Bibliographic record
Abstract
This paper presents recent advancements of pipe penetrating radar (PPR) inspection technology through two selected case studies. The Bear Creek Trunk Sewer in Surrey, BC, Canada is a 2845 m long, 600 mm to 900 mm diameter reinforced concrete and asbestos cement line. The pipe was installed in 1972 and there are known corrosion, erosion, sedimentation, and odor issues. The objective of the PPR survey was to determine the condition and remaining service life of this pipe by mapping its wall thickness, rebar cover and detecting voids and/or other anomalies within or outside the pipe wall. PPR results confirmed minimal corrosion at the crown and 95 mm to 97 mm remaining wall thickness with little variation over the inspected length. Rebar cover appeared to be sufficient with no void type anomalies on any of the inspected lines. The Taggart Outfall in Portland, Oregon is a 3 m diameter, brick lined combined sewer that was built in 1906 and experienced wet weather overflows in the past. There was very little information available about the construction methods and the condition of this pipe. In order to design the most appropriate rehabilitation strategy the knowledge of voids outside the sewer was critical. Over 1829 m of high resolution PPR line data were collected via manned entry. Due to the highly complex nature of the geophysical data, data processing and interpretation was a critical component of this project. PPR data revealed voids both outside and within the pipe wall and thus provided engineers the information needed to take the appropriate approach to rehabilitate the pipe. With limited available funding and budget constraints becoming more prevalent, timing of rehabilitation and overall intelligent asset management is more critical than ever. PPR provides engineers and utility owners the information to accurately estimate the remaining life left in a pipeline, refine timing of repairs, and ultimately better allocate funding for asset management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".