Deep learning-based structural health monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article provides a comprehensive review of deep learning-based structural health monitoring (DL-based SHM). It encompasses a broad spectrum of DL theories and applications including nondestructive approaches; computer vision-based methods, digital twins, unmanned aerial vehicles (UAVs), and their integration with DL; vibration-based strategies including sensor fault and data recovery methods; and physics-informed DL approaches. Connections between traditional machine learning and DL-based methods as well as relations of local to global approaches including their extensive integrations are established. The state-of-the-art methods, including their advantages and limitations are presented. The review draws on current literature on the topic, also providing a synergistic analysis leading to the understanding of the evolution of DL as a basis for presenting the future research and development needs. Our overall finding is that despite the rapid progression of digital technology along with the progression of DL, the DL-based SHM appears to be in its infant stages with enormous potential for future developments to bring the SHM technology to a common practical use with wide scope applications, performance reliability, cost, and degree of automation. It is anticipated that this review paper will serve as a basic resource for readers seeking comprehensive and holistic understanding of the subject matter.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it