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Machine Learning-Based Intelligent Prediction of Elastic Modulus of Rocks at Thar Coalfield

Author

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  • 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 553004, China
    Guizhou Guineng Investment Co., Ltd., Liupanshui 553600, China)

  • Xiaowei Guo

    (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)

  • Xin Wei

    (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)

Abstract

Elastic modulus (E) is a key parameter in predicting the ability of a material to withstand pressure and plays a critical role in the design of rock engineering projects. E has broad applications in the stability of structures in mining, petroleum, geotechnical engineering, etc. E can be determined directly by conducting laboratory tests, which are time consuming, and require high-quality core samples and costly modern instruments. Thus, devising an indirect estimation method of E has promising prospects. In this study, six novel machine learning (ML)-based intelligent regression models, namely, light gradient boosting machine (LightGBM), support vector machine (SVM), Catboost, gradient boosted tree regressor (GBRT), random forest (RF), and extreme gradient boosting (XGBoost), were developed to predict the impacts of four input parameters, namely, wet density ( ρ wet ) in gm/cm 3 , moisture (%), dry density ( ρ d ) in gm/cm 3 , and Brazilian tensile strength (BTS) in MPa on output E (GPa). The associated strengths of every input and output were systematically measured employing a series of fundamental statistical investigation tools to categorize the most dominant and important input parameters. The actual dataset of E was split as 70% for the training and 30% for the testing for each model. In order to enhance the performance of each developed model, an iterative 5-fold cross-validation method was used. Therefore, based on the results of the study, the XGBoost model outperformed the other developed models with a higher accuracy, coefficient of determination ( R 2 = 0.999), mean absolute error (MAE = 0.0015), mean square error (MSE = 0.0008), root mean square error (RMSE = 0.0089), and a20-index = 0.996 of the test data. In addition, GBRT and RF have also shown high accuracy in predicting E with R 2 values of 0.988 and 0.989, respectively, but they can be used conditionally. Based on sensitivity analysis, all parameters were positively correlated, while BTS was the most influential parameter in predicting E. Using an ML-based intelligent approach, this study was able to provide alternative elucidations for predicting E with appropriate accuracy and run time at Thar coalfield, Pakistan.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3689-:d:776152
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    References listed on IDEAS

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    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    2. Ahmed Abdulhamid Mahmoud & Salaheldin Elkatatny & Abdulwahab Ali & Tamer Moussa, 2019. "Estimation of Static Young’s Modulus for Sandstone Formation Using Artificial Neural Networks," Energies, MDPI, vol. 12(11), pages 1-15, June.
    3. Weizhang Liang & Suizhi Luo & Guoyan Zhao & Hao Wu, 2020. "Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms," Mathematics, MDPI, vol. 8(5), pages 1-17, May.
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    Cited by:

    1. Muhammad Saqib Jan & Sajjad Hussain & Rida e Zahra & Muhammad Zaka Emad & Naseer Muhammad Khan & Zahid Ur Rehman & Kewang Cao & Saad S. Alarifi & Salim Raza & Saira Sherin & Muhammad Salman, 2023. "Appraisal of Different Artificial Intelligence Techniques for the Prediction of Marble Strength," Sustainability, MDPI, vol. 15(11), pages 1-24, May.
    2. Niaz Muhammad Shahani & Barkat Ullah & Kausar Sultan Shah & Fawad Ul Hassan & Rashid Ali & Mohamed Abdelghany Elkotb & Mohamed E. Ghoneim & Elsayed M. Tag-Eldin, 2022. "Predicting Angle of Internal Friction and Cohesion of Rocks Based on Machine Learning Algorithms," Mathematics, MDPI, vol. 10(20), pages 1-17, October.
    3. Galimzyanov, Bulat N. & Doronina, Maria A. & Mokshin, Anatolii V., 2023. "Machine learning-based prediction of elastic properties of amorphous metal alloys," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    4. 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.
    5. Bemah Ibrahim & Isaac Ahenkorah & Anthony Ewusi, 2022. "Explainable Risk Assessment of Rockbolts’ Failure in Underground Coal Mines Based on Categorical Gradient Boosting and SHapley Additive exPlanations (SHAP)," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    6. Yuzhen Wang & Mohammad Rezaei & Rini Asnida Abdullah & Mahdi Hasanipanah, 2023. "Developing Two Hybrid Algorithms for Predicting the Elastic Modulus of Intact Rocks," Sustainability, MDPI, vol. 15(5), pages 1-24, February.
    7. 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.
    8. Xiaohua Ding & Mehdi Jamei & Mahdi Hasanipanah & Rini Asnida Abdullah & Binh Nguyen Le, 2023. "Optimized Data-Driven Models for Prediction of Flyrock due to Blasting in Surface Mines," Sustainability, MDPI, vol. 15(10), pages 1-20, May.

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