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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".