Distributed fibre optic sensing of strains on buried full-scale PVC pipelines crossing a normal fault
Bibliographic record
Abstract
Seismic faulting is extremely detrimental to buried pipelines owing to bending moments and axial forces generated by the soil–pipe interaction. Pipe responses to ground rupture are usually evaluated by beam-on-spring analysis. However, the empirical data used to define the spring response were obtained for steel pipes with high flexural stiffness and the effectiveness of the resulting spring stiffness values for flexible pipes is questionable. This paper presents full-scale tests undertaken using a new split-box apparatus which permits the testing of pipes in dry sand subject to a normal fault with dip angle of 90°. Four polyvinyl chloride (PVC) pipes (various diameters), one instrumented with strain gauges and three instrumented with fibre optic sensors, were tested to provide experimental evidence for flexible pipes. The research includes confirmation of the effectiveness of fibre optic strain measurement, which has the advantage of providing much more data than the limited discrete values obtained from conventional strain gauges. However, the fibre optic strain sensing technique is limited to strain values below 1%. The prototype-scale testing permits assessment of the efficacy of the current design approaches, including the relative success of accounting for reduced spring stiffness or patterns of imposed deformation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".