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A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model

Author

Listed:
  • Guangliang Feng

    (State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
    These authors contributed equally to this work.)

  • Guoqing Xia

    (School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
    These authors contributed equally to this work.)

  • Bingrui Chen

    (State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China)

  • Yaxun Xiao

    (State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China)

  • Ruichen Zhou

    (Faculty of Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China)

Abstract

Hydropower is one of the most important renewable energy sources. However, the safe construction of hydropower stations is seriously affected by disasters like rockburst, which, in turn, restricts the sustainable development of hydropower energy. In this paper, a method for rockburst prediction in the deep tunnels of hydropower stations based on the use of real-time microseismic (MS) monitoring information and an optimized probabilistic neural network (PNN) model is proposed. The model consists of the mean impact value algorithm (MIVA), the modified firefly algorithm (MFA), and PNN (MIVA-MFA-PNN model). The MIVA is used to reduce the interference from redundant information in the multiple MS parameters in the input layer of the PNN. The MFA is used to optimize the parameter smoothing factor in the PNN and reduce the error caused by artificial determination. Three improvements are made in the MFA compared to the standard firefly algorithm. The proposed rockburst prediction method is tested by 93 rockburst cases with different intensities that occurred in parts of the deep diversion and drainage tunnels of the Jinping II hydropower station, China (with a maximum depth of 2525 m). The results show that the rates of correct rockburst prediction of the test samples and learning samples are 100% and 86.75%, respectively. However, when a common PNN model combined with monitored microseismicity is used, the related rates are only 80.0% and 61.45%, respectively. The proposed method can provide a reference for rockburst prediction in MS monitored deep tunnels of hydropower projects.

Suggested Citation

  • Guangliang Feng & Guoqing Xia & Bingrui Chen & Yaxun Xiao & Ruichen Zhou, 2019. "A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model," Sustainability, MDPI, vol. 11(11), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3212-:d:238506
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    References listed on IDEAS

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    Cited by:

    1. Jiachuang Wang & Haoji Ma & Xianhang Yan, 2023. "Rockburst Intensity Classification Prediction Based on Multi-Model Ensemble Learning Algorithms," Mathematics, MDPI, vol. 11(4), pages 1-29, February.
    2. Farzin Golzar & David Nilsson & Viktoria Martin, 2020. "Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    3. Chenxi Zhang & Diyuan Li & Shunchuan Wu & Long Chen & Jun Peng, 2021. "Study on Evolution Mechanism of Structure-Type Rockburst: Insights from Discrete Element Modeling," Sustainability, MDPI, vol. 13(14), pages 1-26, July.
    4. Lei Li & Yujiang Xie & Jingqiang Tan, 2020. "Application of Waveform Stacking Methods for Seismic Location at Multiple Scales," Energies, MDPI, vol. 13(18), pages 1-15, September.
    5. Weizhang Liang & Asli Sari & Guoyan Zhao & Stephen D. McKinnon & Hao Wu, 2020. "Short-term rockburst risk prediction using ensemble learning methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1923-1946, November.
    6. Guangliang Feng & Manqing Lin & Yang Yu & Yu Fu, 2020. "A Microseismicity-Based Method of Rockburst Intensity Warning in Deep Tunnels in the Initial Period of Microseismic Monitoring," Energies, MDPI, vol. 13(11), pages 1-15, May.
    7. Jianguo Zhang & Peitao Li & Xin Yin & Sheng Wang & Yuanguang Zhu, 2022. "Back Analysis of Surrounding Rock Parameters in Pingdingshan Mine Based on BP Neural Network Integrated Mind Evolutionary Algorithm," Mathematics, MDPI, vol. 10(10), pages 1-16, May.

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