IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p8424-d1152968.html
   My bibliography  Save this article

Optimized Data-Driven Models for Prediction of Flyrock due to Blasting in Surface Mines

Author

Listed:
  • Xiaohua Ding

    (School of Mines, China University of Mining and Technology, Xuzhou 221116, China
    State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China)

  • Mehdi Jamei

    (Faculty of Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Dashte Azadegan 78986, Iran
    New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah 64001, Iraq)

  • Mahdi Hasanipanah

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Rini Asnida Abdullah

    (Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Binh Nguyen Le

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    School of Engineering and Technology, Duy Tan University, Da Nang 550000, Vietnam)

Abstract

Using explosive material to fragment rock masses is a common and economical method in surface mines. Nevertheless, this method can lead to some environmental problems in the surrounding regions. Flyrock is one of the most dangerous effects induced by blasting which needs to be estimated to reduce the potential risk of damage. In other words, the minimization of flyrock can lead to sustainability of surroundings environment in blasting sites. To this aim, the present study develops several new hybrid models for predicting flyrock. The proposed models were based on a cascaded forward neural network (CFNN) trained by the Levenberg–Marquardt algorithm (LMA), and also the combination of least squares support vector machine (LSSVM) and three optimization algorithms, i.e., gravitational search algorithm (GSA), whale optimization algorithm (WOA), and artificial bee colony (ABC). To construct the models, a database collected from three granite quarry sites, located in Malaysia, was applied. The prediction values were then checked and evaluated using some statistical criteria. The results revealed that all proposed models were acceptable in predicting the flyrock. Among them, the LSSVM-WOA was a more robust model than the others and predicted the flyrock values with a high degree of accuracy.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8424-:d:1152968
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/8424/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/10/8424/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chia Yu Huat & Seyed Mohammad Hossein Moosavi & Ahmed Salih Mohammed & Danial Jahed Armaghani & Dmitrii Vladimirovich Ulrikh & Masoud Monjezi & Sai Hin Lai, 2021. "Factors Influencing Pile Friction Bearing Capacity: Proposing a Novel Procedure Based on Gradient Boosted Tree Technique," Sustainability, MDPI, vol. 13(21), pages 1-23, October.
    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.
    3. Naseer Muhammad Khan & Kewang Cao & Qiupeng Yuan & Mohd Hazizan Bin Mohd Hashim & Hafeezur Rehman & Sajjad Hussain & Muhammad Zaka Emad & Barkat Ullah & Kausar Sultan Shah & Sajid Khan, 2022. "Application of Machine Learning and Multivariate Statistics to Predict Uniaxial Compressive Strength and Static Young’s Modulus Using Physical Properties under Different Thermal Conditions," Sustainability, MDPI, vol. 14(16), pages 1-27, August.
    4. Xuesong Zhang & Biao He & Mohanad Muayad Sabri Sabri & Mohammed Al-Bahrani & Dmitrii Vladimirovich Ulrikh, 2022. "Soil Liquefaction Prediction Based on Bayesian Optimization and Support Vector Machines," Sustainability, MDPI, vol. 14(19), pages 1-15, September.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Cuiying Zhou & Jinwu Ouyang & Zhen Liu & Lihai Zhang, 2022. "Early Risk Warning of Highway Soft Rock Slope Group Using Fuzzy-Based Machine Learning," Sustainability, MDPI, vol. 14(6), pages 1-28, March.
    3. 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.
    4. Jinrui Zhang & Chuanqi Li & Tingting Zhang, 2023. "An Assessment of the Mobility of Toxic Elements in Coal Fly Ash Using the Featured BPNN Model," Sustainability, MDPI, vol. 15(23), pages 1-18, November.
    5. 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.
    6. Sajjad Hussain & Naseer Muhammad Khan & Muhammad Zaka Emad & Abdul Muntaqim Naji & Kewang Cao & Qiangqiang Gao & Zahid Ur Rehman & Salim Raza & Ruoyu Cui & Muhammad Salman & Saad S. Alarifi, 2022. "An Appropriate Model for the Prediction of Rock Mass Deformation Modulus among Various Artificial Intelligence Models," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    7. Naseer Muhammad Khan & Kewang Cao & Muhammad Zaka Emad & Sajjad Hussain & Hafeezur Rehman & Kausar Sultan Shah & Faheem Ur Rehman & Aamir Muhammad, 2022. "Development of Predictive Models for Determination of the Extent of Damage in Granite Caused by Thermal Treatment and Cooling Conditions Using Artificial Intelligence," Mathematics, MDPI, vol. 10(16), pages 1-22, August.
    8. 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.
    9. 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.
    10. 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.
    11. 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.

    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:10:p:8424-:d:1152968. 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.