Visualized Analysis of Mapping Knowledge Domains for Oil and Gas Pipelines Failure Research
Bibliographic record
Abstract
Failure analysis is a vital technique that is intended to strengthen the integrity management of oil and gas pipelines. The significance is to reduce economic losses and avoid or minimize major failure incidents. The aim of this study is to use bibliometric methods to research 2,533 papers retrieved from the Web of Science database spanning from 2004 to 2023. The analysis conducted using VOSviewer, focused on temporal trends, geographic distribution, major organizations, leading authors, journal co-citations, and literature co-citations. The objective is to uncover research hotspots and frontiers, providing insights to advance failure analysis and prevention techniques. The findings revealed a substantial surge in the number of papers related to failure analysis, escalating from 16 in 2004 to 190 in 2023, indicating an overall exponential growth trend. This growth has been most pronounced over the past 8 years. Noteworthy contributors to this field include China, the USA, Canada, England, and Iran, with Iran, Australia, and Italy exerting significant impact. In addition, the top three research producers are all from institutions or universities located in China. The journals Engineering Failure Analysis, International Journal of Pressure Vessels and Piping, and Journal of Loss Prevention in the Process Industries exhibit the highest publication numbers. Significantly, Journal of Loss Prevention in the Process Industries and Gas Science and Engineering emerge as influential and highly regarded publications within this field. The study revealed that while the foundational theory and research framework in oil and gas pipeline failure have crystallized, a plethora of research directions and cutting-edge branches continue to emerge. Notably, the study of failure possibilities and behavior through Bayesian networks, failure characterization analysis, and finite element methods have emerged as the primary development directions and research hotspots. In terms of innovation, the application of bibliometric methods has enhanced the capacity to handle extensive literature databases and conduct network analyses. This study furnishes a theoretical foundation and guidance for the advancement of failure analysis and prevention techniques in the field of oil and gas pipelines.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.046 | 0.039 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".