Bibliographic record
Abstract
Crude oil and gas products transported using pipelines systems is safe and economical all over the the world. Nonetheless, such pipelines can still be subject to various degrees of failure and degradation generating hazardous consequences and severe environmental damages. As a result, it is important for these pipelines to be effectively monitored and assessed for optimal operation. Many models have been developed to predict pipeline failures and conditions. However, most of these models were limited to use corrosion features as the only factor to assess the condition of pipelines which can lead to inaccurate condition prediction. Therefore, the main aim of this paper is to develop models that predict the condition of offshore oil and gas pipelines based on several other factors including corrosion. Regression analysis and artificial neural network (ANN) techniques were used to develop condition prediction models based on historical inspection data of three existing pipelines in Qatar. In addition, a condition assessment scale for pipelines was built based on experts' opinion. All necessary statistical diagnosis have been checked showing sound results for the developed models. The models have been validated and the results showed their robustness with an average validity percentage from 96 to 99%. The models are expected to help pipeline operators to assess and predict the condition of existing oil and gas pipelines and hence prioritize their inspections and rehabilitation planning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".