Optical Fiber Chloride Sensor for Health Monitoring of Structures in Cold Regions
Bibliographic record
Abstract
The ever-increasing need to maintain aging existing reinforced concrete structures, especially in severe winter environments, in good condition and a cost-effective manner arises mainly in structural health monitoring methods, where it is necessary to adopt approaches that allow early detection of corrosion. Chloride-induced corrosion is a significant durability issue in cold regions where de-icing salts are used. This paper presents an embedded optical fiber chemical sensor that detects reliable free chloride concentrations into concrete cover in a precise non-destructive manner. It is based on fluorescence measurements using a chloride-sensitive fluorescent calcium-alginate sol-gel. Optical fibers were not affected by environmental factors, ions presences, or cold climate. Nevertheless, the fluorescent chemical sensor showed sensitivity towards alkalinity, temperature, leaching, and photo-bleaching. These restraints were encountered by applying a successfully validated ratiometric fluorimetry approach. Also, the durability and long-term stability of the sensor were studied. This sensor detects low chloride concentrations in a range of 0.045–0.45 M present in pore solution. It demonstrates a robust behavior, and excellent long-term stability so that it can withstand harsh environments. Thus, this sensor could provide a new approach towards the rapid, simple, and non-destructive monitoring of the structural health for detected the onset of corrosion damage.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".