IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1067-d1027214.html
   My bibliography  Save this article

Compaction Uniformity Evaluation of Subgrade in Highway Based on Principal Components Analysis and Back Propagation Neural Networks

Author

Listed:
  • Changchun Xu

    (Zhong Jiao Jian Ji Jiao Highway Investment Development Co., Ltd., Shijiazhuang 050043, China)

  • Ting Li

    (School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

  • Xujia Li

    (School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

  • Guangqing Yang

    (School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
    State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

Abstract

This paper proposes a comprehensive method for the compaction uniformity evaluation of subgrade in highways based on the principle components analysis and BP neural network. A field test on resilient and Young’s moduli of subgrade during compaction is performed on Zun-Qin highway. The moduli representing the compaction uniformity are the key factors in the principal component analysis, and the components are used as input in Back Propagation (BP) neural networks. The degree of variation and synthesis score of moduli in three subgrade sections are discussed, and the results show that the comprehensive method has a good performance in evaluating the compaction uniformity of the subgrade. The insight from this study provides a novel evaluation method and incites a better understanding of the compaction uniformity of subgrade in highways.

Suggested Citation

  • Changchun Xu & Ting Li & Xujia Li & Guangqing Yang, 2023. "Compaction Uniformity Evaluation of Subgrade in Highway Based on Principal Components Analysis and Back Propagation Neural Networks," Sustainability, MDPI, vol. 15(2), pages 1-9, January.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1067-:d:1027214
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1067/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/2/1067/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1067-:d:1027214. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.