A Design of Experiments Approach to Identifying Damage in Structures Using Modal Frequency
Bibliographic record
Abstract
Modal vibration parameters such as frequency, damping ratio and mode shape have long been considered useful for identifying damage in structures. In this paper a generalized approach is presented that allows for damage to be localized and quantified using regression and response surface modeling of modal frequency. Regression models or response surface models are developed to characterize how modal frequencies of structures are affected by variations in parameters such as defect depth, width and location. Design of experiments (DOE) techniques are used in conjunction with experimental modal frequency measurements to solve for defect parameters of test specimens in the field for condition monitoring. Determining defect parameters can be done by inverting and explicitly solving regression model equations, employing software-driven numeric optimization or through a graphical approach that overlays contour lines of multiple response surface models. Either of these methods can be automated. This approach is explored and validated with finite element and theoretical beam models along with a series of physical experiments on cantilevered aluminum rods. The method performs well for detecting simple and distinct defects. Implementation complexity increases when detecting multiple or more variable, less-easily quantifiable defects. In its general form, the method shows promise for damage detection when a specific type of consistent defect is known to occur or for applications such as quality control on production lines and monitoring of deposit buildup in pipes.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".