A wireless embedded passive sensor for monitoring the corrosion potential of reinforcing steel
Bibliographic record
Abstract
Corrosion of reinforcing steel, which results in premature deterioration of reinforced concrete structures, is a worldwide problem. Most corrosion sensing techniques require some type of wired connection between the sensor and monitoring electronics. This causes significant problems in their installation and long-term use. In this paper we describe a new type of passive embeddable wireless sensor that is based on an LC coil resonator where the resonant frequency is changed by the corrosion potential of the reinforcing steel. The resonant frequency can be monitored remotely by an interrogator coil inductively coupled to the sensor coil. The sensor unit comprises an inductive coil connected in parallel with a voltage dependent capacitor (varactor) and a pair of corrosion electrodes consisting of a reinforcing steel sensing electrode and a stainless steel reference electrode. Change of potential difference between the electrodes due to variation of the corrosion potential of the reinforcing steel changes the capacitance of the varactor and shifts the resonant frequency of the sensor. A time-domain gating method was used for the interrogation of the inductively coupled corrosion sensor. Results of an accelerated corrosion test using the sensor indicate that the corrosion potential can be monitored with a resolution of less than 10 mV. The sensor is simple in design and requires no power source, making it an inexpensive option for long-term remote monitoring of the corrosion state of reinforcing steel.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".