An Analytical Model for Predicting the Axial Stress Distribution of Self-Tapping Screws Due to Axial Load and Moisture Swelling of Mass Timber Products
Bibliographic record
Abstract
Self-tapping screws are becoming increasingly popular for use in modern timber structures. The axial stress distribution of self-tapping screws due to a mechanical load has been previously studied. However, the stress distribution of self-tapping screws due to moisture swelling-induced load from wood has not been explored so far. This research presents an analytical model to predict the axial stress distribution in self-tapping screws embedded in mass timber products under the combined effects of axial mechanical loading and wood moisture-induced swelling. The analytical model has been validated with numerical simulation. The input properties of the analytical model can be determined from withdrawal tests of self-tapping screws and the manufacturer’s guide of screw and mass timber products. A simple program has been developed to predict the stress distribution and maximum axial stress in self-tapping screws for a range of effective penetration lengths under a pre-load and moisture content change. Correctly predicting the maximum axial stress in self-tapping screws under the simultaneous action of a pre-load and wood moisture swelling-induced load can help design safer timber structures. This research provides a practical method for practicing engineers to predict the maximum axial stress in self-tapping screws due to pre-load and wood moisture swelling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".