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Cited by 100Rock Mechanics and ModelingTunneling and Rock MechanicsGeomechanics and Mining Engineering

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Application of Gradient Boosting Machine Learning Algorithms to Predict Uniaxial Compressive Strength of Soft Sedimentary Rocks at Thar Coalfield

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Shahani, Niaz Muhammad, Kamran, Muhammad, Zheng, Xigui, Liu, Cancan, Guo, Xiaowei·2021·Advances in Civil Engineering
DOI 10.1155/2021/2565488
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Abstract

The uniaxial compressive strength (UCS) of rock is one of the essential data in engineering planning and design. Correctly testing UCS of rock to ensure its accuracy and authenticity is a prerequisite for assuring the design of any rock engineering project. UCS of rock has a broad range of applications in mining, geotechnical, petroleum, geomechanics, and other fields of engineering. The application of the gradient boosting machine learning algorithms has been rarely used, especially for UCS prediction, and has performed well, based on the relevant literature of the study. In this study, four gradient boosting machine learning algorithms, namely, gradient boosted regression (GBR), Catboost, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were developed to predict the UCS in MPa of soft sedimentary rocks of the Block‐IX at Thar Coalfield, Pakistan, using four input variables such as wet density (ρw) in g/cm 3; moisture in %; dry density (ρd) in g/cm 3; and Brazilian tensile strength (BTS) in MPa. Then, 106‐point dataset was allocated identically for each algorithm into 70% for the training phase and 30% for the testing phase. According to the results, the XGBoost algorithm outperformed the GBR, Catboost, and LightGBM with coefficient of correlation (R2) = 0.99, mean absolute error (MAE) = 0.00062, mean square error (MSE) = 0.0000006, and root mean square error (RMSE) = 0.00079 in the training phase and R2 = 0.99, MAE = 0.00054, MSE = 0.0000005, and RMSE = 0.00069 in the testing phase. The sensitivity analysis showed that BTS and ρw are positively correlated, and the moisture and ρd are negatively correlated with the UCS. Therefore, in this study, the XGBoost algorithm was shown to be the most accurate algorithm among all the investigated four algorithms for UCS prediction of soft sedimentary rocks of the Block‐IX at Thar Coalfield, Pakistan.

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Metadata

Authors
Shahani, N. M., Kamran, M., Zheng, X., Liu, C., & Guo, X.
Affiliations
  • China University of Mining and Technology (CN)
  • Bandung Institute of Technology (ID)
Corresponding author
Zheng, Xigui
ORCID
Venue
Advances in Civil Engineering
Publisher
Wiley
Published
2021-01
Volume
2021
Issue
1
ISSN
  • 1687-8086 (ISSN-L)
  • 1687-8094
DOI
10.1155/2021/2565488
OpenAlex
W3208381609
Cited by
100
References
1
Citations by year
2026: 19 · 2025: 25 · 2024: 29 · 2023: 20 · 2022: 7
FWCI
6.75
Type
journal article
Language
en
Open access
Licence
Locations
  • Advances in Civil Engineering (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
Science and Technology Innovation Project of Guizhou Province (Qiankehe Platform Talent [2019] 5620 to XZ)
Topics
  • Physical Sciences › Engineering › Mechanics of Materials › Rock Mechanics and Modeling
  • Physical Sciences › Engineering › Civil and Structural Engineering › Tunneling and Rock Mechanics
  • Physical Sciences › Engineering › Mechanics of Materials › Geomechanics and Mining Engineering
Keywords
uniaxial compressive strength, soft sedimentary rocks, XGBoost, gradient boosting regression, CatBoost, LightGBM, splitting tensile strength, rock density, moisture content, strength prediction

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