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Uniform Depth Channel Flow: Problem Solving01:18

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Related Experiment Video

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An Unsupervised Tunnel Damage Identification Method Based on Convolutional Variational Auto-Encoder and Wavelet

Yonglai Zhang1,2, Xiongyao Xie1,2, Hongqiao Li1,3

  • 1School of Civil Engineering, Tongji University, Shanghai 200092, China.

Sensors (Basel, Switzerland)
|March 26, 2022
PubMed
Summary

This study introduces a new algorithm using train vibrations to detect subway tunnel damage efficiently and affordably. The unsupervised novelty detection method achieved high accuracy in lab tests, offering practical value for infrastructure safety.

Keywords:
CVAEdamage detectiondynamic responsein-service trainlaboratory testrelative entropysubway tunnelwavelet packet energy

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Area of Science:

  • Civil Engineering
  • Structural Health Monitoring
  • Artificial Intelligence

Background:

  • Current subway tunnel damage identification relies on manual inspection and vibration monitoring, which are costly and inefficient.
  • Existing methods suffer from high expenses, limited operational duration, and low identification efficiency, posing risks of catastrophic accidents.

Purpose of the Study:

  • To develop a low-cost, highly efficient algorithm for automatic subway tunnel damage identification and localization.
  • To propose an unsupervised novelty detection method utilizing in-service train vibrations.

Main Methods:

  • An algorithm based on the vibration response of in-service trains and Weighted Probabilistic Ensemble with Conditional Variational Autoencoder (WPE-CVAE) was developed.
  • The method employs unsupervised novelty detection, requiring only normal structural data for training.
  • Laboratory model tests simulated damage (void behind tunnel wall) to validate the algorithm's performance.

Main Results:

  • The proposed algorithm achieved a 96.25% recall rate, 86.75% hit rate, and 91.5% accuracy in identifying tunnel damage.
  • The method demonstrated superior performance and noise immunity compared to other unsupervised approaches.
  • The algorithm successfully identified damage location and provided accurate assessments.

Conclusions:

  • The developed WPE-CVAE-based algorithm offers an effective and practical solution for subway tunnel damage identification.
  • The unsupervised approach significantly improves efficiency and reduces costs associated with traditional monitoring methods.
  • This technique holds considerable practical value for enhancing subway infrastructure safety and preventing accidents.