Domain adaptation for structural health monitoring via physics-informed and self-attention-enhanced generative adversarial learning
Bibliographic record
Abstract
Health monitoring technologies, empowered by sensor-driven information and model updating, play an important role in assessing the status of civil structures and detecting anomalies. However, significant domain discrepancies exist in the distribution of physical parameters between numerical structural models and their real-world counterparts. As a result, many health monitoring theories struggle to be effective in practice, even if they perform perfectly in the simulated data. To bridge the domain discrepancies, this paper proposes an unsupervised domain adaptation approach based on an adapted cycle-consistent generative adversarial network (CycleGAN) that incorporates physical constraints and a self-attention mechanism. The approach focuses on the mutual transformation of the multi-channel time series obtained from numerical models and actual structures. Specifically, the physical constraints are derived from the governing equation of linear dynamic systems, while the self-attention mechanism is achieved by adding transformer structures to both the generator and discriminator. Through free-vibration experiments on a steel beam and a large-scale steel bridge model, the physical constraints and transformer structures have proven beneficial for improving the learning capability and training stability of the GAN model. Furthermore, the proposed approach is not only verified as effective in transforming acceleration responses between the test structures and their corresponding finite element models in both time and frequency domains but has also been shown to reproduce mode shapes accurately.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".