Model updating and damage detection of an aluminum truss with double eigenvalues
Bibliographic record
Abstract
Damage in a truss is detected by updating a finite element model before and after\ndamage has occurred. A sensitivity-based update method is used, which requires the derivatives\nof eigenvalues and eigenvectors of the finite element model. Since the truss is symmetric, it has\ndouble eigenvalues, and the corresponding eigenvectors are non-unique. The problem manifests\nitself typically in that the calculated eigenvectors are rotated with respect to the measured eigenvectors.\nThe theory of eigenvector derivatives in the case of multiple eigenvalues is reviewed.\nWith this theory, unique eigenvectors can be determined but they depend on the update\nparameter under consideration. As an <i>ad hoc</i> strategy, the eigenvectors that are closest to the\nmeasured ones are selected, assuming that the corresponding parameter is the one that presumably\nwill undergo the largest change during the update. In this way, the calculated eigenvectors\nare automatically rotated to the measured ones. The algorithm is applied to an aluminum truss\nthat has been tested experimentally at the University of Sherbrooke. Eigenvalues and eigenvectors\nwere determined from ambient vibration tests. Damage has been induced by removing a diagonal\nelement. The algorithm handles well the case of double eigenvalues and predicts accurately\nthe induced damage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".