Application of vibration based methods and statistical pattern recognition techniques to structural health monitoring
Bibliographic record
Abstract
The primary objective of Structural Health Monitoring (SHM) is to diagnose structures for damage, take necessary measures if any damage occurs, and estimate their degradation rate. Conventional non-destructive evaluation methods are not always practical for implementation of a continuous health monitoring system. Vibration Based Damage Identification (VBDI) methods applied to SHM can be useful in interpreting the global vibration response of a structure to identify local changes. Due to complicated features of real life structures there are some uncertainties related to input parameters such as measured frequencies and mode shape data, where output is sensitive to errors in modal parameters. As all VBDI processes rely on experimental data with their inherent uncertainties, statistical procedures are helpful if one is to interpret the vibration response mixed with other ambient affects. The objective of this study is the detection of damage by VBDI methods and statistical pattern recognition techniques. Here, two practical structures, the Crowchild Bridge in Calgary, and a 3D-Space Frame have been tested with two VBDI algorithms. The Damage Index and Matrix Update methods have been selected to study simulated damage cases on the numerical models of the selected structures. For the application of statistical pattern recognition techniques to damage identification, another in-service structure, the Portage Creek Bridge in Victoria, Canada has been tested. The classification of the patterns has been performed using outlier analysis. Alternatively, damage detection by pattern comparison using residual errors has been applied.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".