Damage detection of multi-girder bridge superstructure based on the modal strain approaches
Bibliographic record
Abstract
The research described in this paper focuses on the application of modal strain techniques on a multi-girder bridge superstructure with the objectives of identifying the presence of damage and detecting false damage diagnosis for such structures. The case study is a one-third scale model of a slab-on-girder composite bridge superstructure, comprised of a steel-free concrete deck with FRP rebars supported by four steel girders, similar to North Perimeter Red River Bridge in Winnipeg, Manitoba. The modal test data of the slab-on-girder specimen are analyzed by two mathematical methods and Mindlin approach. The Mindlin approach uses a small number of sensors and only the fundamental mode of vibration to obtain the modal strains. The unit-area normalization method produces a more precise damage patternbased on the Mindlin approach than the widely used unit-norm method and is thus a superior method for locating damage in multi-girder bridge structures. A new method is proposed to distinguish the false damage diagnosis that is common in multi-girder systems. Based on the invariant stress resultant theory and direct stiffness assumptions, a level III damage detection process is applied successfully to indicate damage localization and severity estimation of the damaged girder.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".