Robust and efficient dual-graph neural networks for structural damage detection and localization
Bibliographic record
Abstract
Effective and timely detection of structural damage is crucial for maintaining the integrity, safety, and longevity of civil infrastructure systems. Traditional structural health monitoring techniques, especially vibration-based methods, often encounter limitations such as sensitivity to measurement noise and reliance on manual feature extraction. Moreover, advanced deep learning methods and single-graph models often struggle to accurately localize and assess damages of varying scales and positions within complex, high-dimensional structures. To address these limitations, this study introduces a novel dual-graph convolutional network approach integrating both spatial (sensor topology) and feature-based adjacency matrices. A one-dimensional convolutional neural network preprocessing module is also incorporated to enhance computational efficiency through effective feature extraction and data compression. Comprehensive evaluations were conducted on two established civil engineering benchmark datasets, the Leibniz University Test Structure for Monitoring (LUMO) and Qatar University Grandstand Simulator (QUGS). The proposed method achieved 98.8 % accuracy on LUMO and 99.9 % on QUGS. Robustness tests showed accuracies above 96.5 % (LUMO) and 97 % (QUGS), even at low signal-to-noise ratio (0 dB). CNN-based compression significantly reduced training time. For instance, training time dropped from 295.7 to 12.1 h (LUMO) and from 1712.85 to 148.24 h (QUGS), maintaining accuracy above 98 %. The proposed dual-graph approach thus offers substantial improvements in structural damage detection and localization for complex civil structures.
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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 | 0.000 | 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 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".