A comprehensive review of in-service pipeline repair methods with a focus on axial strain capacity
Bibliographic record
Abstract
Pipelines provide a reliable, cost-effective transport method for oil and gas products. However, their integrity can be threatened by defects and geohazards, making in-service repair methods crucial for maintaining safety, ensuring continuous operation, and supporting long-term sustainability. This review evaluates different in-service pipeline repair methods focusing on axial strain capacity. Repair is crucial for pipeline integrity in geohazard-prone regions where ground movements, such as landslides and subsidence, can induce significant axial strain. The review provides an in-depth analysis of these repair methods' design functionality and real-world applications while discussing their challenges and recent advancements. A comparative assessment of repair methods based on defect coverage, ease of execution, flexibility, and cost-effectiveness is included to select the most suitable repair techniques. Additionally, the review identifies current challenges in improving axial strain capacity and underscores the importance of future research. Further studies are required to optimize sleeve geometry and weld configurations, improve installation practices, and investigate the long-term behavior and interfacial bonding of grout and filler materials, as these factors are critical to enhance axial and hoop strain capacities and improve the pipeline's overall resilience and longevity. • Reviews in-service repair methods for pipelines under axial strain conditions. • Identifies strengths and limitations of sleeves, grinding, and composite wraps. • Highlights new repair strategies using 3D printing, sensors, and smart coatings. • Discusses factors affecting axial strain capacity in existing repair systems. • Outlines future needs for adaptive, data-driven, strain-tolerant repair designs.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".