Identification of material parameters of two-dimensional anisotropic bodies using an inverse multi-loading boundary element technique
Bibliographic record
Abstract
An inverse technique, based on the boundary element method (BEM) and elastostatic experiments for identification of elastic constants of orthotropic and general anisotropic 2D bodies is presented. Displacement measurements at several points on the boundary of the body, obtained by a few known load cases are used in the inverse analysis to find the unknown elastic constants of the body. Using data from more than one elastostatic experiment results in a more accurate and stable solution for the identification problem. In the inverse analysis, sensitivities of displacements of only boundary points with respect to the elastic constants are needed. Therefore, the BEM is a very useful and effective method for this purpose. An iterative Tikhonov regularization method is used for the inverse analysis. A method for simple computation of initial guesses for unknown elastic constants and a procedure for appropriate selection of the regularization parameter appearing in the inverse analysis is also proposed. Convergence and accuracy of the presented method with respect to measurement errors and number of load cases are investigated by presenting several examples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".