Discussion of “Phreatic Surface Location after Drawdown”

1971 ◽  
Vol 97 (8) ◽  
pp. 1134-1135
Author(s):  
Sivajogi D. Koppula
Keyword(s):  
Phreatic Surface ◽  
Surface Location

Closure to “Phreatic Surface Location After Drawdown”

1972 ◽  
Vol 98 (2) ◽  
pp. 224-224
Author(s):  
Arthur H. Dvinoff ◽  
Milton E. Harr
Keyword(s):  
Phreatic Surface ◽  
Surface Location

Phreatic Surface Location after Drawdown

1971 ◽  
Vol 97 (1) ◽  
pp. 47-58
Author(s):  
Arthur H. Dvinoff ◽  
Milton E. Harr
Keyword(s):  
Phreatic Surface ◽  
Surface Location

Electronic surface states in GaAs/Ga1 − xAlxAs superlattice: effect of surface location

Vacuum ◽  
1995 ◽  
Vol 46 (5-6) ◽  
pp. 459-463 ◽  
Author(s):  
M Stȩślicka ◽  
R Kucharczyk ◽  
EH El Boudouti ◽  
B Djafari-Rouhani ◽  
ML Bah ◽  
...  
Keyword(s):  
Surface States ◽  
Surface Location ◽  
Electronic Surface ◽  
Effect Of Surface

Surface Location and Enclosure

2006 ◽  
Vol 102 (2) ◽  
pp. 517-528 ◽  
Author(s):  
Arthur E. Stamps
Keyword(s):  
Surface Location

Forecast Model of Phreatic Surface on Tailings Dam Based on GM-GRNN Theory

2010 ◽  
Vol 44-47 ◽  
pp. 3403-3407
Author(s):  
Fei Yue Wang ◽  
Zhi Sheng Xu ◽  
Long Jun Dong
Keyword(s):  
Prediction Method ◽  
Forecast Model ◽  
Random Disturbance ◽  
Tailings Dam ◽  
Phreatic Surface ◽  
Factors Affecting ◽  
Depth Analysis ◽  
Tailing Dam ◽  
Calculation Results ◽  
Surface Prediction

Due to the extremely complicated seepage boundary conditions of tailing dam, the calculation results adopting two-dimensional simplified theory may greatly different from the measured results. It is urgent need of an accurate calculation method to forecast phreatic surface. In-depth analysis of factors affecting tailings dam phreatic surface, phreatic surface prediction model based on GRNN and GM (1,1) was established. A tailing dam engineering is tested using this model. It shows that the model uses the advantages of "accumulative generation" of a Gray prediction method, which weakens the original sequence of random disturbance factors, and increases the regularity of data. It also makes full advantage of the GRNN approximation performance, which has a fast solving speed, describes the nonlinear relationship easily, and avoids the defects of Gray theory.

Surface Location Error in Milling

2009 ◽  
pp. 173-198
Author(s):  
Tony L. Schmitz ◽  
Kevin S. Smith
Keyword(s):  
Location Error ◽  
Surface Location Error ◽  
Surface Location
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