Effectiveness of Weld Buttering Repair of Externally Loaded Pressure Vessels With Corrosion Damage
Bibliographic record
Abstract
General corrosion damage can significantly reduce the collapse resistance of externally loaded pressure vessels. This is a consequence of the loss of wall thickness and the associated development of high bending and membrane stresses in the corroded area. A common method used to recover wall thickness is weld buttering, whereby weld material is applied to the affected area. This method is convenient and cost-effective compared to other repair options; however, the extent to which weld-induced distortions and residual stresses counteract the reclamation of wall thickness has not been studied. In the current work, the influence of weld buttering on collapse resistance was explored through collapse tests. Three ring-stiffened cylinders were fabricated by cold rolling and welding high-strength steel plates. Corrosion damage was simulated in two of the cylinders by removing approximately 20% of the wall thickness in a square patch extending between two ring-stiffeners. The simulated damage on one cylinder was repaired using weld buttering. A third baseline cylinder had no simulated corrosion damage. With respect to the baseline cylinder, the simulated corrosion damage reduced the collapse capacity by 5.9%; weld buttering recovered 4.8% of that capacity. It is concluded that weld buttering can be an effective corrosion repair technique, despite the introduction of weld distortions and residual stresses in the vessel.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".