Automated Surface Preparation and Lining Installation in Penstocks and Large Diameter Water Pipes
Bibliographic record
Abstract
Abstract Since the catastrophic accident at the Cabin Creek penstock in Georgetown, CO on October 2, 2007 where five coating applicator’s lives were lost (Ref 1), the hydroelectric and water transmission industry has been looking for safer practices to line the interior of penstocks and other large diameter water pipes. In nearly all applications, owners and specifiers have required the use of 100% solids coatings as opposed to the previously used solvent borne coatings. Safety protocols have been established that limit the amount of solvent and other flammable liquids in the pipe. However, in most cases, the surface preparation and coating work is still performed with personnel in the pipes. One hydroelectric utility essentially eliminated the need for personnel to enter the pipe during the surface preparation and lining installation processes. BC Hydro in Vancouver, British Columbia developed coating work prerequisites that required contractors to develop surface preparation and lining installation methods that were automated, controlled, and monitored from outside the pipe. Entry into the pipe by workers was only needed for equipment set-up, adjustments, spent abrasive removal, and some quality control. This paper addresses the conceptual design process, describes the robotic equipment that was developed for the first project at BC Hydro Bridge River 2 Penstock 1, and the overall performance of the equipment. Lessons learned from the project implemented with automated equipment are also described.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".