Data-Driven Approach to Assessing the Tensile Strain Capacity of Pipelines With Two Different Girth Welds
Bibliographic record
Abstract
Abstract The effect of girth welds on the tensile strain capacity (TSC) of pipes is critical in the strain-based design and assessment of pipelines. In this study, machine learning (ML) models for regression and classification were developed and evaluated to predict the tensile strain capacity for typical mechanized gas metal arc welding (GMAW) and flux-cored arc welding (FCAW)/shielded metal arc welding (SMAW) pipes and to classify data from the girth-welded pipes. The regression models were trained on over 15,000 data points for each pipe, derived from TSC equations found in the literature. The classification model utilized all data points from the two types of pipes. The developed regression models demonstrated accurate predictions of the TSC for both FCAW and GMAW pipes, without overfitting or underfitting, and properly captured relationships between the TSCs and features. Random Forest’s built-in capability for computing feature importance indicated that flaw depth is a critical feature affecting the TSCs of the two girth-welded pipes. However, the performance of the classification model was unsatisfactory due to inseparable data within a certain range, although it could be improved to some extent by applying different selections of features.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".