Pipe condition assessments using Pipe Penetrating Radar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper describes the development of Pipe Penetrating Radar (PPR), the underground in-pipe application of GPR, a non-destructive testing method that can detect defects and cavities within and outside mainline diameter (>;18 in/450mm) non-metallic (concrete, PVC, HDPE, etc.) underground pipes. The method uses two or more high frequency GPR antennae carried by a robot into underground pipes. The radar data are transmitted to the surface via fibre optic cable and are recorded together with the output from CCTV (and optionally sonar and laser). Proprietary software analyzes the data and pinpoints defects or cavities within and outside the pipe. Thus the testing can identify existing pipe and pipe bedding symptoms that can be addressed to prevent catastrophic failure due to sinkhole development and can provide useful information about the remaining service life of the pipe, enabling accurate predictability of needed intervention or the timing of replacement. This reliable non-destructive testing method significantly impacts subsurface infrastructure condition based asset management by supplying previously unattainable measurable conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it