Integrity Analysis of Dented Pipelines using Artificial Neural Networks
Bibliographic record
Abstract
The repair of dents in oil or gas pipelines is mandated based on depth and interaction with stress risers, according to pipeline regulations in Canada and the United States. However, there have been cases where dents that did not meet the regulatory repair criteria have ended up failing, leading to operator need for an accurate assessment method for dents in order to maintain safety. While there is no agreed-upon method currently available in industry, conservative techniques employed by operators have led to poor dig efficiency. Recent research in industry has focused on strain- and fatigue-based techniques to assess the severity of dents and prioritize them for excavation and repair. Finite element analysis has been highlighted as an accurate method to evaluate strains and stresses within dented regions of pipe, although the significant computational time required for this method makes it inefficient for system-wide analysis. In this paper, the results from hundreds of finite element analysis models are used to train artificial neural networks. Subsequently, the artificial neural networks output accurate stresses and strains, that would be obtained using finite element analysis, when presented with input dent and pipe information. As a result, the artificial neural networks harness the accurate results that can be obtained from finite element analysis while results can be obtained efficiently for applicability to a pipeline system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".