IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i17p10500-d895427.html
   My bibliography  Save this article

Rock Mass Classification Method Based on Entropy Weight–TOPSIS–Grey Correlation Analysis

Author

Listed:
  • Bing Dai

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China)

  • Danli Li

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China)

  • Lei Zhang

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China)

  • Yong Liu

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China
    School of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China)

  • Zhijun Zhang

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China)

  • Shirui Chen

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China)

Abstract

The accurate and reliable classification of rock mass is the basis of a reasonable engineering design. In the Xishan mining region of Sanshandao Gold Mine, three conventional rock mass classification methods of Tunneling Quality Index (Q), Rock Mass Rating (RMR) and China National Standard-basic quality (BQ), were compared in the burial depth area above 780 m, and it was discovered that the classification results of different rock mass classification methods had a low coincidence rate in the deep area; Therefore, this paper adopted entropy weight method, TOPSIS method and grey correlation analysis method to calculate the entropy weight and relative closeness of different methods in different middle sections. The study’s findings revealed that in the deep area, the relative closeness between each classification mass was: RMR > Q > BQ; Based on the above results, the IRMR method with modified RMR was selected for comprehensive analysis, and the concept of importance degree of evaluation index was defined; it was found that the importance degree of evaluation index of in-situ stress loss was the highest, while the importance degree of joint direction was the lowest; The “ETG” rock mass classification method based on “site-specific” is established, which provides a reference for the establishment of deep rock mass classification method.

Suggested Citation

  • Bing Dai & Danli Li & Lei Zhang & Yong Liu & Zhijun Zhang & Shirui Chen, 2022. "Rock Mass Classification Method Based on Entropy Weight–TOPSIS–Grey Correlation Analysis," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10500-:d:895427
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/17/10500/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/17/10500/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. M. L. Walker & Y. H. Dovoedo & S. Chakraborti & C. W. Hilton, 2018. "An Improved Boxplot for Univariate Data," The American Statistician, Taylor & Francis Journals, vol. 72(4), pages 348-353, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hongxing Deng & Wen Wen & Jie Zhou, 2023. "Competitiveness Evaluation of Express Delivery Enterprises Based on the Information Entropy and Gray Correlation Analysis," Sustainability, MDPI, vol. 15(16), pages 1-11, August.
    2. Jianjun Wang & Chicheng Ma & Sai Wang & Xiaojuan Lu & Dongyi Li, 2022. "Risk Assessment Model and Sensitivity Analysis of Ordinary Arterial Highways Based on RSR–CRITIC–LVSSM–EFAST," Sustainability, MDPI, vol. 14(23), pages 1-19, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhu, Bangzhu & Wan, Chunzhuo & Wang, Ping, 2022. "Interval forecasting of carbon price: A novel multiscale ensemble forecasting approach," Energy Economics, Elsevier, vol. 115(C).

    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:14:y:2022:i:17:p:10500-:d:895427. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.