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A BHR Composite Network-Based Visualization Method for Deformation Risk Level of Underground Space.

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This study introduces a new method for visualizing underground space deformation risks. The BP-Hopfield-RGB network provides real-time visual alerts for dangerous zones, enhancing structural safety monitoring.

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Area of Science:

  • Geotechnical Engineering
  • Structural Health Monitoring
  • Computer Vision

Background:

  • Underground spaces face risks from deformation, necessitating effective monitoring.
  • Current methods may lack real-time visualization capabilities for deformation risks.

Purpose of the Study:

  • To develop a novel visualization processing method for underground space deformation risk.
  • To enable real-time dynamic indication of dangerous zones.

Main Methods:

  • A composite network integrating a BP neural network, Hopfield network, and RGB color space model (BHR network).
  • BP neural network integrates complex environmental factors.
  • Hopfield network automatically classifies dynamic monitoring data.
  • Real-time visualization of deformation risk levels using the RGB color space.

Main Results:

  • The BHR composite network effectively visualizes the deformation monitoring process in real time.
  • The method dynamically indicates dangerous zones within underground structures.
  • Experimental validation using ultrasonic omnidirectional sensors and benchmark datasets confirmed efficacy.

Conclusions:

  • The proposed BHR network offers a significant advancement in real-time deformation risk visualization for underground spaces.
  • This method enhances the ability to identify and respond to potential structural hazards promptly.