Parameters Affecting Dent Strain Using 3D Laser Scan Profile
Bibliographic record
Abstract
Today’s in-ditch laser scan inspection technology provides pipeline operators with relatively accurate 3D dent profile data. Some of the benefits of using laser scan data are to accurately calculate dent strain and quantify its severity. However, there are some concerns regarding the scan parameters used such as; scanner resolution settings, scan coverage over the dent, and its surrounding area as well as repeatability and reliability of scanned dent data that could affect the accuracy of the calculated dent strains. Therefore, it is important to understand how these parameters affect the accuracy of calculated dent strains, which could lead to either over- or under-estimating equivalent strains and result in unnecessary repairs or leaving critical dents in the pipeline without mitigation. The benefit of this study is to help the pipeline operators to reduce in-ditch dent inspection time without compromising on the accuracy of dent geometry and its strain. In this paper the effect of different scan resolutions on the calculated strain is studied first. Then, using high resolution, the effect of scan coverage on the dent strain is studied. In particular, the difference in the calculated strain among 60°, 90°, 180°, 360° scans coverage circumferentially. The repeatability of dent scan with two different resolutions is then evaluated with two real life dent samples. Finally, the findings from this study are summarized. This serves as the basis for developing an optimal procedure for dent laser scanning with acceptable level of scan parameters for a reliable strain assessment. The benefits and limitations of 3D laser scanning technology from this study are also presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".