Comparison of NDE techniques for assessing mechanical properties of unjointed and finger-jointed lumber
Bibliographic record
Abstract
Abstract Non-destructive evaluation (NDE) methods are common for grading structural lumber with static bending as the traditional NDE method for strength. More recently, longitudinal and transverse vibration techniques have also been proposed for grading lumber. In this study, unjointed and finger-jointed sawn lumber has been evaluated by these traditional and relatively new NDE methods. In total, 188 pieces of 38 mm×89 mm black spruce lumber were tested. Of these, 40 were unjointed, 47 had 2–3 finger joints, and 101 had 5–7 finger joints. The main objective was to evaluate the reliability of the various NDE techniques in predicting the bending stiffness and tensile strength of finger-jointed lumber with different number of finger joints. Results show that all NDE methods provide stiffness values of unjointed and finger-jointed lumber that correlate well with laboratory measured static bending stiffness with R2 values ranging from 0.76 to 0.97. Moreover, lumber with finger joints has lower bending stiffness than unjointed lumber. Based on the correlation coefficients, there is no evidence that finger joints affect the precision of the strength prediction by NDE methods.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".