A Review of Passive Wireless Sensors for Structural Health Monitoring
Bibliographic record
Abstract
Wireless sensors for Structural Health Monitoring (SHM) is an emerging new technology that promises to overcome many disadvantages pertinent to conventional, wired sensors. The broad field of SHM has experienced significant growth over the past two decades, with several notable developments in the area of sensors such as piezoelectric sensors and optical fibre sensors. Although significant improvements have been made on damage monitoring techniques using these smart sensors, wiring remains a significant challenge to the practical implementation of these technologies. Wireless SHM has recently attracted the attention of researchers towards un-powered and more effective passive wireless sensors. This article presents a review of some of the underlying technologies in the field of wireless sensors for SHM - with a focus on the research progress towards the development of simple, powerless, yet effective and robust wireless damage detection sensors. This review examines the development of passive wireless sensors in two different categories: (1) use of oscillating circuits with the help of inductors, capacitors and resistors for damage detection; and (2) use of antennas, Radio Frequency Identification (RFID) tags and metamaterial resonators as strain sensors for wireless damage monitoring. An assessment of these electromagnetic techniques is presented and the key issues involved in their respective design configurations are discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".