Bibliographic record
Abstract
This article describes an effort to develop low-cost, wireless sensors for detecting corrosion in reinforced concrete bridges. This sensor enhances the type and quality of information that can be obtained during a periodic inspection of a bridge. To minimize life-cycle costs and to maximize service life, the sensors do not include onboard processing capabilities or batteries. An external reader is used to interrogate and power the sensors in a wireless manner using inductively coupled magnetic fields. The sensors are designed to be embedded in the concrete during construction and interrogated using a reader coil on the surface of the concrete. The prototype corrosion sensor may be idealized as two resonant circuits. The frequencies of the two circuits are easily identified by measuring the phase of the impedance across the terminals of the reader coil. The lower frequency resonant circuit includes a sacrificial steel wire, which extends into the concrete and is exposed to the same levels of oxygen, moisture, and chlorides as the nearby reinforcement. As corrosion develops on the surface of the steel sensing wire, the resonant frequency of this circuit is lost, and only one resonant frequency is detected when the sensor is interrogated. If two resonant frequencies are detected when the sensor is interrogated, the likelihood of corrosion at that location is low. However, if one resonant frequency is detected, the likelihood that corrosion has initiated is high. The results of accelerated corrosion tests demonstrated that the prototype sensors provided valuable information about the condition of the embedded reinforcement before evidence of corrosion was visible. The prototype sensors also demonstrated that wireless transmission of data through reinforced concrete structural elements is possible. The initial expenditure associated with installing this type of wireless sensor is expected to be low, with the cost of materials needed to fabricate a single sensor being less than $2.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".