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Pavement Roughness Grade Recognition Based on One-dimensional Residual Convolutional Neural Network.

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This study introduces a new method for real-time pavement roughness recognition using a lightweight residual convolutional network. The approach accurately classifies pavement conditions, improving road safety and vehicle performance.

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Area of Science:

  • Civil Engineering
  • Transportation Engineering
  • Machine Learning

Background:

  • Pavement roughness significantly impacts road service life and driving comfort.
  • Current pavement roughness recognition methods often suffer from complexity and low accuracy.

Purpose of the Study:

  • To propose a real-time pavement roughness recognition method.
  • To improve the accuracy and reduce computational effort of pavement roughness classification.

Main Methods:

  • Simulated vehicle vibration response data using a 1/4 vehicle model and a random input pavement model.
  • Employed a lightweight residual convolutional network (ResNet) for feature learning and classification of pavement roughness grades.
  • Utilized time-series acceleration data for real-time analysis.

Main Results:

  • The residual convolutional network demonstrated robust feature-capturing capabilities for vehicle vibration signals.
  • Achieved a high pavement roughness classification accuracy of 98.7%.
  • Significantly improved recognition accuracy while reducing computational load.

Conclusions:

  • The proposed method offers a fast and accurate solution for pavement roughness grade classification.
  • The lightweight ResNet is suitable for real-time applications in pavement condition assessment.
  • Enhanced accuracy and efficiency contribute to better road maintenance and driving experience.