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Artificial Neural Network for Prediction of Rockburst in Deep-Buried Long Tunnel

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Book cover Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3498))

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Abstract

Rockburst is a main engineering geological problems in deep-buried long tunnels. Rockburst phenomena have been analyzed, assumptions and criteria have been presented from the perspectives of strength, stiffness, energy, steadiness, fracture, damage, fractal and catastrophe, etc. Considering individual factors only among some assumptions and criteria will cause unilateral and limited results. Based on the assumptions and criteria of rockburst and real examples of tunnel engineering, a neural network model is proposed to predict rockburst. The prediction results show that it is feasible and valid to use artificial neural network for predicting rockburst.

Funded by National Natural Science Foundation Project (No.50334060).

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References

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Author information

Authors and Affiliations

  1. Key Lab of Ministry of Education for the Exploitation of Southwest Resources &, the Environmental Disaster Control Engineering, College of Resources &, Environmental Sciences, Chongqing University, Chongqing, 400044, China

    Xiaohong Li, Xinfei Wang, Yong Kang & Zheng He

Authors
  1. Xiaohong Li
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  2. Xinfei Wang
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  3. Yong Kang
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  4. Zheng He
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Li, X., Wang, X., Kang, Y., He, Z. (2005). Artificial Neural Network for Prediction of Rockburst in Deep-Buried Long Tunnel. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_155

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  • DOI: https://doi.org/10.1007/11427469_155

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25914-5

  • Online ISBN: 978-3-540-32069-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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