Distributed Strain Sensing to Study a Composite Liner for Cast Iron Water Pipe Rehabilitation
Bibliographic record
Abstract
Abstract The use of cured-in-place pipe lining systems for the rehabilitation of cast iron water pressure pipe has grown significantly in the past 20 years. With limited information on the performance of these lining systems in service, strain measurements are needed to evaluate theoretical findings about three-dimensional strength limits. The application of distributed optical fiber sensing to study the properties of a polymer composite liner was investigated in this research. Polyimide optical fibers were installed into flat liner coupons fabricated in the laboratory, and the specimens were subsequently tested in uniaxial tension and four-point bending. Using optical frequency domain reflectometry, Rayleigh backscatter was monitored from preinstalled optical fiber sensors and strains of up to 4,000 μϵ were obtained. Comparisons between experimental optical fiber measurements and results obtained from conventional strain measurement techniques (i.e., extensometer and crosshead displacement) showed a strong correlation with an average difference of 3 % in four-point bending and 6 % in tension. However, the orientation and diameter of the composite fiber reinforcements were observed to affect distributed sensing performance.
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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".