STRUCTURAL 3D MONITORING USING A NEW SINUSOIDAL FITTING ADJUSTMENT
Bibliographic record
Abstract
Abstract. Digital photogrammetric systems combined with image processing techniques have been used for structural monitoring purposes for more than a decade. For applications requiring sub-millimetre level precision, the use of off-the-shelf DSLR cameras is a suitable choice, especially when the low cost of the involved sensors is a priority. The disadvantage in the use of entry level DSLRs is that there is a trade-off between frame rate and burst rate – a high frame rate is either not available or it cannot be sustained long enough. This problem must be overcome when monitoring a structural element undergoing a dynamic test, where a range of loads are cycled through multiple times a second. In order to estimate deflections during such a scenario, this paper proposes a new least-squares adjustment for sinusoidal fitting. The new technique is capable of processing multiple back-to-back bursts of data within the same adjustment, which synthetically increases the de-facto temporal resolution of the system. The paper describes a beam deformation test done in a structures laboratory. The experimental results were assessed in terms of both their precision and accuracy. The new method increased the effective sampling frequency three-fold, which improved the standard deviations of the estimated parameters with up to two orders of magnitude. A residual RMSE as low as 30 μm was attained, and likewise the RMSE of the computed amplitudes between the photogrammetric system and the control laser transducers was as small as 34 μm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".