Sizing Stress Corrosion Cracks Using Laser Ultrasonics
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Bibliographic record
Abstract
Integrity management decisions related to operating energy transmission pipelines affected by Stress Corrosion Cracking (SCC) represent a formidable challenge to the pipeline industry. Effective management of SCC damage requires the development of tools and technology to identify the occurrence of SCC and to assess the impact of the SCC on pipeline integrity. Development of practical non-destructive evaluation (NDE) solutions for the measurement and evaluation of SCC, including crack depths, is difficult due to the complexity of crack shapes and their inter-relationship and distribution within crack colonies. Laser ultrasonics is an inspection technology using laser beams to generate and detect ultrasonic waves in the pipeline wall to be inspected. Unlike conventional ultrasound, it has a large bandwidth and the beams have a very small (∼0.5mm) footprint. These characteristics make it ideally suited for application as a depth sizing tool for SCC in pipelines. Through a collaborative research project jointly funded by the US Department of Transportation, Pipeline and Hazardous Materials Safety Administration (PHMSA) and PRCI, Applus RTD and its research partners have conclusively shown that laser ultrasonic inspection technology using the Time of Flight Diffraction (TOFD) technique reliably and accurately measures the depth of SCC. In addition, this technique may also be applicable to measuring the depth of other cracks such as seam weld anomalies. The project included the development of a prototype NDE inspection tool for measurement of SCC, and recently culminated with a series of full-scale demonstrations of the tool. This paper describes the detailed technical work conducted to support the development of the tool and validation of the TOFD technique for sizing the depth of SCC. In addition, this paper presents the preliminary results of work on a closely related project that builds on the technology described above to produce an integrated approach and tool for mapping, sizing, and evaluating SCC that filters significant (i.e., deep) cracks from more benign cracks within an SCC colony.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it