Fuzzy-probabilistic evaluation for the dynamic instability of corroded buried pipes conveying fluids
Bibliographic record
Abstract
This study develops fuzzy-probabilistic models to investigate the dynamic instability (i.e., the first instability point) of corroded buried pipes conveying fluids. The models are developed via a novel hybrid of random fields and fuzzy logic methods to capture both aleatoric uncertainty , stemming from the stochastic nature of pipeline, fluid, and soil parameters, and epistemic uncertainty , arising from corrosion inspection challenges, especially in urban areas. Key features of the models include: (1) applicability to various pipe geometries, fluid types, and soil stiffness distributions ; (2) Pipe Condition Index, on 0–100 scale, updatable from field inspection to reflect corrosion levels; and (3) uncertainty quantifiers including the random field significance level and fuzzy model coefficients . Results showcase the corrosion’s significant influence on dynamic instability. Furthermore, non-uniform soil stiffness distribution, with minimum stiffness at mid-span, increases the susceptibility to dynamic instability compared to the uniform soil case. The developed models achieve a high goodness-of-fit, with R 2 within 0.91–0.96, underscoring their accuracy in predicting the dynamic instability of corroded pipelines conveying fluids.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".