Damage detection on a steel-free bridge deck using random vibration
Bibliographic record
Abstract
Vibration-based damage detection (VBDD) methods utilize measured changes in the dynamic characteristics of structural systems (natural frequencies, mode shapes, and damping characteristics) to indicate the presence and location of damage. Previous studies have demonstrated that small-scale damage can be reliably located in simple bridge systems when resonant harmonic loading is used as the excitation source for the VBDD measurements. In full-scale bridge applications, however, random loading due to traffic or wind is often more readily achievable. A numerical study was therefore undertaken to investigate the use of random loading for damage detection in a simple-span, slab-on-girder bridge deck. Transient dynamic analyses of a finite element model of the bridge deck subjected to randomly varying loading were performed for nine different simulated small-scale damage states. To reduce the inherent uncertainty arising from the random loading, averaged results from a large number of repeated random trials were used. Several factors that may influence the probability of successfully locating the damage were investigated, including the number of repeated random trials used, the distance from the damage to the nearest sensor, the proximity of the damage to simple supports, the severity of the damage and the presence of random measurement error. It was found that a large number of repeated random trials was required to achieve reasonable probabilities of successfully locating the damage; even then, reliable detection results were not guaranteed for all of the damage conditions considered. Based on these results, therefore, random excitation appears to be less reliable in VBDD than harmonic loading.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".