Water-Inflow Forecast of Submarine Tunnel Based on BP Neural Networks

Chen Cai; Tao Huang; Xun Li; Yun Zhen Li

doi:10.4028/www.scientific.net/AMM.90-93.2173

Paper Titles

Relation between Surrounding Rock Displacement and its Size of Shaft on its Self-Weight Body Force Condition
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Calculation and Analysis of the Tunnel Lining's Thermal Stress Field under High Temperature
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Application Research of Ground Penetrating Radar Detection on the Metal Structure in Tunnel Lining
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Two Dimensional Parametric Analysis of Time Variation Characteristics of Raft Foundation on Saturated Subsoil
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Water-Inflow Forecast of Submarine Tunnel Based on BP Neural Networks
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Study on Shock Absorption Measures of Highway Tunnel in Karst Areas
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Comparison of Micro-Pressure Wave Radiated Out of Tunnel Exit between Slab Track and Ballast Track
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Experimental Research on Mechanical Properties of the early-Age Shotcrete
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Study on the Dispersion Curves of Rayleigh Wave in Foundation of Obstacles
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 90-93Water-Inflow Forecast of Submarine Tunnel Based on...

Water-Inflow Forecast of Submarine Tunnel Based on BP Neural Networks

Abstract:

The submarine tunnel water-inflow question has many kinds of factor synthesis influences, has highly the complexity and the misalignment, This article used the BP neural network algorithm to establish the submarine tunnel welling up water volume forecast model and to carry on the computation analysis, The result indicated that this model restraining performance is good, the forecast precision is high and simple feasible. This method has provided a new mentality for the submarine tunnel welling up water volume's forecast.

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Periodical:

Applied Mechanics and Materials (Volumes 90-93)

Pages:

2173-2177

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.90-93.2173

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Online since:

September 2011

Authors:

Chen Cai, Tao Huang, Xun Li, Yun Zhen Li

Keywords:

BP Neural Network (BPNN), Submarine Tunnel, Welling up Water Volume

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References

[1] EISENSTEIN ZD. Large undersea tunnels and the progress of tunneling technology[J]. Tunneling and Underground Space Technology，1994，9(3)：283–292.

DOI: 10.1016/0886-7798(94)90054-x

Google Scholar

[2] NILSEN B, PALMSTROM A. Stability and water leakage of hard rock subsea tunnels[J]. Modern Tunneling Science and Technology, 2001: 497–502.

DOI: 10.1201/9780203746653-85

Google Scholar

[3] SEDKI A,OUAZAR D,MAZOUDI E El. Evolving neural network using real coded genetic algorithm for daily rainfall–runoff forecasting[J]. Expert Systems with Applications, 2009，36 (3): 4523–4527.

DOI: 10.1016/j.eswa.2008.05.024

Google Scholar

[4] L IPPONEN A , MANNINEN S , NIINI H , et al .Effect of water and geological factors on the long-term stability of f racture zones in the Paijanne Tunnel Finland : a case study[J ] . International Journal Of Rock Mechanics And Mining Sciences , 2005 ,42 (1) : 3212.

DOI: 10.1016/j.ijrmms.2004.05.006

Google Scholar

[5] JIN FAN,FAN JUN-BO,TAN YONG-DONG. neural network and neural computer Principles[M]. Chengdu: Southwest Jiaotong University Press,1991.

Google Scholar

[6] LIAN JIAN-FA,SHEN NAI-QI,ZHANG JIE-KUN. Research on surrounding rockevaluation of underground engineering based on extension method[J]. Chinese Journal of Rock Mechanics and Engineering,2004,23 (9):1450-1453.

Google Scholar