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Progressive Classifier Mechanism for Bridge Expansion Joint Health Status Monitoring System Based on Acoustic

Xulong Zhang1, Zihao Cheng2, Li Du1

  • 1School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China.

Sensors (Basel, Switzerland)
|June 10, 2023
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Summary

This study introduces an IoT system for bridge expansion joint health monitoring using acoustic signals. It achieves high accuracy in detecting and classifying joint failures, improving maintenance efficiency.

Keywords:
IoTacoustic sensorend-to-cloud coordinatedfault diagnose and classification

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Area of Science:

  • Civil Engineering
  • Structural Health Monitoring
  • Internet of Things (IoT)

Background:

  • Bridge expansion joints are critical infrastructure components requiring efficient maintenance.
  • Existing methods for monitoring expansion joint health often lack efficiency and rely on scarce failure data.
  • The integration of IoT technology offers a promising solution for real-time structural health monitoring.

Purpose of the Study:

  • To develop and evaluate a low-power, end-to-cloud coordinated IoT system for bridge expansion joint health monitoring.
  • To address the challenge of limited authentic data for bridge expansion joint failures by creating a simulation platform.
  • To propose a novel progressive two-level classifier for accurate fault detection and classification.

Main Methods:

  • Implementation of a low-power, end-to-cloud coordinated monitoring system analyzing acoustic signals.
  • Establishment of an expansion joint damage simulation data collection platform for annotated datasets.
  • Development of a two-level classification mechanism combining Automatic Peak Detection (AMPD) template matching and Variational Mode Decomposition (VMD)-based deep learning.

Main Results:

  • The simulation-based datasets validated the two-level algorithm's effectiveness.
  • The first-level edge-end template matching achieved a 93.3% fault detection rate.
  • The second-level cloud-based deep learning algorithm demonstrated a 98.4% classification accuracy.

Conclusions:

  • The proposed IoT system efficiently monitors bridge expansion joint health.
  • The developed two-level classifier significantly enhances fault detection and classification accuracy.
  • This approach offers a robust solution for proactive bridge maintenance and infrastructure longevity.