Computational and Risk Assessment Modeling of Bridge Inspection Data Enabled by Sensors and Monitoring Systems
Bibliographic record
Abstract
Aiming at the bridge monitoring system, some of the monitoring data are abnormal due to equipment failure and environmental impacts.In this paper, the time-frequency domain convolutional neural network method is applied to the calculation of monitoring data and the risk assessment of bridge structure.The data collected by the acceleration sensor is firstly sliced and sampled and visualized.Then wavelet analysis is used to preprocess the cluttered data, and Wigner-Ville distribution and Fast Fourier Transform are introduced to extract time-frequency features from the collected data.A convolutional neural network is proposed and the network is trained on dual channel images fusing time and frequency domain images.By analyzing the spectrogram and and time-frequency diagram of the bridge monitoring data, the method of this paper classifies the bridge health condition into three kinds: no disease, slight disease and disease, which can accurately determine the health condition of different bridges, and the assessment accuracy of the risk assessment model based on the fusion of time-frequency domain information reaches 97.78%, which indicates that the high efficiency and feasibility of the bridge inspection data computation and the risk assessment model in this paper can meet the actual engineering construction needs of bridge inspection.
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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 | 0.000 |
| 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 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".