The Inversion Analysis of the Reservoir Saturation Landslide's Permeability Coefficient

Abstract:

Determination of sliding body saturated permeability coefficient has an important significance for groundwater dynamic field, deformation and stability of reservoir landslide. Based on in-depth analysis of landslide geological characteristics, groundwater monitoring data, rainfall and reservoir water lever in Three Gorges Reservoir, it is found that reservoir water level is the main factor which affecting the groundwater dynamic field. According to two layers of sliding body, the saturated permeability coefficient test plan of two factors and three levels has been designed. Based on saturated-unsaturated seepage finite element and the reservoir water level fluctuation curve, groundwater saturation line has obtained under different combinations of parameters. Combined with genetic algorithm, an optimal neural network model was established to describe the nonlinear relationship between the sliding body saturated permeability coefficient and borehole water level. Then based on the monitoring data of borehole water level, the saturated permeability coefficient of two layers sliding body can be gotten. Finally, with the inversion parameters, it is found that the calculating water level results and the actual level of underground water are in good agreement. Results show that the inverse thought and method is feasible and effective, which can provide reference for the similar engineering.

• Periodical: Applied Mechanics and Materials (Volumes 490-491)
• Pages: 633-639
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.490-491.633
• Online since: January 2014
• Authors: Ya Jun Wang, Qi Wang, Yang Bing Cao
• Keywords: Evolutionary Neural Network, Inversion Analysis, Permeability Coefficient, Reservoir Landslide, Saturated-Unsaturated Seepage

References

[1] WEI Jinbing, DENG Jianhui, L. G. Tham et. al: Field tests of saturated and unsaturated hydraulic parameters of gravelly soil in Xietan landslide. Rock and Soil Mechanics. Vol. 28(2007), pp.327-330, 335. In Chinese.

[2] LI Yongle, LIU Cuiran, LIU Haining et. al: Testing study on permeability characteristics of unsaturated soil. Chinese Journal of Rock Mechanics and Engineering. Vol. 23 (2004), pp.3861-3865. In Chinese.

[3] XIE Jinzhuang, WU Xiling: Estimation of oil-water relative permeability curves from production logging data. Petroleum exploration and development. Vol. 31 (2004), pp.64-66. In Chinese.

[4] JIANG Xiaowei, Li Wan, WANG Xusheng et. al: Estimation of depth-dependent hydraulic conductivity and deformation modulus using RQD. Rock and Soil Mechanics. Vol. 30 (2009), pp.3163-3166, 3167. In Chinese.

[5] ZHENG Guilan, WANG Yuan, WANG Fei: Application of generalized Bayesian method to stochastic analysis of fissured rock permeability coefficient. Shuili xuebao. Vol. 39 (2008), pp.419-425. In Chinese.

[6] WANG Fuming, Li Miao, GUO Xuemang: Inverse analysis on seepage of dams based on fluid-solid coupling. Journal of hydroelectric engineering. Vol. 27 (2008), pp.60-64. In Chinese.

[7] WEI Jinbing, DENG Jianhui, GAO Chunyu et. al: Unsaturated seepage analysis and back analysis of permeability coefficient for Xietan landslide in Three Gorges Reservoir area. Rock and Soil Mechanics. Vol. 29 (2008), pp.2262-2266. In Chinese.

[8] LI Shucai, ZHAO Yan, XU Bangshu et. al: Study of determining permeability coefficient in water inrush numerical calculation of subsea tunnel. Rock and Soil Mechanics. Vol. 33 (2012), pp.1497-1512. In Chinese.

[9] WANG Kai, TANG Minggao, XU Qiang: Study on the Inversion Method of Selecting Seepage Parameter for Sanma Mountain Landslide. Yellow River. Vol. 35 (2013), pp.124-126. In Chinese.

[10] VAN GENUCHTEN M T. A closed-form equation for prediction the hydraulic conductivity of unsaturated soils. Soil Science Society of American Journal. Vol. 44 (1980). pp.892-898.

DOI: 10.2136/sssaj1980.03615995004400050002x