Probabilistic Modeling of Corroded Pipeline Structures
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
Corrosion is one of the most important damage mechanisms for in-service pipelines, and a significant portion of the maintenance budget is directed toward corrosion-related problems. A major challenge associated with the assessment of the impact of corrosion on the integrity of pipeline structures involves quantification of the amount and severity of corrosion damage present in the structure. Corrosion defects are typically characterized by spatially random distributions and variabilities in size, shape, and morphological characteristics throughout the exposed part of the structure. For pipeline corrosion, such spatial randomness and variability are best modeled using a nonhomogeneous random field approach. A review of some existing random field modeling strategies and their potential for modeling in-service pipeline corrosion data (including their limitations) is presented. A practical random field modeling strategy is developed, which is suitable for in-service pipeline corrosion modeling and circumvents the limitations of existing models. The application of the strategy is demonstrated via example problems, wherein the model is applied to actual pipeline corrosion data. A preliminary application of the corrosion model is also undertaken to assess the residual strength of a pipeline subjected to corrosion damage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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