3D Thermal and Spatial Modeling of a Subway Tunnel: A Case Study
Bibliographic record
Abstract
Infrared thermography (IR) is a modern, non-destructive evaluation technology for monitoring and assessing civil infrastructure conditions. It mainly relies on measuring the infrastructure surface temperature to identify any potential defects. Currently, most of the existing research studies in IR rely on 2D thermal images which are time-consuming and labor-intensive. This paper describes a case study that examines the use of both infrared and visual sensing in recording thermal and spatial conditions of a subway tunnel segment in the city of Montreal, Canada. In the case study, both thermal and visible images of the infrastructure conditioning data were collected separately. Next, the visible images were used to generate a 3D point cloud model by applying the structure from motion approach. In parallel, each set of overlapping thermal images were stitched to form a thermal panoramic image that covers a large surface area with an accurate temperature representation. The stitched thermal images were finally mapped to the 3D point cloud in order to produce both thermal and metric measurements of a subway tunnel segment. The results of the proposed framework demonstrate that 3D thermal modeling using visual and infrared sensing is able to generate geometric and thermal information of indoor infrastructure environments. Furthermore, this approach is affordable in terms of cost and time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".