Wireless strain sensor based on resonant RF cavities
Bibliographic record
Abstract
In this paper we describe a new type of strain sensor which can be embedded in civil structures for structural health monitoring applications. This strain sensor is a passive device that can be embedded in a structure and remotely interrogated using RF signals via an attached antenna. Such a sensor has the advantage of requiring no permanent physical connection between the sensor and the data acquisition system. The sensor is a metal coaxial cavity that can be embedded or bonded to the structure in which strain is to be measured. The design presented here exhibits a dominant mode of electromagnetic resonance for wavelengths two times its cavity length. When the material in which the sensor is embedded is strained, the strain will be reflected in changes in the sensor dimensions, and hence will cause a shift in the resonant frequency of the cavity. The resonant frequency, or shift therein, can be easily obtained by various methods. The acquired resonant frequency is then used to calculate the strain on the structure. The sensor presented in this paper operates at a frequency of approximately 2.4 GHz, and exhibits a shift in resonance of 2.4 kHz per microstrain. The sensor has been embedded in concrete test cylinders and interrogated using external antenna. Experimental results show a strain resolution of better than 1 microstrain with a bandwidth of 30 Hz on this sensor. We will also present results of different interrogation systems including a simple switched detector circuit, a gated detector circuit and also using lock in techniques. This new class of embeddable sensor will have application in monitoring the health of and assessing damage in civil structures.
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How this classification was reachedexpand
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".