Risk-based pipeline integrity management: A road map for the resilient pipelines
Bibliographic record
Abstract
Pipelines are the most vital energy-transportation mediums of today’s energy-intensive economies. To a level, pipeline integrity is tied to the continuous development and robustness of modern societies, where major failures may result in dire environmental, societal, and economic consequences. Therefore, pipeline safety and integrity are crucial for a sustainable future and responsible development. Pipeline integrity management has been a topic of interest for regulators, practitioners, and academicians alike. Over the past four decades, integrity management has evolved from prescriptive visual inspection and assessment to risk-based integrity management using real-time data. This paper aims to capture the evolution of risk-based methods in integrity management, focusing on the last two decades. The paper answers four primary questions: What is integrity management, and how has it evolved? How does the concept of risk fit in integrity management? What are the methods used to assess and manage pipeline integrity? How will integrity accommodate Industry 4.0?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".