Evidence map›Paper›PMID 32517349›Full record

ArticleMaterials (Basel, Switzerland)2020

Structural Instability-Enabled Mechanical Sensors Using Fiber Bragg Grating.

Pengcheng Jiao, Yiwei Xie, Shengnan Wu, Xinyu Liu

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Pengcheng JiaoInstitute of Port, Coastal and Offshore Engineering, Ocean College, Zhejiang University, Zhoushan 316021, China.
Yiwei XieCentre for Optical and Electromagnetic Research, State Key Laboratory for Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory for Sensing Technologies, Zhejiang University, Hangzhou 310058, China.
Shengnan WuCentre for Optical and Electromagnetic Research, National Engineering Research Center for Optical Instruments, Zhejiang Provincial Key Laboratory for Sensing Technologies, Zhejiang University, Hangzhou 310058, China.
Xinyu LiuCentre for Optical and Electromagnetic Research, State Key Laboratory for Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory for Sensing Technologies, Zhejiang University, Hangzhou 310058, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structural health monitoring (SHM) has been extensively used in civil infrastructures to assess structural condition and situation. Here, we develop a novel type of mechanical sensing technique using the structural instability of cylindrical cells detected by fiber Bragg grating (FBG). The cylinders are fabricated using a 3D printing technique, which are coiled by the FBG wires to detect the transverse deformation. Structural instability under axial compression is obtained in the experiments and the force-displacement relations are validated by the numerical simulations with satisfactory agreements. The wavelength variation of the FBG, caused by the structural instability, is observed and compared with the predefined threshold. Defining the variation larger than the threshold as "1" and smaller as "0", the pattern recognition algorithm is used to convert the FBG results into binary data, which can, therefore, be analyzed to indicate the structural conditions. In the end, we envision the potential applications of the reported sensing technique, such as wireless sensors for structural health monitoring (SHM) in civil infrastructures.

Indexed as

fiber Bragg grating (FBG)mechanical sensorspattern recognitionstructural health monitoring (SHM)structural instability

Identifiers

PMID32517349
PMCPMC7321629

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.