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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© 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)