On the Constraint-Based Failure Assessment of Surface Cracks in T-Plate Welded Joints
Bibliographic record
Abstract
The loss of crack tip constraint leads to enhanced resistance to both cleavage and ductile tearing. However, conventional failure assessment schemes (CEGB-R6, BS-7910) use lower bound toughness obtained from highly constrained test specimens. Cracks in many real engineering structures are not highly constrained, which makes failure predictions using conventional failure assessment schemes based on lower bound fracture toughness values overly pessimistic. Excessive pessimism in the structural assessment can lead to unwarranted repair or decommissioning of structures, and thus cause unneeded cost and inconvenience. Recent developments on constraint-based fracture mechanics have enabled the practical assessment of defective components including the constraint effect. For example, the recent revision of R6 and the newly developed structural integrity assessment procedures for European industry (SINTAP) have suggested a framework for failure assessments including the constraint effect. In this paper, the constraint-based failure assessment of surface cracked T-plate welded joints under tension load is presented. Different issues including the constraint-based failure assessment diagrams, the treatment of combining primary and the secondary loads, and the calculation of stress intensity factors, limit loads and constraint parameters for surface cracked T-plate joints are discussed. It is demonstrated that when the lower constraint effect is properly accounted for, the maximum allowable tensile stress level increases substantially.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".