Determination of the Welding Residual Stresses in Welded Joints and Their Effects on SIFs for Surface Cracks: A Review of Recent Progress
Bibliographic record
Abstract
Abstract In this paper, the recent progress on the determination of welding residual stresses in Q345qD steel welded structural components and their effects on stress intensity factors for semi-elliptical surface cracks has been reviewed. Two different welded structural components: butt-welded steel plate and orthotropic steel deck (OSD) are considered. First, we summarize the results from recent studies on the experimental measurements and numerical simulations of the surface and through-thickness residual stress in the welded plate and OSD; the complete longitudinal and transverse residual stress distributions are discussed. Then, the effects of the obtained welding residual stresses on the SIFs for surface cracks in the weldments are quantified. The loading conditions considered are the combinations of service loading and residual stresses on the butt-welded plate and OSD. Semi-elliptical surface cracks with a wide range of aspect ratios and relative depths are investigated. In particular, both 3D FEA and weight function method are used for the determination of SIFs for surface cracks. The effects of welding residual stresses on SIFs are illustrated thoroughly. The current results are very important for the appropriate fatigue life assessment for welded structural components.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".