Bridge Structural Damage Identification Using Causal-Invariant Spatio-Temporal Representation Learning
Bibliographic record
Abstract
Bridge structural health monitoring (SHM) based on vibration signals is strongly affected by variations in operating conditions, environmental disturbances, and limited labeled data.Under such circumstances, data-driven models tend to exploit condition-dependent correlations rather than damage-related mechanisms, which leads to unstable performance and limited generalization across different operating scenarios.To address this problem, a causal-invariant spatio-temporal representation learning (CISRL) framework is developed for bridge damage identification and localization.The framework integrates three components within a unified architecture: spatio-temporal feature extraction from multisensor vibration signals, graph-based modeling of structural damage propagation along the bridge topology, and cross-condition invariance regularization to suppress conditionspecific features.The invariance constraint guides the learning process toward representations that remain stable across different operating conditions while preserving sensitivity to structural damage.The proposed method is evaluated on the Japanese Old ADA bridge dataset and several public multivariate time-series datasets.The results show consistent improvements over existing deep learning approaches in both damage identification and localization tasks, particularly under cross-condition testing, small-sample training, and sensor layout variation.The findings indicate that incorporating causal invariance into spatio-temporal and graph-based learning provides a reliable and practical approach for robust structural damage identification in complex engineering environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".