IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p5201-d1097949.html
   My bibliography  Save this article

Study on the Prediction of the Uniaxial Compressive Strength of Rock Based on the SSA-XGBoost Model

Author

Listed:
  • Bing Xu

    (School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China)

  • Youcheng Tan

    (School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China)

  • Weibang Sun

    (School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China)

  • Tianxing Ma

    (Ocean College, Zhejiang University, Zhoushan 316021, China)

  • Hengyu Liu

    (School of Resources and Safety Engineering, Central South University, Changsha 410083, China)

  • Daguo Wang

    (School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China)

Abstract

The uniaxial compressive strength of rock is one of the important parameters characterizing the properties of rock masses in geotechnical engineering. To quickly and accurately predict the uniaxial compressive strength of rock, a new SSA-XGBoost optimizer prediction model was produced to predict the uniaxial compressive strength of 290 rock samples. With four parameters, namely, porosity ( n ,%), Schmidt rebound number ( R n ), longitudinal wave velocity ( V p , m/s), and point load strength ( I s (50) , MPa) as input variables and uniaxial compressive strength (UCS, MPa) as the output variables, a prediction model of uniaxial compressive strength was built based on the SSA-XGBoost model. To verify the effectiveness of the SSA-XGBoost model, empirical formulas, XGBoost, SVM, RF, BPNN, KNN, PLSR, and other models were also established and compared with the SSA-XGBoost model. All models were evaluated using the root mean square error (RMSE), correlation coefficient ( R 2 ), mean absolute error (MAE), and variance interpretation (VAF). The results calculated by the SSA-XGBoost model ( R 2 = 0.84, RMSE = 19.85, MAE = 14.79, and VAF = 81.36), are the best among all prediction models. Therefore, the SSA-XGBoost model is the best model to predict the uniaxial compressive strength of rock, for the dataset tested.

Suggested Citation

  • Bing Xu & Youcheng Tan & Weibang Sun & Tianxing Ma & Hengyu Liu & Daguo Wang, 2023. "Study on the Prediction of the Uniaxial Compressive Strength of Rock Based on the SSA-XGBoost Model," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5201-:d:1097949
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/5201/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/6/5201/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Huihui Xie & Peng Lin & Jintao Kang & Chenyu Zhai & Yuchao Du, 2024. "Prediction Method of Rock Uniaxial Compressive Strength Based on Feature Optimization and SSA-XGBoost," Sustainability, MDPI, vol. 16(19), pages 1-20, September.
    2. Yutao Li & Chuanguo Jia & Hong Chen & Hongchen Su & Jiahao Chen & Duoduo Wang, 2023. "Machine Learning Assessment of Damage Grade for Post-Earthquake Buildings: A Three-Stage Approach Directly Handling Categorical Features," Sustainability, MDPI, vol. 15(18), pages 1-23, September.

    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:6:p:5201-:d:1097949. 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.