Assessment of vibration-based damage detection for an integral abutment bridge
Bibliographic record
Abstract
Vibration-based damage detection (VBDD) comprises a family of nondestructive testing methods in which changes to signature dynamic characteristics are used to track the condition of a structure. Although VBDD methods have been successfully applied to various mechanical systems and to simple beam-like structures, significant challenges remain in extending this technology to complex, spatially distributed structures such as bridges. In the present study, numerical simulations using a calibrated finite element model were used to investigate the use of VBDD methods to detect small-scale damage on a two-span, integral abutment overpass structure located in Saskatoon, Saskatchewan. Five different VBDD techniques were evaluated, as were the effects of sensor spacing, mode shape normalization, and uncertainty in the measured mode shapes. It was found that localized damage to the top concrete cover of the bridge deck could be reliably detected and located if the sensors were located sufficiently close to the damage and if uncertainty in the mode shapes was attenuated through the use of a sufficient number of repeated trials. Furthermore, preliminary studies indicate that it may be possible to detect damage using sensors that are placed well away from the damaged area.Key words: vibration-based damage detection, structural health monitoring, integral abutment bridge, numerical modelling, field testing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".