Numerical analysis of the fatigue behaviour of friction stir welded joints in aluminum bridge decks
Bibliographic record
Abstract
Friction stir welding (FSW), a relatively new welding technique, has been widely used in the aero- space and manufacturing industries, showing superior mechanical and durability properties. How- ever, its application in civil engineering is very limited due to the absence of appropriate standards and quality control guidelines. FSW appears to be a promising welding solution for the fabrication of vehicular bridge decks made from aluminum extrusions, with a potential to reduce distortions and improve fatigue properties. The fatigue behaviour of common FSW joint types such as the butt FSW has extensively been investigated and documented in literature. However, certain practical configurations such as the butt-lap joint used in the fabrication of extruded aluminum bridge decks have rarely been studied, especially in the area of fatigue performance. In this context, the present research provides first an overview on the welding process of typical aluminum friction stir welded bridge deck extrusions presenting the butt-lap configuration. Then, the fatigue behaviour of butt- lap FSW joints is assessed using the effective notch stress (ENS) approach. The effect of geometrical features on the fatigue behaviour of butt-lap FSW joints is numerically investigated also by the ENS approach.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".