Test Embankment Dam of Fracture Grouting

1989 ◽  
Vol 115 (11) ◽  
pp. 1668-1672 ◽  
Author(s):  
Yu‐jiong Chen ◽  
Shu‐Iu Zhang
1986 ◽  
Vol 23 (2) ◽  
pp. 203-215 ◽  
Author(s):  
A. W. Hanna ◽  
G. Ambrosii ◽  
A. D. McConnell

Investigation of the coarse alluvial foundation for the Pichi Picun Leufu embankment dam is described and evaluated. Direct and indirect investigation methods are compared and an assessment is made of their relative adequacy in order to gain a realistic understanding of foundation conditions. Indirect methods—dynamic cone penetration testing and shear wave velocity measurement—calibrated by comparative testing in a test embankment, have been found to provide a satisfactory means of evaluating the density of thick alluvial deposits below the water table. Relationships of relative density, penetration resistance, and shear wave velocity are discussed. Dynamic penetration resistance normalized for effective overburden pressure appears to be the more sensitive indicator of changes in material density. Key words: coarse alluvium, relative density, dynamic penetration, shear wave velocity, test embankment, overburden pressure.


2021 ◽  
Vol 651 (3) ◽  
pp. 032047
Author(s):  
Shaozhen Cheng ◽  
Fa Yang ◽  
Yuchen Dai ◽  
Zili Yang ◽  
Ye Shi

2014 ◽  
Vol 519 ◽  
pp. 177-189 ◽  
Author(s):  
Vahid Nourani ◽  
Mohammad Hossein Aminfar ◽  
Mohammad Taghi Alami ◽  
Elnaz Sharghi ◽  
Vijay P. Singh
Keyword(s):  

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Rafał Wróżyński ◽  
Krzysztof Pyszny ◽  
Mariusz Sojka ◽  
Czesław Przybyła ◽  
Sadżide Murat-Błażejewska

AbstractThe article describes how the Structure-from-Motion (SfM) method can be used to calculate the volume of anthropogenic microtopography. In the proposed workflow, data is obtained using mass-market devices such as a compact camera (Canon G9) and a smartphone (iPhone5). The volume is computed using free open source software (VisualSFMv0.5.23, CMPMVSv0.6.0., MeshLab) on a PCclass computer. The input data is acquired from video frames. To verify the method laboratory tests on the embankment of a known volume has been carried out. Models of the test embankment were built using two independent measurements made with those two devices. No significant differences were found between the models in a comparative analysis. The volumes of the models differed from the actual volume just by 0.7‰ and 2‰. After a successful laboratory verification, field measurements were carried out in the same way. While building the model from the data acquired with a smartphone, it was observed that a series of frames, approximately 14% of all the frames, was rejected. The missing frames caused the point cloud to be less dense in the place where they had been rejected. This affected the model’s volume differed from the volume acquired with a camera by 7%. In order to improve the homogeneity, the frame extraction frequency was increased in the place where frames have been previously missing. A uniform model was thereby obtained with point cloud density evenly distributed. There was a 1.5% difference between the embankment’s volume and the volume calculated from the camera-recorded video. The presented method permits the number of input frames to be increased and the model’s accuracy to be enhanced without making an additional measurement, which may not be possible in the case of temporary features.


2009 ◽  
Vol 23 (6) ◽  
pp. 406-414 ◽  
Author(s):  
Pedro J. Amaya ◽  
John T. Massey-Norton ◽  
Timothy D. Stark
Keyword(s):  
Fly Ash ◽  

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