scholarly journals Rock Mass Hydraulic Conductivity Estimated by Two Empirical Models

10.5772/15669 ◽  
2011 ◽  
Author(s):  
Shih-Meng Hsu ◽  
Hung-Chieh Lo ◽  
Shue-Yeong Chi ◽  
Cheng-Yu Ku
Keyword(s):  
Rock Mass ◽  
Hydraulic Conductivity ◽  
Empirical Models

Formulation of strain-dependent hydraulic conductivity for a fractured rock mass

2007 ◽  
Vol 44 (7) ◽  
pp. 981-996 ◽  
Author(s):  
Yifeng Chen ◽  
Chuangbing Zhou ◽  
Yongqing Sheng
Keyword(s):  
Rock Mass ◽  
Hydraulic Conductivity ◽  
Fractured Rock ◽  
Fractured Rock Mass ◽  
Strain Dependent

Discrete fracture network and equivalent hydraulic conductivity for tunnel seepage analysis in rock mass

2006 ◽  
Vol 21 (3-4) ◽  
pp. 403
Author(s):  
H.S. Lee ◽  
B.K. Son ◽  
Y.G. Kim ◽  
S.W. Jeon
Keyword(s):  
Rock Mass ◽  
Hydraulic Conductivity ◽  
Fracture Network ◽  
Discrete Fracture Network ◽  
Seepage Analysis ◽  
Discrete Fracture

A new equation for the equivalent hydraulic conductivity of rock mass around a tunnel

2012 ◽  
Vol 54 ◽  
pp. 125-128 ◽  
Author(s):  
Mahdi Zoorabadi ◽  
Buddhima Indraratna ◽  
Jan Nemcik
Keyword(s):  
Rock Mass ◽  
Hydraulic Conductivity ◽  
New Equation

Influences of Fracture Aperture and Roughness on Hydraulic Conductivity in Fractured Rock Mass

2011 ◽  
Author(s):  
J. P. Sun ◽  
Z. Y. Zhao ◽  
Jiachun Li ◽  
Song Fu
Keyword(s):  
Rock Mass ◽  
Hydraulic Conductivity ◽  
Fractured Rock ◽  
Fracture Aperture ◽  
Fractured Rock Mass

scholarly journals Soft Computing Based Prediction of Support Pressure in Tunnels

2019 ◽  
Vol 8 (6) ◽  
pp. 856-863 ◽  
Keyword(s):  
Neural Network ◽  
Rock Mass ◽  
Linear Regression Analysis ◽  
Network Models ◽  
Empirical Models ◽  
Support Pressure ◽  
Neural Network Models ◽  
Connection Weight ◽  
Rock Classification ◽  
Tunnel Support

Prediction of Tunnel support pressure up to an accurate and reliable degree is difficult, but of utmost importance. Empirical models are available with different set of parameters, mostly are based on the rock classification parameters. A feed forward neural network based predictive models from the data collected from literature for the Himalayan tunnels have been developed. The input variables in the developed neural network models were depth of over burden, radius of tunnel, normalised closure. The fourth input variable was rock mass quality or rock mass number or rock mass rating. The output was a support pressure. Sensitivity analysis relating the variables affecting the support pressure has been performed. The developed neural network models were compared with models developed based on the multiple linear regression analysis as well as with empirical models already available in literature. Finally, model equations have been presented based on the connection weight.

Estimation of rock mass permeability using variation in hydraulic conductivity with depth: experiences in hard rocks of western Turkey

2017 ◽  
Vol 77 (4) ◽  
pp. 1663-1671 ◽  
Author(s):  
Vincenzo Piscopo ◽  
Antonella Baiocchi ◽  
Francesca Lotti ◽  
Emre A. Ayan ◽  
Alper R. Biler ◽  
...  
Keyword(s):  
Rock Mass ◽  
Hydraulic Conductivity ◽  
Western Turkey ◽  
Hard Rocks ◽  
Rock Mass Permeability

A Method for Determining the Hydraulic Conductivity of Rock Mass with Fractures of Limited Length

2019 ◽  
Vol 53 (2) ◽  
pp. 155-158
Author(s):  
S. N. Chernyshev ◽  
T. V. Zommer ◽  
V. L. Zommer
Keyword(s):  
Rock Mass ◽  
Hydraulic Conductivity ◽  
Limited Length

A Rock Mass Classification Scheme for Estimating Hydraulic Conductivity of Fractured Rocks

2008 ◽  
Author(s):  
Shih Meng Hsu ◽  
Ming Chan Chung ◽  
Yu Ku Cheng ◽  
Chi Ho Tan ◽  
Su Yun Chi
Keyword(s):  
Rock Mass ◽  
Hydraulic Conductivity ◽  
Fractured Rocks ◽  
Classification Scheme ◽  
Rock Mass Classification ◽  
Mass Classification
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