equivalent composite system-level load-displacement curve based on the behaviour of the EACs and glulam beam in isolation was proposed. The findings indicated that simple analytical techniques can be used to obtain the full-scale behaviour based on information of the EAC and glulam beam in isolation. Analytical methods such as single-degree-of-freedom (SDOF) and two degree-of-freedom (TDOF) analysis in which the dynamic behaviour of the system under blast loading was predicted were validated against experimental results, and the same techniques were used to investigate the potential contribution of the EACs in terms of additional energy dissipation relative to the glulam beam alone. It was found that the EAC-glulam systems achieved, on average, 570 % more energy-dissipation relative to the case where the beam was assumed to be simply supported.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".