Lessons Learned Mapping Critical Pressure Pipelines: City of Ottawa Case Studies
Bibliographic record
Abstract
The City of Ottawa is a municipality of 960,000 people that owns and operates its own watermain and sewer infrastructure. The City has developed industry-leading comprehensive asset management strategies for its pipe infrastructure. A key element of the management strategy for its critical pressure pipes-the large-diameter watermains (24 in. to 78 in.) and sewer force mains (all sizes up to 42 in.)-includes mapping. Pipeline mapping is a useful tool for addressing distress and managing the asset. However, when trying to correlate internal pipe conditions to above ground coordinates, a number of challenges arise, particularly when defects are identified as a part of the condition assessment. In order to effectively intervene on a distressed pipe, it is critical to be able to locate and excavate that pipe with certainty. Errors in mapping can lead to erroneous excavations, costing the City valuable time and money. In order to overcome this challenge, a variety of technologies and methodologies have been utilized to map the City’s pipelines. To date the City has completed over 24.2 mi of 2-dimensional mapping using a combination of above ground GPS points and internal pipe data collected as part of the condition assessment work. Different inline inspection tools were used to collect the data required to create the maps, including tethered and free swimming tools, depending on the inspection requirements. This paper will discuss the methods used to create the GIS data of the over 24.2 mi of pipeline; the benefits to having the mapping data; lessons learned while trying to use the map; comparisons between the inspection GIS and the City’s GIS; as well as overall challenges with mapping pipelines in general.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".