Longitudinal Bending and Failure of GFRP Pipes Buried in Dense Sand under Relative Ground Movement
Bibliographic record
Abstract
Pipelines extend thousands of kilometers for transport and distribution of oil, gas, and other chemical products. With the ever persistent challenges often faced with corrosion, relative rigidity, and other issues characteristic to steel pipes, the need to explore the use of new pipeline materials, such as glass fiber–reinforced polymers (GFRP), increases. The pipe-soil interaction and the longitudinal behavior of such pipes resulting from relative ground movements is poorly understood. In this study, a series of GFRP pipe bending experiments have been conducted on 115-mm-diameter and 1,830-mm-long GFRP pipes buried in dense sand. The pipe ends were pulled by two parallel cables attached to a spreader beam outside the test region, which was pulled by a hydraulic actuator. The study investigated the effect of laminate structure of pipe, including a cross-ply and angle-ply laminates, on the strength, deflections and failure modes, at different burial depth-to-diameter (H/D) ratios of 3, 5, and 7. Results were also compared with steel control pipes of comparable dimensions and pressure rating. The peak load was shown to increase as burial depth increases, and was generally associated with soil failure, except for the angle-ply pipe at H/D=7 that experienced a structural failure. At peak loads, the net deflections of GFRP pipes were 4–7.5 times those of the equivalent steel pipes, with the cross-ply pipes being stiffer than angle-ply pipes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".