Road roughness acquisition and classification using improved restricted Boltzmann machine deep learning algorithm

Qinghua Liu, Lu Sun, Alain Kornhauser, Jiahui Sun, Nick Sangwa

Research output: Contribution to journalArticle

Abstract

Purpose: To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation algorithm for road roughness detection is presented in this paper. The developed measurement system, including hardware designs and algorithm for software, constitutes an independent system which is low-cost, convenient for installation and small. Design/methodology/approach: The inputs of restricted Boltzmann machine deep neural network are the vehicle vertical acceleration power spectrum and the pitch acceleration power spectrum, which is calculated using ADAMS finite element software. Adaboost Backward Propagation algorithm is used in each restricted Boltzmann machine deep neural network classification model for fine-tuning given its performance of global searching. The algorithm is first applied to road spectrum detection and experiments indicate that the algorithm is suitable for detecting pavement roughness. Findings: The detection rate of RBM deep neural network algorithm based on Adaboost Backward Propagation is up to 96 per cent, and the false positive rate is below 3.34 per cent. These indices are both better than the other supervised algorithms, which also performs better in extracting the intrinsic characteristics of data, and therefore improves the classification accuracy and classification quality. Additionally, the classification performance is optimized. The experimental results show that the algorithm can improve performance of restricted Boltzmann machine deep neural networks. The system can be used for detecting pavement roughness. Originality/value: This paper presents an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation for identifying the road roughness. Through the restricted Boltzmann machine, it completes pre-training and initializing sample weights. The entire neural network is fine-tuned through the Adaboost Backward Propagation algorithm, verifying the validity of the algorithm on the MNIST data set. A quarter vehicle model is used as the foundation, and the vertical acceleration spectrum of the vehicle center of mass and pitch acceleration spectrum were obtained by simulation in ADAMS as the input samples. The experimental results show that the improved algorithm has better optimization ability, improves the detection rate and can detect the road roughness more effectively.

Original languageEnglish (US)
Pages (from-to)733-742
Number of pages10
JournalSensor Review
Volume39
Issue number6
DOIs
StatePublished - Nov 18 2019

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Keywords

  • Acquisition system
  • Adaboost Backward Propagation algorithm
  • Data acquisition system
  • Deep learning
  • Deep neural network
  • Neural networks
  • Road engineering
  • Road roughness
  • Sensors

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