Improving robustness of damage imaging in model-based structural health monitoring of complex structures
Bibliographic record
Abstract
Model-based Structural Health Monitoring (SHM) approaches offer higher resolution in damage detection and characterization. However, the performance of damage imaging algorithms relies on the quality of the model and the proper knowledge of its parameters, which might prove challenging to obtain for complex structures. In this paper, models are first compared for ultrasonic guided wave generation by bonded piezoceramic (PZT) transducers, from the well-known pin-force model to analytical approaches taking into account the detailed interfacial shear stress under the PZT, and including an electro-mechanical hybrid model. Then, the modeling of guided wave propagation in complex structures is investigated, more specifically in composite structures, considering: (1) dependency of phase velocity and damping on the angle, (2) steering effect due to the anisotropy of the structure, and (3) full transducer dynamics. Validation of the models is conducted on isotropic and composite materials, by comparing amplitude curves and time domain signals with simulation results from Finite Element Models and with experimental measurements using a 3D laser Doppler vibrometer for principal and non-principal directions. Finally, the sensitivity of the damage imaging algorithms to variability in the model parameters is studied, and the benefit of identifying those parameters in-situ, prior to damage imaging, is demonstrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".