Axial strain capacity of grouted sleeve repairs: A numerical investigation of critical design factors
Bibliographic record
Abstract
While grouted sleeves are widely used to repair localized damage in pipelines, their capability to minimize geohazard-induced strains and resist slippage and pull-out forces during service is relatively less studied. In this research, finite element analysis (FEA) is employed to explore how grouted sleeve repair systems respond to axial strain in defective pipelines, emphasizing the role of key design parameters and their interactions. The analysis systematically varies these parameters to understand their influence on load transfer mechanisms and strain profiles. Findings show that repair length and grout thickness must be carefully optimized to reduce axial strain effectively, as further increases offer limited or even negative returns. Increasing grout stiffness contributes to lower strain levels, and the sleeve’s stiffness and thickness are major factors affecting overall repair performance. A key contribution of this study is the adoption and application of a strain-based failure criterion, enabling prediction of the limit state behavior under axial loading and guiding design limits for repair effectiveness. Simulations beyond service-level conditions demonstrate that all repaired models outperform the unrepaired pipe, with strain capacity improving as grout and sleeve parameters are optimized. However, these benefits plateau or decline beyond certain values, confirming the existence of optimal ranges for effective repair design. The study offers practical guidance for improving the design of grouted sleeve repairs, and it also provides a foundation for future experimental validation and ongoing numerical studies aimed at further quantifying system performance and supporting broader field implementation.
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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 | 0.001 | 0.005 |
| 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".