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Structural Instability-Enabled Mechanical Sensors Using Fiber Bragg Grating.

Pengcheng Jiao1,2, Yiwei Xie3, Shengnan Wu4

  • 1Institute of Port, Coastal and Offshore Engineering, Ocean College, Zhejiang University, Zhoushan 316021, China.

Materials (Basel, Switzerland)
|June 11, 2020
PubMed
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This summary is machine-generated.

This study introduces a new mechanical sensing method using 3D-printed cylinders and fiber Bragg gratings (FBG) for structural health monitoring (SHM). The technique detects structural instability to assess civil infrastructure conditions.

Area of Science:

  • Materials Science
  • Civil Engineering
  • Mechanical Engineering

Background:

  • Structural health monitoring (SHM) is crucial for assessing the condition of civil infrastructures.
  • Existing methods require robust sensing technologies for reliable data acquisition.
  • Detecting structural instability is key to understanding material and structural integrity.

Purpose of the Study:

  • To develop a novel mechanical sensing technique for SHM.
  • To utilize the structural instability of cylindrical cells detected by fiber Bragg grating (FBG).
  • To create a system for binary data analysis indicating structural conditions.

Main Methods:

  • Fabrication of cylindrical cells using 3D printing.
  • Coiling fiber Bragg grating (FBG) wires around cylinders to detect transverse deformation.
Keywords:
fiber Bragg grating (FBG)mechanical sensorspattern recognitionstructural health monitoring (SHM)structural instability

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  • Inducing structural instability under axial compression and validating force-displacement relations via numerical simulations.
  • Analyzing FBG wavelength variation against a predefined threshold to generate binary data (1 for variation > threshold, 0 for < threshold).
  • Applying a pattern recognition algorithm for data analysis.

    • Main Results:

    • Successful detection of structural instability in 3D-printed cylindrical cells using FBG.
    • Experimental force-displacement relations show satisfactory agreement with numerical simulations.
    • FBG wavelength variations were observed and successfully converted into binary data representing structural states.
    • The pattern recognition algorithm effectively translated FBG signals into actionable binary data.

    Conclusions:

    • The developed FBG-based sensing technique effectively utilizes structural instability for SHM.
    • The method provides a reliable way to assess structural conditions through binary data analysis.
    • Potential applications include wireless sensors for enhanced SHM in civil infrastructures.