Identifying Initial Imperfection Patterns of Energy Pipes Using a 3D Laser Scanner
Bibliographic record
Abstract
Measurement of initial imperfections of energy pipes and incorporating them in analytical models has been a major focus of research in the pipeline industry as well as at the University of Alberta. Researchers at the University of Alberta have devised various techniques to measure initial imperfection of pipes prior to testing. The analytical imperfection models developed based on these techniques have proven to be effective in predicting pipe behavior. These techniques, however, are time consuming, error prone to some extent, and yield limited data, in addition to their limitations regarding the size of the pipes that can be measured. The objective of the current study is to overcome the limitations of the previous measurement techniques by utilizing advanced surface profiling technology. A high accuracy 3D laser scanner is used to create three dimensional models of energy pipes. Commercially available reverse engineering and inspection software is used to measure the different geometric attributes of the pipes that are of interest. This new technique enables us to overcome the previous limitations by acquiring data in the field at a faster rate and creating high resolution point clouds. The actual pipe surfaces are compared with the model of a perfect cylinder of uniform nominal diameter. It is possible to locate the axes of the scanned pipes and use these axes as references for measurements. Outer diameter variation, thickness variations, weld geometry variations and deviations from a perfect cylinder are measured. Results indicate that the deviations from a perfect cylinder can be used to describe the pattern of radii variations around the perimeter of the pipes. When described with respect to the seam weld location, distinct patterns of radii variations were identified. Thickness variations showed identical behavior in all the pipes when viewed with respect to the seam weld location.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".