IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i6p3689-d776152.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/6/3689/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/6/3689/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    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.
    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. 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.

    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. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    2. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    3. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    4. Nahushananda Chakravarthy H G & Karthik M Seenappa & Sujay Raghavendra Naganna & Dayananda Pruthviraja, 2023. "Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    5. Tim Voigt & Martin Kohlhase & Oliver Nelles, 2021. "Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge," Mathematics, MDPI, vol. 9(19), pages 1-26, October.
    6. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
    7. Zhu, Haibin & Bai, Lu & He, Lidan & Liu, Zhi, 2023. "Forecasting realized volatility with machine learning: Panel data perspective," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 251-271.
    8. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    9. Zhang, Ning & Li, Zhiying & Zou, Xun & Quiring, Steven M., 2019. "Comparison of three short-term load forecast models in Southern California," Energy, Elsevier, vol. 189(C).
    10. Smyl, Slawek & Hua, N. Grace, 2019. "Machine learning methods for GEFCom2017 probabilistic load forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1424-1431.
    11. Barzin,Samira & Avner,Paolo & Maruyama Rentschler,Jun Erik & O’Clery,Neave, 2022. "Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping Countries," Policy Research Working Paper Series 9979, The World Bank.
    12. Eike Emrich & Christian Pierdzioch, 2016. "Volunteering, Match Quality, and Internet Use," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 136(2), pages 199-226.
    13. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    14. Zhu, Siying & Zhu, Feng, 2019. "Cycling comfort evaluation with instrumented probe bicycle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 217-231.
    15. Catherine Ikae & Jacques Savoy, 2022. "Gender identification on Twitter," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 58-69, January.
    16. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
    17. Martijn Kagie & Michiel Van Wezel, 2007. "Hedonic price models and indices based on boosting applied to the Dutch housing market," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(3‐4), pages 85-106, July.
    18. Matthias Bogaert & Michel Ballings & Dirk Van den Poel, 2018. "Evaluating the importance of different communication types in romantic tie prediction on social media," Annals of Operations Research, Springer, vol. 263(1), pages 501-527, April.
    19. Dursun Delen & Hamed M. Zolbanin & Durand Crosby & David Wright, 2021. "To imprison or not to imprison: an analytics model for drug courts," Annals of Operations Research, Springer, vol. 303(1), pages 101-124, August.
    20. Doruk Cengiz & Arindrajit Dube & Attila S. Lindner & David Zentler-Munro, 2021. "Seeing Beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes," NBER Working Papers 28399, National Bureau of Economic Research, Inc.

    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:6:p:3689-:d:776152. 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.