Inspecting Twin 42” Reinforced Concrete Pipes with Pipe Penetrating Radar Supplemented by LiDAR
Bibliographic record
Abstract
In the midst of a renovation project set to convert an old field house into a recreational centre, concerns were raised about the feasibility of the project due to the structural integrity of the pipes running under the building. The project entailed converting an old Campbell soup factory into a recreational cold storage facility for the residents of Worthington, MN. The issue involved two 80+-year-old 42 in. reinforced concrete pipes with unknown conditions that lay beneath the building. Without proper inspection of the pipes, the consultants could not allow the project to continue. A condition assessment was called for, in order to continue the project, but the consultants were not convinced either CCTV or LiDAR alone was the solution, so they contacted SewerVUE Technology and inquired about their patented pipe penetrating radar (PPR) technology. PPR is the in-pipe application of ground penetrating radar. GPR antennas are taken inside the pipe and are used to scan the inner wall. With this method, PPR surveys can see remaining wall thickness, rebar cover, delamination, and detect the presence of voids developing outside the pipe. PPR, supplemented by LiDAR, were assets in coming up with the appropriate design approach for the project.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".