Fillet Weld Groups Loaded with Out-of-Plane Eccentricity: Simulations and New Model for Strength Characterization
Bibliographic record
Abstract
The strength of fillet weld groups, loaded with an out-of-plane eccentricity, is controlled by complex interactions of weld yielding, as well as bearing between the connected parts. Current models that characterize connection strength, including those used in North American design specifications, are highly conservative, leading to oversized welds. These models are phenomenological, because the internal stress distribution within the welds is difficult to characterize experimentally. A new model is proposed for characterizing the strength of these connections. The model is based on insights developed from sophisticated finite-element (FE) simulations that feature accurate measurements of weld profiles, multiaxial plasticity, and simulation of contact and gapping phenomena that strongly influence connection response. The FE simulations reveal that current models do not reflect key aspects of force transfer within the connection, especially on the compression side. The proposed model incorporates these insights by using stress profiles and mechanisms consistent with those implied by the FE simulations. The model is evaluated against 79 experiments from three test programs. It is determined that the new model greatly reduces the conservatism of the existing models, resulting in an average test-to-predicted ratio of 1.01. This is in contrast to previous models, for which the average test-to-predicted ratios are in the range of 1.33–1.77. The efficacy of the proposed model is analyzed with respect to various parameters, and its limitations are outlined.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".