Published

2017-07-01

PNN-based Rockburst Prediction Model and Its Applications

Modelo de predicción de fractura de rocas basado en una red neuronal probabilística y sus aplicaciones

DOI:

https://doi.org/10.15446/esrj.v21n3.65216

Keywords:

Probabilistic neural network (PNN), Rockburst, Prediction (en)
Red Neuronal Probabilística, fracturamiento de rocas, predicción (es)

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Authors

  • Yu Zhou School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
  • Tingling Wang School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China

Rock burst is one of main engineering geological problems significantly threatening the safety of construction. Prediction of rock burst is always an important issue concerning the safety of workers and equipment in tunnels. In this paper, a novel PNN-based rock burst prediction model is proposed to determine whether rock burst will happen in the underground rock projects and how much the intensity of rock burst is. The probabilistic neural network (PNN) is developed based on Bayesian criteria of multivariate pattern classification. Because PNN has the advantages of low training complexity, high stability, quick convergence, and simple construction, it can be well applied in the prediction of rock burst. Some main control factors, such as rocks’ maximum tangential stress, rocks’ uniaxial compressive strength, rocks’ uniaxial tensile strength, and elastic energy index of rock are chosen as the characteristic vector of PNN. PNN model is obtained through training data sets of rock burst samples which come from underground rock project in domestic and abroad. Other samples are tested with the model. The testing results agree with the practical records. At the same time, two real-world applications are used to verify the proposed method. The results of prediction are same as the results of existing methods, just same as what happened in the scene, which verifies the effectiveness and applicability of our proposed work.

 

El fracturamiento o explosión de rocas es uno de los principales problemas en ingeniería geológica que amenaza significativamente la seguridad de una construcción. La predicción del fracturamiento de rocas es importante para la seguridad de los trabajadores y el equipamiento en túneles. En este artículo se propone un nuevo modelo de predicción de fracturamiento de rocas basado en una red neuronal probabilística (PNN por sus siglas en inglés) para determinar la posible ocurrencia e intensidad de uno de estos eventos en proyectos subterráneos. La PNN se desarrolló con base en un criterio Bayesiano para la clasificación multivariada de patrones. Debido a que la PNN tiene las ventajas de una menor complejidad de adiestramiento, estabilidad, rápida convergencia y simplicidad en su construcción, se puede adecuar en la predicción del fracturamiento de rocas. Algunos factores principales de control, como la fuerza máxima tangencial de rocas, la resistencia de compresión uniaxial, la fuerza de tensión uniaxial, y el índice de energía elástica de las rocas fueron escogidos como los vectores característicos de la PNN. El modelo se obtuvo a través del adiestramiento de datos sobre fracturamiento de rocas en proyectos subterráneos en diferentes localidades. Otras datos también se analizaron con el modelo. Los resultados de la evaluación se ajustan a los registros observados. Simultáneamente, se utilizaron dos aplicaciones prácticas para verificar el método propuesto. Los resultados de la predicción son similares a los de métodos existentes, un factor que además se presentó en las pruebas de campo, lo que demuestra la efectividad y la aplicabilidad de la metodología propuesta. 

References

Adeli, H. & Panakkat, A. (2009). A probabilistic neural network for earthquake magnitude prediction, Neural Network, 22, 1018-1024.

Adoko, A. C., Gokceoglu, C., Wu, L., & Zuo, Q. J. (2013). Knowledge-based and data-driven fuzzy modeling for rockburst prediction. International Journal of Rock Mechanics and Mining Sciences, 61, 86-95. DOI: https://doi.org/10.1016/j.ijrmms.2013.02.010

Ataa, S., Hazmi, I. R., & Samsudin, S. F. (2017). Insect’s visitation on melastoma malabathricum in UKM Bangi forest reserve. Environment Ecosystem Science, 1, 20-22.

Cai, S. J. Zhang, L. H., & Zhou, W. L. (2005). Research on prediction of rock burst in deep hard-rock mines. Journal of Safety Science and Technology, 1, 17-20.

Du, Z. J., Xu, M. G., & Liu, Z. P. (2006). Laboratory integrated evaluation method for engineering wall rock rock-burst. Gold, 11, 26-30.

He, Z., Li, X. H., Lu, Y. Y. (2008). Application of BP neural network to the prediction of rockburst in Tongyu Tunning. Chinese Journal of Underground Space and Engineering, 4, 494-498.

Hoek, E., & Brown, E. T. (1997). Practical estimates of rock mass strength, International Journal of Rock Mechanics and Mining Sciences, 34, 1165-1186.

Hou, F. L., Liu, X. M., & Wang, M. C. (1992). Re-analysis of rockburst mechanism and discussion on the gradation of the rockburst intensity. Proceedings of the Third National Conference on Rock Dynamics. Wuhan: Wuhan University of Mapping Technology Press, 448-457.

Jian, Z., Xibing, L., & Xiuzhi, S. (2012). Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Safety science, 50, 629-644.

Kidybinski, A. (1981). Bursting liability indices of coal. Journal of Rock Mechanics and Mining Sciences, 18, 295-304.

Li, D. Q., & Wang, L. G. (2009). Theory and technology of the large-scale mining in hard-rock and deep mine-A case study of Dongguashan copper mine. Beijing: Metallurgical Industry Press.

Mansurov, V. A. (2001). Prediction of rockbursts by analysis of induced seismicity data. International Journal of Rock Mechanics and Mining Sciences, 38, 893-901.

Rutkowski L, "Adaptive probabilistic neural networks for pattern classification in time-varying environment", IEEE Trans Neural Netw, 15 (2004): 811-827.

Specht, D. F. (1990). Probabilistic neural networks. Neural networks, 3, 109-118.

Song, T., Jamshidi, M. M., Lee, R. R. & Huang, M. (2007). A modified probabilistic neural network for partial volume segmentation in brain MR image. IEEE Transactions on Neural Networks, 18, 1424-1432.

Tang, B. Y. (2000). Rockburst Control using Distress Blasting. Ph.D. Dissertation. McGill University.

Tao, Z. Y. (1988). Support design of tunnels subjected to rockbursting. In: Romana (Ed.), ISRM International Symposium, Rock Mechanics and Power Plants, 407-411.

Thaldiri, N. H., Hanafiah, M. H., & Halim, A. A. (2017). Effect of modified micro-sand, poly-aluminium chloride and cationic polymer on coagulation-flocculation process of landfill leachate. Environment Ecosystem Science, 1, 17-19.

Turchaninov, I. A., Markov, G. A., Gzovsky, M.V., Kazikayev, D. M., Frenze, U. K., Batugin, S. A., & Chabdarova, U. I. (1972). State of stress in the upper part of the Earth’s crust based on direct measurements in mines and on tectonophysical and seismological studies. Physics of the Earth and Planetary Interiors, 6, 229-234. DOI: https://doi.org/10.1016/0031-9201(72)90005-2

Wang, X. Z., Aamir, R. & Ai-Min, F. (2015). Fuzziness based sample categorization for classifier performance improvement. Journal of Intelligent & Fuzzy Systems, 29, 1185-1196.

Wang, J., Zeng, Y., Xu, Y., & Feng, K. (2017). Analysis of the influence of tunnel portal section construction on slope stability. Geology, Ecology, and Landscapes, 1, 56-65.

Wang, Y. H., Li, W.D., & Li, Q. G., (1998). Method of fuzzy comprehensive evaluations for rockburst prediction. Chinese Journal of Rock Mechanics and Engineering, 15, 493-501.

Wu, D. X. & Yang, J. (2005). Prediction and countermeasure for rockburst in Cangling mountain highway tunnel. Chinese Journal of Rock Mechanics and Engineering, 24, 3965-3971.

Yang, T., Li, G. W. W. (2000). Study on rockburst prediction method based on the prior knowledge. Chinese Journal of Rock Mechanics and Engineering, 19, 429-431.

Zhang, X. Z. (2005). Prediction of rock burst at underground works based on artificial neural network. Yangtze River, 36, 17-18.

Zhang, Z. Y., Song, J. B., & Li, P. F. "Rock burst comprehensive forecasting method for the chamber group of underground power house", Advance in Earth Sciences, 19 (2004): 451-456 .

Zhen, S., & Gao, W. (2017). Geological tourist route planning of Henan province based on geological relics zoning. Geology, Ecology, and Landscapes, 1, 66-69.

How to Cite

APA

Zhou, Y. and Wang, T. (2017). PNN-based Rockburst Prediction Model and Its Applications. Earth Sciences Research Journal, 21(3), 141–146. https://doi.org/10.15446/esrj.v21n3.65216

ACM

[1]
Zhou, Y. and Wang, T. 2017. PNN-based Rockburst Prediction Model and Its Applications. Earth Sciences Research Journal. 21, 3 (Jul. 2017), 141–146. DOI:https://doi.org/10.15446/esrj.v21n3.65216.

ACS

(1)
Zhou, Y.; Wang, T. PNN-based Rockburst Prediction Model and Its Applications. Earth sci. res. j. 2017, 21, 141-146.

ABNT

ZHOU, Y.; WANG, T. PNN-based Rockburst Prediction Model and Its Applications. Earth Sciences Research Journal, [S. l.], v. 21, n. 3, p. 141–146, 2017. DOI: 10.15446/esrj.v21n3.65216. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/65216. Acesso em: 23 sep. 2024.

Chicago

Zhou, Yu, and Tingling Wang. 2017. “PNN-based Rockburst Prediction Model and Its Applications”. Earth Sciences Research Journal 21 (3):141-46. https://doi.org/10.15446/esrj.v21n3.65216.

Harvard

Zhou, Y. and Wang, T. (2017) “PNN-based Rockburst Prediction Model and Its Applications”, Earth Sciences Research Journal, 21(3), pp. 141–146. doi: 10.15446/esrj.v21n3.65216.

IEEE

[1]
Y. Zhou and T. Wang, “PNN-based Rockburst Prediction Model and Its Applications”, Earth sci. res. j., vol. 21, no. 3, pp. 141–146, Jul. 2017.

MLA

Zhou, Y., and T. Wang. “PNN-based Rockburst Prediction Model and Its Applications”. Earth Sciences Research Journal, vol. 21, no. 3, July 2017, pp. 141-6, doi:10.15446/esrj.v21n3.65216.

Turabian

Zhou, Yu, and Tingling Wang. “PNN-based Rockburst Prediction Model and Its Applications”. Earth Sciences Research Journal 21, no. 3 (July 1, 2017): 141–146. Accessed September 23, 2024. https://revistas.unal.edu.co/index.php/esrj/article/view/65216.

Vancouver

1.
Zhou Y, Wang T. PNN-based Rockburst Prediction Model and Its Applications. Earth sci. res. j. [Internet]. 2017 Jul. 1 [cited 2024 Sep. 23];21(3):141-6. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/65216

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2. Bing Ji, Fa Xie, Xinpei Wang, Shengquan He, Dazhao Song. (2020). Investigate Contribution of Multi-Microseismic Data to Rockburst Risk Prediction Using Support Vector Machine With Genetic Algorithm. IEEE Access, 8, p.58817. https://doi.org/10.1109/ACCESS.2020.2982366.

3. 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, 11(11), p.3212. https://doi.org/10.3390/su11113212.

4. Mahmood Ahmad, Herda Yati Katman, Ramez A. Al-Mansob, Feezan Ahmad, Muhammad Safdar, Arnold C. Alguno, Teddy Craciunescu. (2022). Prediction of Rockburst Intensity Grade in Deep Underground Excavation Using Adaptive Boosting Classifier. Complexity, 2022(1) https://doi.org/10.1155/2022/6156210.

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