Assessment of Trihedral Corner reflectors to improve satellite-based monitoring of bridges
Bibliographic record
Abstract
A two-phase feasibility study has been conducted to investigate whether concrete bridges with poor natural radar reflectivity could be fitted with artificial corner reflectors at strategically selected locations to improve their monitoring suitability from radar satellites. The first phase consisted of identifying the optimal size and positioning of the corner reflectors on a case study bridge, being the Confederation Bridge linking the Canadian provinces of New-Brunswick and Prince-Edward Island. The optimization was based on several parameters including those related to the satellite viewing geometry and others depending on bridge construction details. Based on this theoretical study, the ideal size of the corner reflectors and the most suitable locations were identified to maximize the strength of the return radar signals and minimize the theoretical displacement error. The second phase of the study consisted of validating these preliminary theoretical specifications by installing a pair of corner reflectors on the bridge for field evaluation. These reflectors were installed by the bridge operator on the exterior side of the bridge barrier wall according to specifications determined in Phase 1. After the installation of the reflectors, satellite imagery was acquired over a period of six months to analyze and compare the backscatters coming from the artificial corner reflectors and the nearby elements of the bridge. The results from this feasibility study allowed to conclude that bridges with poor natural radar reflectivity like the Confederation Bridge would substantially benefit from having a dense array of corner reflectors for satellite-based monitoring which would enable accurate displacement measurements at precisely known locations for bridge performance assessment.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".