EXPERIMENTAL TESTING OF SMALL-SCALE TIMBER-CONCRETE COMPOSITE BEAMS UTILIZING ADHESIVE SHEAR CONNECTIONS
Bibliographic record
Abstract
Timber-concrete composite (TCC) structural elements have shown desirable mechanical characteristics for use in the structural design of mass timber buildings.Most shear connections used in TCC elements are not perfectly rigid.However, a nearly full composite behaviour can be achieved in TCC sections that utilize adhesive for their shear connections, which significantly increases the stiffness and strength of the resulting sections.Due to the costs and logistics of large-scale studies, an experimental program was conducted to test small-scale TCC beams from a black spruce glulam section.Modulus of Elasticity (MOE), Modulus of Rupture (MOR) and compression tests were conducted on two groups of samples.The first group was for small-size beams with no concrete layer on top, that were taken from a glulam beam section, and consisted of three sub-groups: Group A for beams with no glue line present; Group B for beams with one horizontal glue line at mid-depth between two lamellae; and Group C for beams with one vertical glue line at the beam midspan.The second group was for small-size TCC beams that utilized adhesive for their shear connections and consisted of three sub-groups: Group D for TCC beams with 28-day concrete age that utilized wet-on-dry adhesion process; Group E for TCC beams with 7-day concrete age that utilized wet-on-wet adhesion process; and Group F for TCC beams with 28-day concrete age that utilized wet-on-wet adhesion process.Each sub-group consisted of 10 samples for determining the MOE and MOR and another 10 samples for the compression tests.Additionally, Scanning Electron Microscope images were taken for the adhesive lines between the wood lamellae highlighting how a perfectly rigid connection can be developed using adhesive.The results for the glulam beam specimens were found to have a low coefficient of variability and are close to the results published in the available literature.This suggests similar characteristics of glulam made from small diameter trees on both small and large scales.In contrast, the results of the TCC beam specimens were more variable due to the application of two different adhesion methods (i.e., wet-on-wet, and dry-on-wet methods).Also, all test specimens experienced brittle failures.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".