Probabilistic-Based Assessment of Corroded Pipelines: A Comparison Between Closed Form and Surrogate Limit States
Bibliographic record
Abstract
Protecting steel pipeline systems from failure due to corrosions defects is a crucial issue in pipeline industry. Reliability models that use the rate of corrosion growth combined with closed form solutions for the failure pressure are often used to estimate the time periods before excavation and repair. A methodology is presented for the assessment of predicted failure pressure based on finite element analysis (FEA) and reliability analysis. Deterministic failure equations are transformed to probabilistic limit state models. The failure mode is considered to be controlled by the stresses due to internal pressure and the presence of corrosion. A response surface method (RSM) is utilized to build a surrogate model of the limit state function. A comparison between closed-form and the surrogate model approach is discussed. A stochastic model is assumed to match the uncertainty inherent in both loads and strength. Simulation-based approaches and asymptotic methods for probability of failure evaluation are used, namely, Monte Carlo simulation, importance sampling, First Order Reliability Method (FORM) and Second Order Reliability Method (SORM). An adaptive building of the numerical experimental design for the surrogate limit state is proposed. A new artificial neural network (ANN) is developed in order to reduce the computational cost of experimental design scheme’s evaluation. The outcomes obtained from such an approach are useful as a decision-making tool for the maintenance, repair or optimization of pipelines systems.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".