Machine Learning-Based Digital Twin Framework for Realistic Guided Wave Signal Generation, Applied to Reliability Assessment and Global Sensitivity Analysis in SHM
Bibliographic record
Abstract
Abstract An ultrasonic guided wave-based structural health monitoring system has potential applications in mainy domains such as, the oil and gas industry, civil engineering, and aerospace. However, there are some inherent challenges, such as the sensitivity of the Guided Waves (GW) to environmental and operational conditions (EOCs), defect(s) size and location, and sensor(s) placement. Therefore, the reliability of detection systems based on GW requires validation. Simulation tools are often used to study the impact of the above-mentioned factors. However, the computational burden associated to extensive simulation campaigns is excessive. To increase the computational efficiency, this work proposes a machine learning-based Digital Twin (DT) framework. More specifically, the DT framework employed in this paper comprises a linear dimensionality reduction algorithm and fully connected neural networks that work as a metamodel. The performance of the DT is evaluated on a simulation configuration of Aluminum with uncertainties in instrumentation and damage size. The simulation data required for training are obtained from CIVA simulation platform. The predicted signals from the DT are quantified using misfit-based criteria targeting amplitude and phase aspects based on time-frequency transformation. The assessment of the results suggests that DT has captured all the dynamics of the signals, and the predicted signals are in good agreement with the simulated ones. Furthermore, the developed DT has been employed to efficiently carry out the probability of detection study for reliability assessment and sensitivity analysis based on the propagation of uncertainties.
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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) | 0.001 | 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".