Reliability Assessment of Offshore Asset under Pitting Corrosion Using Bayesian Network
Bibliographic record
Abstract
Abstract Corrosion is a major cause of structural deterioration in marine and offshore industries. It affects the life of process equipment and pipelines resulting in structural failure, leakage, product loss, environmental pollution and the loss of life. Pitting corrosion is regarded as one of the most hazardous forms of corrosion in marine and offshore structures. Hence reliability assessments of these structures are crucial. The empirical and statistical degradation models are developed by either fitting field or lab data. However, these models are only useful for specific site or operating conditions and still carry a high degree of uncertainty. Other modeling approaches used for assessing rate of pitting corrosion in industry is phenomenological model which is based on corrosion scientific principles. These models provide strong understanding of corrosion process but are often hard to test in engineering applications. This paper presents a novel methodology for predicting the pitting corrosion rate of structural steel in long-term marine environment. The proposed methodology combines a multi-phase phenomenological and empirical model with calibrated real-world data using the Bayesian Network (BN) approach. A case study is presented which exemplifies the application of this methodology to predict the long-term pitting corrosion rate in marine environment. The result shows that the proposed BN based methodology is successful in predicting the time-dependent pitting corrosion rate for steel structures in different environmental conditions.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".