Development of a FAD-Based Girth Weld ECA Procedure: Part I — Theoretical Framework
Bibliographic record
Abstract
Beginning in the late 1970’s and early 1980’s, “alternative defect acceptance criteria” were adopted in various codes and standards in the pipeline industry. These criteria relate the tolerable defect sizes with the magnitude of loads and materials’ resistance to failure. They allow engineers to assess the suitability of the pipes containing defects for intended service conditions, or fitness-for-service. Assessments based on the fitness-for-service principles are often referred to as Engineering Critical Assessment, or ECA. Although most of these codes are based on fracture mechanics principles, the defect tolerance levels vary significantly from code to code. This paper describes a two-year effort funded by PRCI (Pipeline Research Council International) to develop an ECA procedure specifically tailored to pipeline girth welds. The newly developed procedure is in FAD (failure assessment diagram) format. The key features of this procedure are provided in this paper. Based on prior research and extensive experimental data analysis, a modified Miller plastic collapse solution was selected for its rigorous formulation and good agreement with full-scale test results. The effects of weld strength mismatch on plastic collapse load (limit load) were examined and validated through finite element (FE) analysis. Parametric formulae of mismatch correction factors to the plastic collapse solution were adopted. The stress intensity factor solutions of finite-length surface-breaking defects in girth welds were developed and validated. Failure assessment curves (FACs) for girth weld defects were generated. These curves incorporated the effects of material’s strain hardening rate and defect size. They are more accurate than some of the generic material and defect independent FACs, yet easy to use.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".