Methodology for estimating strain gauge measurement biases and uncertainties on isotropic materials
Bibliographic record
Abstract
Compared to actual strains, the values obtained with strain gauges during experimental measurements contain biases and uncertainties. In this article, we propose a methodology using Monte Carlo simulations to estimate the effects of biases and uncertainties from the following: location uncertainty, integration effect and transverse sensitivity errors in unidirectional strain gauges. Moreover, the specific behaviour of welded gauges is also considered. The approach simulates strain gauges on the displacement fields obtained from the structure’s finite element analyses to predict the expected biases and uncertainties. With the use of experimental measurements designed to highlight the biases between gauge measurements and finite element analyses strain results, we verify the methodology. In our experimental verification, we observe that biases are adequately predicted by the proposed method. It is worth mentioning that such an approach can be used not only for validations between finite element analyses and experimental measurements but also for optimizations of strain gauge positioning during measurement campaigns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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 teacher head, 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".