Mass normalized mode shape identification of bridge structures using a single actuator-sensor pair
Bibliographic record
Abstract
Identifying mode shapes of bridge structures typically require a dense array of stationary sensors to accurately capture mode shapes with appropriate spatial resolution. An alternative approach is developed here, which requires only a single pair of actuator and sensor. The mode shape identification involves, first, identifying the natural frequencies and modal damping ratios, followed by an estimation of the mass normalized mode shapes components at the excited and measured degrees of freedom. An input–output balance is employed with a series of inputs and outputs obtained from a sequence of tests. The sequence of tests include exciting and measuring at different locations along the bridge, using either a roving actuator and/or a roving sensor; the requirement for a unique identification is that the roving actuator and sensor must be collocated in at least one of the tests. The performance of the proposed method using different types of responses, namely, displacement, velocity, and acceleration, is assessed using numerical simulations. The effect of different types of errors in the identification process is also studied. The method is finally applied to experimental data obtained from laboratory scale tests.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".