Fiber Bragg grating (FBG)-based sensors: a review of technology and recent applications in structural health monitoring (SHM) of civil engineering structures
Bibliographic record
Abstract
Structural health monitoring (SHM) has become increasingly common in assessing built structures and the ongoing monitoring of older ones, whether for preventive maintenance applications or for assuring the integrity of deteriorating structures. Several sensing technologies have been developed and utilized for these applications. Over the past two decades, optical fiber sensors (OFS) have been utilized in various civil engineering projects due to their inherent advantages. This includes properties such as lightweight, size, immunity to electromagnetic interference (EMI), corrosion resistance, and embedding capability. Several monitoring systems based on OFS have been developed to measure and assess real-time data of various civil infrastructures continuously. Since its inception, Fiber Bragg grating (FBG) has been an ideal candidate for OFS technology; currently, most OFS systems use FBG. Due to its inherent characteristics and potential applications, considerable efforts and advancements have been made in the FBG-based sensing field. One of the most developed FBG sensors is strain OFS. Not just because of their benefits compared to the traditional strain gauge but also because of their high sensitivity and low cost. FBG strain sensors acquire an essential share of the SHM market. This review paper aims to give a general understanding of the basic principles of FBG sensors, advances in sensing and data processing techniques, developments of novel optical fiber sensors, temperature compensation techniques, and practical applications of FBG sensing technology in SHM. Moreover, this paper aims to contribute to the existing knowledge by highlighting the evolving landscape of FBG sensors and suggesting potential research areas in the context of SHM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".