Simultaneous measurement of elastic constants of full-size engineered wood-based panels by modal testing
Bibliographic record
Abstract
Abstract Engineered wood-based panels are widely used in structural applications. Accurate measurement of their elastic properties is of great importance for predicting their mechanical behavior during structural design. In this study, an efficient non-destructive test method for measurement of effective elastic constants of orthotropic wood-based panels is proposed based on a modal testing technique. An algorithm was developed based on an improved approximate frequency equation of transverse vibration of orthotropic plates under the boundary condition, in which two opposite sides are simply supported and the other two are free (SFSF). The method is able to predict the frequency ranges and mode indices as well as corresponding normalized sensitivity to elastic constants based on initial estimates of orthotropic ratios with uncertainties and measured fundamental natural frequency. Full-size engineered wood-based panels including cross laminated timber (CLT), oriented strand board (OSB), and medium density fiberboard (MDF) were tested with the proposed method. In general, the measured elastic constants of the three types of panel based on modal test agreed well with those corresponding values measured by static tests. More tests are needed with a range of panel sizes and types for further validation of the proposed test 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".