Machine Learning-Based Severity Assessment of Pipeline Dents
Bibliographic record
Abstract
Abstract One challenge to pipeline operators is to identify potentially injurious dents among thousands of reported deformation features using limited information (e.g., reported dent’s length, width, and depth) and to prioritize the efforts and allocate the resources to obtain additional more detailed information (e.g., dent profiles) for those potentially severe dents. An innovative approach based on machine learning predictions stemming from a representative dictionary of finite element analysis (FEA) generated prototypes was developed. The proposed approach predicts multiple severity-based indicators for each dent, then combines them in an overall severity score, which finally is used to prioritize the acquisition of dent profiles. Once the dent profiles are available, detailed level 3 FEA quantitative reliability analyses, following previously developed and published methodology (QuAD) [1], is performed allowing pipeline operators to confirm dent’s severity more accurately and perform an integrity risk informed decision (IRIDM) leading to a safer and more efficient integrity management. Three severity indicators were considered herein and intended to address both formation-induced and service-induced failure mechanisms. The maximum dent formation plastic strain and accumulated ductile failure damage were used for evaluating the likelihood of forming a crack during indentation. The third indicator was the stress concentration factors (SCFs) to assess the potential of service-induced failure due to fatigue. A machine learning model, as an emulator, trained and tested using ∼4000 FEA-based dent prototypes was shown to be able to effectively predict dent severity indicators previously referred to. These predicted dent severity indicators are combined to produce an overall severity score, which was finally used to prioritize the acquisition of the detailed dent profiles. Once profiles are obtained, detailed FEA quantitative reliability assessments will ultimately confirm the severity and hence drive repair/no repair decisions, enabling in this way an efficient and effective allocation of resources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".