Development and Performance Evaluation of a Double-Grating Temperature-Compensated Bolt for Accurate Strain Measurement
Bibliographic record
Abstract
Recently, the fiber Bragg grating (FBG)-based bolt has been proposed and attempted to apply to the monitoring of pit slope stability. However, the measurement performance of the FBG-embedded bolt is known to be influenced not only by strain but also by ambient temperature. This often makes it challenging, if not impossible, to obtain accurate deformation measurements of the bolt. To address this issue, this study developed a novel temperature-compensated double-grating bolt to achieve temperature compensation and accurate strain measurement. This bolt incorporates two FBG sensors installed using different techniques to achieve distinct temperature sensitivity and strain sensitivity coefficients. This unique setup successfully decouples temperature and strain effects in the host bolt by employing the double wavelength matrix method. This paper first details the decoupling principle of the developed bolt, followed by an experimental investigation into its effectiveness and feasibility for temperature compensation and strain measurement in a laboratory setting. The results confirm that the developed bolt provides a great possibility to accurately measure the strain despite significant variations in ambient temperature. This bolt shows promising potential to be used in the area of the monitoring for building structures, mining slopes, and other areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".