Application of vibration-based damage detection to an integral abutment bridge
Bibliographic record
Abstract
Vibration-based damage detection (VBDD) methods use changes to the dynamic characteristics of a structure (i.e. its natural frequencies, mode shapes, and damping properties) to detect the presence of damage and determine its location. The application of these methods to constructed civil engineering facilities is complicated by a number of factors unique to these structures. Despite the challenges, the development of reliable VBDD methods for constructed facilities has the potential for great benefit and cost savings to infrastructure owners. This paper focuses on the application of VBDD techniques based on changes to mode shapes to a two-span, slab-on-girder, integral abutment bridge in Saskatoon, Canada. The dynamic response of the bridge under ambient traffic loading has been measured periodically using temporarily installed accelerometers over a range of ambient temperatures. A detailed finite element (FE) model has been developed and calibrated to match the first three measured natural frequencies and mode shapes. This model was then used to simulate the dynamic response of the bridge as various states of small-scale damage were induced, and several VBDD techniques were applied to detect and locate the damage. Preliminary results show that the ambient temperature significantly influences measured natural frequencies. In addition, the presence and location of damage may be found using any of VBDD techniques. The performance of the techniques is influenced by the number of sensors used to characterize mode shapes, as well as by the procedures used to normalize the mode shapes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".