A Novel Strain Sensor for Reinforced Concrete Structures
Bibliographic record
Abstract
Abstract: Reinforced concrete (RC) is the most commonly used structural material in civil engineering applications. RC structures have long‐term service lives under normal loading conditions; however, overload caused by misuse or statistically remote events such as earthquakes may create damages that, if not detected in time, may eventually cause failure. Hence, it is important to monitor RC structures to take necessary precautions and save human lives. A long‐gauge strain (LGS) sensor has been developed to monitor these structures. While it has been developed mainly with concrete applications in mind, the new sensor can also be used in a variety of applications, including measuring strains in pipelines, steel structures, and the like. The proposed sensor system has a very low cost compared with the commercially available competing systems. Prototypes of the proposed strain sensors have been built and calibrated. Test results prove the accuracy, repeatability and reliability of the proposed strain sensor. When the LGS sensor was incorporated into a concrete beam there was very good agreement between the experimental measurement of strain using the LGS sensor when compared with two strain‐gauged parallel steel rebars in the same concrete beam.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".