Vibration-Based Damage Detection of Bridges under Varying Temperature Effects Using Time-Series Analysis and Artificial Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Structural health monitoring (SHM) has become a very important research area for evaluating the performances of bridges. An important issue with continuous SHM and damage detection of bridges is the effects of temperature variations on the measurement data, which can produce bigger effects in the response than the damage itself. In this study, a sensor-clustering-based time-series analysis method integrated with artificial neural networks (ANNs) was employed for damage detection under temperature variations. The damage features obtained solely using the time-series-based damage-detection algorithm are very effective for damage assessment; however, they yield false positives and negatives when temperature variations are present. Therefore, ANNs were used to compensate the temperature effects on the damage features obtained from time-series analysis. This methodology is applied to a footbridge finite-element model in which 2,000 simulations with temperature effects and damage cases were conducted. Results reveal that the proposed method can successfully determine the existence, location, and relative severity of damage using output-only vibration and temperature data even when temperature variations are present.
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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 | 0.001 |
| 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