Damage detection for structural health monitoring using reinforcement and imitation learning
Bibliographic record
Abstract
Structural damages are responsible for expenses associated with maintaining the safety and serviceability of infrastructures. Detecting damages is difficult because they often develop over years affecting structural responses in orders of magnitudes smaller than external effects, such as temperature. When damage occurs, structural responses depart from a normal condition to an abnormal one, which is referred to as an anomaly. Existing anomaly detection methodologies lack a mechanism to quantify the probability of rightfully detecting anomalies as a function of the anomaly’s characteristics, e.g. duration and magnitude, and associate them with the severity of structural damages. This paper proposes a framework addressing these challenges by relying on Bayesian dynamic linear models as well as reinforcement and imitation learning approaches. The former allows separating the changes in the structural responses from the ones caused by external effects, while the latter two enable incorporating information obtained from the changes in the structural responses for detecting anomalies. The proposed methodologies are validated using measurements collected on three instrumented bridge spans in Canada. The results show a good performance of the methods proposed in detecting structural damages with different severity levels and lay the foundation for further applications for other civil infrastructures.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".