Use of 3D range cameras for structural deformation measurement
Bibliographic record
Abstract
A three-dimensional range camera is a state-of-the-art imaging technology that has strong potential for various closerange high-precision measurement applications. One such application is the measurement of structural deformation under external loading conditions. Deformation tests have been conducted on two concrete beams with and without steel-reinforced polymer sheets in an indoor testing facility using an SR4000 range camera. The achieved measurement precision and accuracy were both within 1 mm when compared with a terrestrial laser scanner. Further testing on the concrete beam with the steel-reinforced polymer sheets has shown that a deformation as small as 3 mm can be reliably detected with a range camera with a measurement precision of 0.3 mm and an accuracy of 0.4 mm. These results clearly indicate the high metric potential of 3D range cameras in spite of their coarse imaging resolution and low (centimeterlevel) single point accuracy. The high accuracy can be achieved thanks to the differencing scheme used to derive the deflection estimates from two sets of range camera measurements, one at no-load and one of the beam in a loaded state, which eliminates the scene-dependent range biases such as scattering and multi-path errors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".