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Predictive Modeling of the Uniaxial Compressive Strength of Rocks Using an Artificial Neural Network Approach

Author

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  • Xin Wei

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China)

  • Niaz Muhammad Shahani

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    The State Key Laboratory for Geo Mechanics and Deep Underground Engineering, China University of Mining & Technology, Xuzhou 221116, China)

  • Xigui Zheng

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    The State Key Laboratory for Geo Mechanics and Deep Underground Engineering, China University of Mining & Technology, Xuzhou 221116, China
    School of Mines and Civil Engineering, Liupanshui Normal University, Liupanshui 553001, China
    Guizhou Guineng Investment Co., Ltd., Liupanshui 553001, China)

Abstract

Sedimentary rocks provide information on previous environments on the surface of the Earth. As a result, they are the principal narrators of the former climate, life, and important events on the surface of the Earth. The complexity and cost of direct destructive laboratory tests adversely affect the data scarcity problem, making the development of intelligent indirect methods an integral step in attempts to address the problem faced by rock engineering projects. This study established an artificial neural network (ANN) approach to predict the uniaxial compressive strength (UCS) in MPa of sedimentary rocks using different input parameters; i.e., dry density ( ρ d ) in g/cm 3 , Brazilian tensile strength (BTS) in MPa, and wet density ( ρ wet ) in g/cm 3 . The developed ANN models, M1, M2, and M3, were divided as follows: the overall dataset, 70% training dataset and 30% testing dataset, and 60% training dataset and 40% testing dataset, respectively. In addition, multiple linear regression (MLR) was performed for comparison to the proposed ANN models to verify the accuracy of the predicted values. The performance indices were also calculated by estimating the established models. The predictive performance of the M2 ANN model in terms of the coefficient of determination ( R 2 ), root mean squared error ( RMSE ), variance accounts for ( VAF ), and a 20-index was 0.831, 0.27672, 0.92, and 0.80, respectively, in the testing dataset, revealing ideal results, thus it was proposed as the best-fit prediction model for UCS of sedimentary rocks at the Thar coalfield, Pakistan, among the models developed in this study. Moreover, by performing a sensitivity analysis, it was determined that BTS was the most influential parameter in predicting UCS.

Suggested Citation

  • Xin Wei & Niaz Muhammad Shahani & Xigui Zheng, 2023. "Predictive Modeling of the Uniaxial Compressive Strength of Rocks Using an Artificial Neural Network Approach," Mathematics, MDPI, vol. 11(7), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1650-:d:1110865
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    References listed on IDEAS

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    2. Niaz Muhammad Shahani & Xigui Zheng & Xiaowei Guo & Xin Wei, 2022. "Machine Learning-Based Intelligent Prediction of Elastic Modulus of Rocks at Thar Coalfield," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
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    Cited by:

    1. Zhi Yu & Chuanqi Li & Jian Zhou, 2023. "Tunnel Boring Machine Performance Prediction Using Supervised Learning Method and Swarm Intelligence Algorithm," Mathematics, MDPI, vol. 11(20), pages 1-16, October.

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