Abstract
The study of surrounding rock stability is always an important subject in the field of tunnel engineering research. After comprehensively analyzed the factors of tunnel surrounding rock stability, combined the strong nonlinear mapping capability of artificial neural network with the compatibility and development ability of extension theory, putting forward a method of forecasting surrounding rock’s stability based on the extension neural network.
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References
Wen, C.: Matter Element Model and Its Application. Science and Technology Press, Beijing (1994)
Shi, Z.: Neural Network. Science Press, Beijing (2009)
Zhou, Y., Qian, X., Zhang, J.: Survey and Research of Extension Neural Network. Application Research of Computers 27, 1–5 (2010)
Zhao, Y., Su, N.: Extension Design. Higer Education Press, Beijing (2010)
Sun, B., Xing, A., Zhang, J.: The Extension Neural Network Model Design and Implementation. Journal of Harbin Institute of Technology 38, 1156–1159 (2006)
Kang, Z., Feng, X., Zhou, H.: The Application of Extenics Theory based on AHP in the Underground Carven Rock Quality Evaluation. Chinese Journal of Rock Mechanics and Engineering, 3687–3693 (2006)
Zhou, M., Yang, Y.: Implementation of Extension Neural Network of Dimamond thinking. Systems Engineering-Theory & Practice 6, 123–126 (2000)
Li, K., Xu, J., Li, S., Tao, Y.: Evaluation of Slope Stability based on Extension Theory. Journal of Chongqing University of Architecture 29, 75–78 (2007)
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Long, X., Zhang, B., Ye, W. (2013). The Study of Rock Tunnel Stability Based on Extension Neural Network. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_17
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DOI: https://doi.org/10.1007/978-3-642-36669-7_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36668-0
Online ISBN: 978-3-642-36669-7
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