Deflectometry, a full field slope measurement technique: General perspectives and application to loading identification using the virtual fields method
Bibliographic record
Abstract
The ability of photographic techniques to quickly record high volumes of scientific data has been understood since the early 1900s. It is only following the advent of digital imaging and data processing systems that so-called full-field optical measurement techniques became sufficiently reliable for performing high spatial density structural vibration measurements. In particular, the deflectometry technique directly provides a spatially and temporally resolved measurement of slope fields on plane structures using a single high-speed camera. The presentation first demonstrates the principles of this technique and explores some direct applications of such full-field measurements. The advantages and drawbacks of deflectometry are compared with other optical techniques like digital image correlation or scanning laser Doppler vibrometry. A key aspect of several engineering domains consists in the identification of dynamic loads acting on structures by inverse methods. It is shown that coupling deflectometry measurements with the virtual fields method enable the reconstruction of stationary and transient excitations without any specific regularization. Experimental reconstruction results on an aluminum panel are presented for two different transient mechanical loadings: instrumented impact hammer and impacting metal marbles (multiple unknown excitations). Finally, the identification of random excitations is considered using a plate and a membrane.
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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 | 0.001 |
| 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 | 0.001 |
| 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".