Numerical Investigation of Axial Behavior in Grouted Sleeve Repairs: Insights into Key Design Parameters
Bibliographic record
Abstract
Grouted sleeves are commonly used for pipeline repairs involving localized defects. However, their application to address geohazard-induced strains is less established and needs further exploration. In this research, finite element analysis (FEA) is utilized to examine the axial strain behavior of grouted sleeve repair systems for defective pipelines, considering the influence of various design parameters. Key parameters, including repair length, grout thickness, material properties, interface friction, and sleeve characteristics, are systematically varied to evaluate their impact on load transfer and strain distribution. This study investigates their potential effectiveness for such scenarios. The results indicate that optimal repair length and grout thickness are crucial for minimizing strain, with diminishing benefits beyond certain thresholds. Higher grout stiffness reduces strain, while the modulus of elasticity and thickness of the sleeve significantly influence performance. Proper design balances strain reduction, material costs, and structural efficiency. This research provides actionable insights for optimizing grouted sleeve repairs, offering a framework for enhancing durability and cost-effectiveness in pipeline rehabilitation.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".