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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".