Development and Validation of Load-Interaction Based Models for Crack Growth Prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This investigation primarily focused on the validation of the software being developed for crack growth and remaining life prediction using SCADA data. A total of nine pressure spectra, four for oil pipelines and five for gas pipelines, have been collected and used as inputs for the software. It was found that these spectra could be categorized as the underload-, the meanload- and the overload-dominant spectra; each of them have shown different effects on crack growth: the underload spectra, typical of pressure fluctuations at the discharging sites, are most susceptible to crack growth because of load interactions between the minor pressure fluctuations and the unload cycles; while the overload spectra, often found at the suction site, have exhibited retarded crack growth due to the retardation effects caused by overloading. The relative severity of the load interactions in terms of crack growth rate for a given spectrum was quantified using a parameter termed as the Spectrum Factor. A Spectrum Factor greater than one indicates the enhanced crack growth rate by load interactions, such as the case where unloading is frequently present in the pressure spectra, while a Spectrum Factor lower than one may be associated with a retarded crack growth, which can be seen in pressure spectra with predominant overloading events. The predictions made by the models being developed were also compared with those made by the rainflow counting method. The software allows for the SCADA/pressure fluctuation data, in excel spreadsheet format, to be directly analyzed producing a projected remaining life of the pipeline based on the past pressure fluctuations and the assumed future pressure fluctuations.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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