Risk assessment of gas pipeline using an integrated Bayesian belief network and <scp>GIS</scp>: Using Bayesian neural networks for external pitting corrosion modelling
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Corrosion poses a great risk to the integrity of oil and gas pipelines, leading to substantial investments in corrosion control and management. Several studies have been conducted on accurately estimating the maximum pitting depth in oil and gas pipelines using available field data. Some of the frequently employed machine learning techniques include artificial neural networks, random forests, fuzzy logic, Bayesian belief networks, and support vector machines. Despite the ability of machine learning methods to address a variety of problems, traditional machine learning methods have evident limitations, such as overfitting, which can diminish the model's generalization capabilities. Additionally, traditional machine learning models that provide point estimations are incapable of addressing uncertainties. In the current study, a Bayesian neural network is proposed to include uncertainty in estimating the corrosion defect of a pipeline exposed to external pitting corrosion. The results are then incorporated into a Bayesian belief network for evaluating the probability of failure and its corresponding consequences in terms of social impact, thus forming a comprehensive risk assessment framework. The results of the Bayesian neural network are validated using field data and achieved a testing accuracy of 90%. The framework of the study offers a powerful decision‐making tool for the integrity management of pipelines against external corrosion.
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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 | 0.001 | 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 | 0.001 |
| 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