Response of Corroded Steel Pipeline Without and with CFRP Reinforcement to Reverse Fault Movement
Bibliographic record
Abstract
Corroded steel pipelines are particularly vulnerable to failure due to ground movement, which highlights the need to improve their seismic resistance through reinforcement methods. This paper establishes a three-dimensional finite element model of a corroded steel pipeline subjected to a reverse fault, which considers the effects of the corrosion position and depth, winding thickness, and length of carbon fiber-reinforced polymer (CFRP), to investigate the stress, strain, elliptic deformation, and failure modes of the pipeline before and after CFRP reinforcement. Results indicate that the main failure mode of the intact and corroded pipeline crossing the reverse fault is local buckling. Corrosion intensifies the response of the cross-fault pipeline, accelerates its failure occurrence, and promotes transformation from a single failure mode to multiple failure modes. For CFRP reinforcement, an increase in CFRP winding thickness can effectively inhibit the growth of the pipeline’s compressive strain, thus reducing the buckling potential. Each additional CFRP layer can further enhance the overall buckling resistance but at a decreasing rate. Similarly, longer CFRP winding improves buckling resistance though the effectiveness per meter decreases. Therefore, it is recommended that the thickness and length of CFRP winding on the pipeline should be optimized to obtain the best reinforcement at a reasonable cost.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".