Field Performance Monitoring of a Sliplined Watermain
Bibliographic record
Abstract
A field instrumentation system was successfully installed to monitor the performance of a HDPE sliplined 83-year old 910-mm diameter cast iron watermain in Ottawa. This monitoring system included various sensors to measure strains in both pipe walls, the internal hydrostatic pressure, the in-situ soil moisture contents and the temperature profiles in the soil backfill. The work included the insertion of sections of the HDPE pipe with sensors and cables attached during the rehabilitation of the entire 1.5-km water line. The installation therefore required the collaborative effort of the research team, the owner, the consultant and the general contractor for the rehabilitation project. About 50% of the installed strain gauges and about 95% of the installed thermocouples survived the installation process. The preliminary results based on the first four-month data show that the measured strain ranges in the HDPE pipe were within the ranges predicted using the short- and long-term modulus of elasticity of the HDPE material. The results have confirmed the assumption that bonding at the interfaces between the HDPE and the grout and between the grout and the cast iron pipe was negligible. The results also show that the granular 'C' backfill material above the pipe had little insulation capacity and that the soil around the watermain is likely to freeze over the winter.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".