Performance of axially loaded self-tapping screw in mass timber subjectedto moisture content change
Bibliographic record
Abstract
Self-tapping screws (STS) are a popular fastener in mass timber construction due to several advantages over traditional fasteners such as bolts and lag screws. The advantages include ease of installation, increased strengths and stiffness in connections, and availability in a wide range of length and diameter. However, their performance is impacted by several parameters. Due to their potential large penetration length, a particular concern of designers and builders is unintended stress in the screw when wood member is subjected to moisture content change. The increase in moisture content effects negatively on the strength of timber members and components, and also results in swelling (in-plane and out-of-plane deformations), which influences the level of tensile stress through the length of the screw, potentially resulting in premature failure which has been often overlooked or neglected in the past. The aim of the research is to analyze the failure possibility of STS with different penetration lengths used in glue-laminated timber (glulam) subjected to different initial axial load due to torquing and moisture content change. A 2D axisymmetric finite element model (FEM) has been developed using ABAQUS commercial software to evaluate the stress distribution along the length of the STS. Analyses were performed by varying the overall moisture content of the glulam, but it was assumed that the moisture content remains uniform inside the glulam, i.e. no moisture gradient through the member dimension. The results of this study provide valuable information about the critical points on the STS based on the maximum principal stress theory and indicate damage areas that may happen under different loading conditions. These results can be used to improve the design and performance of self-tapping screws in mass timber construction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".