Data-Driven Approach to Assessing the Tensile Strain Capacity of Pipelines With Two Different Girth Welds
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it