Wavelet-Statistic-Frame with LSTM-Attention Network for Continuous Damage Identification and Localization in Bridge Vibration Signals
Bibliographic record
Abstract
To address the challenges of non-stationarity and multi-scale feature extraction in bridge vibration signals for structural health monitoring, this paper proposes a deep learning framework, termed Wavelet-Statistic-Frame with LSTM-Attention Network (WSF-LANet), that integrates multi-source feature extraction with temporal modeling to extract damagesensitive features from bridge vibration signals for identification and localization of different damage states.The model architecture is designed with three parallel feature extraction pathways: Discrete Wavelet Transform (DWT) based time-frequency analysis, extract statistical descriptors for quantifying latent damage indicators, and frame-wise segmentation and extraction of spatiotemporal features.After merging the features extracted from these three paths, a multi-attention block dynamically allocates weights across feature dimensions.The Long Short-Term Memory (LSTM) network is then used to further effectively capture the temporal dependencies of the sequence.The output is the final predicted damage matrix, which contains damage identification for each channel.In order to achieve both damage identification and localization functions, we additionally use unique heat encoding to represent multi-location and multi-category labels in a unified format.Experimental results show that on a bridge dataset from Japan, the proposed method achieves an accuracy of up to 97.5% for damage classification and a macro precision of 96.82% for localization.Ablation studies further validate the effectiveness of each feature extraction path.Crossdataset evaluations also demonstrate strong generalization capability.In summary, the proposed WSF-LANet offers an efficient, accurate, and generalizable solution for intelligent damage identification and localization in bridge structural health monitoring.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".