Vibration-Based Structural Damage Identification under Varying Temperature Effects
Why this work is in the frame
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Bibliographic record
Abstract
Vibration-based methods are promising for damage identification; however, their capabilities for damage identification under temperature variations are usually limited. In the paper, a vibration-based nondestructive global damage identification method based on a genetic algorithm (GA) is proposed to identify structural damage location and severity under the influence of temperature variation and noise. The proposed method is verified by a number of damage scenarios of a three-span continuous beam and a two-span steel grid and shows good robustness under random noise levels. First, considering that the material properties of a structure and boundary conditions of a system are generally temperature-dependent, the relation between temperature and elastic and geometric stiffness matrices is introduced, and damage parameters along with temperature are defined as variables of the numeric model. Second, a GA is introduced where a new objective function with different weight coefficients, combined with frequencies and mode shape, is proposed and developed. Third, damage identification of a three-span continuous beam and a two-span steel grid under temperature variation is carried out numerically, considering changes of material properties and boundary conditions, and damage existence, location, and severity are accurately identified. Finally, it is shown that the proposed method is very robust even when the data are polluted with artificial noise.
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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 | 0.000 | 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) | 0.000 | 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 it