CVA and ARMAV: Performance Comparison Over Real Data
Bibliographic record
Abstract
The comparison between two different identification techniques from control system theory is proposed inthis paper, together with two applications to real structures. The comparison concerns the well assessed Auto Regressive Moving Average Vector (ARMAV) procedure, widely used by the same authors for bridge monitoring, and Canonical Variate Analysis-Balanced Realisation (CVA-BR), recently applied in the field of bridge identification. Particular care has been devoted to the CVA-BR procedure: numerical simulations have first been performed in order to verify the identification capabilities when dealing with high modal density structures. In particular, a bridge-like structure has been simulated, whose modes shapes have been obtained via the building blocks method by D.J. Gorman, using random road profiles extracted from an isotropic distribution and different characteristics for the vehicles running over it. The real cases considered to test both procedures are a bridge under traffic excitation and a building subject to seismic excitation from the Italian earthquake in 1997. For both cases, the main advantage achieved by adopting these methods is the parameter extraction from output-only measurements (i.e., from the traffic and ground movement excitation), where the excitation is impossible to measure. The bridge considered here is a reinforced concrete deck supported by girders and stringers. It is non-symmetric, and the traffic is flowing on it along the two directions. To perform the test, six accelerometer set-ups have been chosen, three of them were kept in fixed positions for data correlation. The second application concerns earthquake data: an experimental campaign was carried out by the Italian National Seismic Survey during the seismic sequence involving the middle of Italy in September 1997. On this occasion, more than seventy aftershocks were monitored. The data records of a hospital building were kept, and form an example for this paper. The building was instrumented using up to sixteen force-balance accelerometers, positioned all over the structure and on the ground. The results obtained by using both procedures reveal that the CVA-BR needs longer data time records (which implies longer computing time) but allows the extraction of higher order modes with respect to the ARMAV technique. Furthermore, the CVA-BR method permits the setting of many quality indexes, allowing a global control on the overall identification procedure via stabilisation diagrams for eigenfrequencies, dampings and Modal Assurance Criterion(MAC) values.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".