Experimental Investigation and Numerical Modeling of Tongue-and-Groove Plank Wood Decking under the Effects of Concentrated Loads
Bibliographic record
Abstract
Tongue-and-groove plank wood decking is a product that is commonly used in post and beam timber construction to transfer gravity loads on roofs and floors. In 2010, the National Building Code of Canada changed the application area of the specified concentrated roof live loads from 750×750 mm to 200×200 mm. Preliminary analysis showed that the change in the application area of concentrated loads would have a significant impact on the design of decking systems. An experimental program was undertaken at the University of Ottawa’s structural laboratory to better understand the stiffness characteristics of plank decking under concentrated loads. The experimental test program was complimented with a detailed finite-element model in order to predict the behavior of a plank decking system, especially the force transfer between decks through the tongue and groove joint. The study found that under concentrated loads, the stiffness of the decking system increased significantly as more boards were added. The number of boards found to be representative of a system was eight boards. A deflection coefficient of γ=0.4 was found to be appropriate to calculate the deflection for the simple span, two-span continuous, and controlled random layup, under concentrated load on an area of 200 by 200 mm. A finite-element model was created and compared with the experimental results, and it was found to predict the behavior with reasonable 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".