Satellite monitoring for early detection of excessive displacements on bridges
Bibliographic record
Abstract
Many bridges around the world are in urgent need of refurbishment and face challenges of limited capital investment and condition assessment. Public transportation agencies need reliable monitoring technologies to provide advance warnings of performance loss and pending failure of their critical assets, probability of which may increase due to climate change and extreme weather events. Satellites can be used to address some of these issues by taking several images of a bridge structure at different times and analyzing them with advanced processing techniques such as Interferometric Synthetic Aperture Radar (InSAR). With this approach, accurate weather-independent displacement measurements can be obtained to complement scheduled visual inspection data for bridge performance assessment. This paper presents the InSAR methodology with application to a major bridge in Montreal, Canada, namely the historic Victoria Bridge over which acquired satellite images have been validated against analytical and numerical predictions. A new calculation methodology is presented for the validation of satellite displacement measurements and their comparison to predefined thresholds in order to identify the extent and location of excessive motion on the bridge structure. The displacement results of interest include linear rate (or velocity) and thermal sensitivity (or thermal expansion rate), where both, taken separately or in combination, are found valuable for an accurate assessment of the bridge structural integrity. Finally, a new software platform called BRIGITAL is presented, which can make use of satellite data for 3D visualization, data analysis, and detection and early warning of excessive bridge displacements, which can help bridge engineers identify hot spots on bridges and take action to avoid possible problems and failures.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".