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Intelligent Identification of Micro-NPR Bolt Shear Deformation Based on Modular Convolutional Neural Network.

Guang Han1,2,3, Chen Shang1,2, Zhigang Tao3

  • 1Hebei Provincial Collaborative Innovation Center of Transportation Power Grid Intelligent Integration Technology and Equipment, Shijiazhuang Tiedao University, Shijiazhuang 050043, China.

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Micro-Negative Poisson Ratio (Micro-NPR) bolts enhance slope stability. Stress wave non-destructive testing combined with a convolutional neural network accurately detects shear deformation in these advanced bolt anchoring systems.

Keywords:
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Area of Science:

  • Geotechnical Engineering
  • Materials Science
  • Non-Destructive Testing

Background:

  • Slope instability is a critical issue in civil engineering.
  • Conventional bolts have limitations in mitigating large deformations.
  • Micro-Negative Poisson Ratio (Micro-NPR) bolts offer improved performance.

Purpose of the Study:

  • To develop and validate a non-destructive testing method for Micro-NPR bolt anchoring systems.
  • To accurately detect shear deformation in slope support structures.
  • To enhance the quality inspection of bolt anchoring systems.

Main Methods:

  • Utilized stress wave non-destructive detection technology.
  • Employed a modular convolutional neural network (CNN) approach.
  • Integrated shear angle and shear location identification sub-modules.

Main Results:

  • Successfully identified shear deformation in Micro-NPR bolt anchoring systems.
  • The integrated CNN approach significantly improved detection accuracy.
  • Demonstrated the effectiveness of time-domain signal characteristics for defect detection.

Conclusions:

  • The combined stress wave NDT and CNN method is effective for detecting shear deformation in Micro-NPR bolt anchoring systems.
  • This technology aids in quality inspection and ensures the stability of slope support.
  • The findings will benefit future engineering applications in geotechnical stability.