Bending Response and Design Equations for Gravity-Flow Pipe Liners Passing across Ring Fractures or Joints
Bibliographic record
Abstract
Vehicle loads and differential ground movements can induce tensile strains in close-fitting polymer liners installed within gravity flow pipes, where the liner stretches across ring fractures or joints experiencing rotation (i.e., opening of the joint at the invert if the joint is moving down relative to the other ends of the pipe segments, or at the crown if the joint is moving upward compared with the other ends). A finite-element model is established and suitable pipe length and mesh size are determined. The stress and strain distributions along hoop and axial directions are then evaluated, considering factors such as inside diameter of the host pipe, liner thickness, rotation angle, liner elastic modulus, friction coefficient between the liner and host pipe, and Poisson’s ratio of the liner. After that, curve fitting is used to develop design equations for estimating stress and strain, and their performance is evaluated against the finite-element data. Finally, the potential effects of gravity and buoyancy are investigated. For small rotations, the stress is proportional to strain, and the maximum stress of the liner occurs directly at the joint, at the point where joint opening is greatest. The friction coefficient and liner thickness have a small effect on the maximum stress, so this simplifies consideration of this limit state in design. The design equation for stress provides estimates within 8.6% of those obtained from the three-dimensional finite-element analysis (with R2 between 0.992 and 0.993). Subsequent evaluation of the proposed equation using strain measurements obtained from full-scale experiments is recommended.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".