Energy-Absorbing Connection for Heavy-Timber Assemblies Subjected to Blast Loads–Concept Development and Application
Bibliographic record
Abstract
This paper presents the procedure for the development of energy-absorbing connections (EACs) in glued-laminated timber (glulam) and cross-laminated timber (CLT) assemblies to resist the effects of blast explosions. A probabilistic approach, which ensures that energy dissipation occurs in the ductile connections prior to brittle flexure failure of the wood element, is presented and discussed. The design and detailing of the EACs was also based on the results from a preliminary numerical investigation. A total of 23 dynamic tests were conducted on full-scale glulam and CLT elements with EACs as boundary connections using a shock tube test apparatus. The behaviors of the assemblies were established and failure modes were characterized based on the observations of damage occurring in the connections and wood member. The results from experimental testing demonstrated that when properly designed, the EACs allow the assemblies to withstand a greater amount of blast energy than typical glulam and CLT connections as well as idealized simply supported end conditions. A two-degree-of-freedom (TDOF) analysis was conducted to model the assemblies through the discretization of the timber and connection components, and the results showed that TDOF modeling is capable of predicting the behavior of the full-scale assemblies with good accuracy.
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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 | 0.001 | 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".