Real-Time Prediction of Seepage Field during Tunnel Excavation

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Selecting accurate hydraulic conductivity is critical to calculating correctly seepage field. Because of geotechnical hydraulic conductivity with spatial randomness and its errors in measurement and computation, the extended Kalman filter (EKF) is proposed to its back analysis. In conjunction with the finite element method (FEM) of seepage, details of the EKF are introduced in this paper. With FORTRAN programs, the hydraulic conductivity is revised according to the real-time monitoring data during tunnel excavation, and illustrates the EKF merits of considerable convergence and tolerable accuracy.

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11-16

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January 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] R. E. Kalman, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Eng. (ASME), 82D(1960), pp.35-46.

Google Scholar

[2] Fossen Ti, Perez T, Kalman Filtering for Positioning and Heading Control of Ships and Offshore Rigs Estimating The Effects of Waves, Wind, and Current, IEEE Control Systems Magazine, Vol. 29(2009), pp.32-46.

DOI: 10.1109/mcs.2009.934408

Google Scholar

[3] Volynskii Ma, Gurov Ip, Zakharov As, Dynamic Analysis of Ahe Signals in Optical Coherent Tomography by The Method of Nonlinear Kalman filtering, Journal of Optical Technology, Vol. 75(2008), pp.682-686.

DOI: 10.1364/jot.75.000682

Google Scholar

[4] Schneebeli M, Matzler C, A Calibration Scheme for Microwave Radiometers Using Tipping Curves and Kalman Filtering , IEEE Transactions on Geoscience and Remote Sensing, Vol. 47(2009), pp.4201-4209.

DOI: 10.1109/tgrs.2009.2023784

Google Scholar

[5] Jiang shu-ping, Cai zhi-wei, Lin zhi, et al. Kalman Filtering & FEM Coupling Back Analysis Method Considering Broken Zone and Its Application to Stability Analysis of Surrounding Rock, Rock and Soil Mechanics, Vol. 30(2009), pp.2529-2534.

Google Scholar

[6] Yang chen-gxiang, Feng xia-ting, and Chen bing-rui, Parameter Identification of Rock Rheological Model Based on Extended Kalman Filter, Chinese Journal of Rock Mechanics and Engineering, Vol. 26(2007), pp.754-761.

Google Scholar

[7] Jiang shu-ping, Coupling Algorithm of Extended Kalman Filter-FEM and Its Application to Tunnel Engineering, Chinese Journal of Geotechnical Engineering, Vol. 18(1996), p.11–19.

Google Scholar

[8] Zhao Hong-liang, Feng Xia-ting, Zhang Dong-xiao, et al. Spatial Variability of Geomechanical Parameter Estimation Via Ensemble Kalman Filter, Rock and Soil Mechanics, Vol. 28(2007), pp.2219-2223.

Google Scholar

[9] Lu Fu-min, Wang Shang-qing, and Li Jin, Application of Discrete KFM in Landslide Deformation Forecast, Advances in Science and Technology of Water Resources, Vol. 29(2009), pp.6-9.

Google Scholar

[10] Li Jie-bin, Kong Ling-jie, Application of BP Neural Network Based on Kalman Filtering to Dam Deformation Prediction, Journal of Geodesy and Geodynamics, Vol. 29(2009), pp.124-126.

Google Scholar

[11] Qin Yong-yuan, Zhang Hong-yue, and Wang Shu-hua, The Kalman Filter and integrated navigation, Xi'an: Northwestern Polytechnical University Press, (1998).

Google Scholar

[12] R S Bucy, Linear and Nonlinear Filtering, Proc. IEEE, Vol. 58(1970), pp.854-864.

DOI: 10.1109/proc.1970.7792

Google Scholar

[13] Y Sunahara, Stochastic Optimal Control for Non-linear Dynamical Systems under Noisy Observations, Automatica, Vol. 6(1970), pp.731-737.

DOI: 10.1016/0005-1098(70)90046-4

Google Scholar

[14] Song Wen-yao, Zhang Ya, The Kalman Filter, Bengjing: Science Press, (1991).

Google Scholar