Bibliographic record
Abstract
Vibration-based damage detection (VBDD) comprises a family of non-destructive testing methods in which changes to 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. The small scale damage was defined in this study as the removal of a concrete element from the top surface of the bridge deck, resembling the spalled clear cover of concrete deck of the overpass. Five different VBDD techniques were evaluated, including the Change in Mode Shape, Change in Flexibility, Change in Mode Shape Curvature, Change in Uniform Flexibility Curvature and Damage index methods. In addition, the influence of the size of damage, the orientation of damage geometry, sensor spacing (3 m, 5 m and 7.5 m), the approach used for mode shape normalization, and uncertainty in the measured mode shapes was investigated. It was found that localized damage could be reliably detected and located if the sensors were located within 3 m of the damage (the distance between adjacent girders) and if uncertainty in the mode shapes was attenuated through the use of a sufficient number of repeated trials. Furthermore, studies using a limited sensor installation that could be achieved without interrupting the flow of traffic indicated that small scale damage could be detected and potentially located using sensors that are placed well away from the damaged area, provided uncertainty in mode shape was attenuated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".