A rolling shear analysis-based method for determining the apparent stiffness and bending capacity of CLT panel under out-of-plane load
Bibliographic record
Abstract
Abstract Most of the currently employed methods, such as Gamma method and shear analogy method, to estimate the bending stiffness and bending capacity of cross-laminated timber (CLT) beams, are computationally extensive. In our previous study, a rolling shear analysis (RSA)-based method, which simplifies the calculation, was developed to determine the shearing capacity of CLT beams. In the present study, the authors expand upon the RSA method to determine the apparent stiffness and bending capacity of 3- and 5-layer CLT beams. By considering the shear deformation of cross layers, simplified formulas to determine the apparent bending stiffness of CLT beam was derived. Two schemes to determine the CLT bending capacity were proposed. One is based on the shear stress analysis, and the other is based on the formula specified in Canadian standard, CSA O86, by replacing the effective stiffness with the apparent stiffness. Test results from the authors and the other researchers were adopted to validate the method. The findings showed that the RSA method, using the apparent stiffness obtained from the proposed method along with the bending capacity formula in CSA O86, can provide a simpler and more reliable estimation of the apparent bending stiffness and bending capacity of CLT beams as compared to the Gamma method and shear analogy method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".