Journal of Mechanical Engineering >

2018 , Vol. 54 >Issue 10: 167 - 172

DOI: https://doi.org/10.3901/JME.2018.10.167

Research on Shield Cutting Tool Configuration Based on PSO-BP Neural Network

  • NIU Jiangchuan ,
  • HAN Litao ,
  • LI Sujuan ,
  • GUO Jingbo ,
  • LIU Jinzhi
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  • 1. School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043;
    2. School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043

Received date: 2017-08-28

  Revised date: 2018-02-05

  Online published: 2018-05-20

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Abstract

Shield cutting tools play a key role in the process of shield machine tunneling, and its reasonable configuration selection affects the success of the project. For the reasonable configuration and the geological applicability of shield cutting tools, an intelligent configuration method based on PSO-BP neural network hybrid algorithm is put forward, where the configuration principle of shield cutting tools is considered. The successful shield construction cases are used as sample data to train the relationship model, which is established between geological conditions and the types of shield cutting tools. And the trained model can achieve intelligent recommendation for the configuration scheme of shield cutting tools. The trained model is tested by engineering case, and the test result is compared with the actual configuration scheme. The test results show that, the PSO-BP neural network algorithm can not only achieve the reasonable recommendation on configuration scheme of shield cutting tools, but also it has significant improvement compared with the BP neural network algorithm in two aspects of calculation accuracy and training time.

Key words： shield cutting tool; particle swarm optimization; BP neural network; intelligent selection

Cite this article

NIU Jiangchuan , HAN Litao , LI Sujuan , GUO Jingbo , LIU Jinzhi . Research on Shield Cutting Tool Configuration Based on PSO-BP Neural Network[J]. Journal of Mechanical Engineering, 2018 , 54(10) : 167 -172 . DOI: 10.3901/JME.2018.10.167

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