There exist many fluvio-glacial deposits in the valley of Dadu River, Southwest China, which dates back to the Pleistocene. As some of the deposits are located within the seasonal water fluctuation zone of reservoirs,...There exist many fluvio-glacial deposits in the valley of Dadu River, Southwest China, which dates back to the Pleistocene. As some of the deposits are located within the seasonal water fluctuation zone of reservoirs, the seepage of groundwater acts as one of the key factors influencing their stability. Investigation into the sediment properties and permeability is, therefore, crucial for evaluating the sediment stability. In this study, in-situ permeability and sieving tests have been carried out to determine grain size distribution, correlations of permeability and hydraulic gradients, and relations between permeability and sedimentation properties. Test results indicate that the deposits are composed mostly of sands, gravels, cobbles and boulders, and the percentage of fines is less than 5%. The sediments have high densities, low porosities and natural moisture contents. At low hydraulic gradients, the seepage velocity obeys the Darcy＇s law, while a non- Darcy permeability is observed with hydraulic gradient exceeding a certain value （about 0.5 - 0.7）. The linear permeability coefficient ranges from 0.003 to 0.009 cm/s. Seepage failure occurs above a threshold between 1.1 and 1.5. The test data fit well with the non-linear permeability equations suggested by Forchheimer and Izbash. The non-Darcy permeability proves to be in accordance with the seepage equation suggested by Izbash with the power ＇m＇ of about 0.6 - 0.7. The characteristic grain sizes of the studied deposits are found in a narrow range between 0.024 and o.o31 mm, which is much lowerthan the effective grain size （dlo）.展开更多
The most challenging problem in mesh denoising is to distinguish features from noise. Based on the robust guided normal estimation and alternate vertex updating strategy, we investigate a new feature-preserving mesh d...The most challenging problem in mesh denoising is to distinguish features from noise. Based on the robust guided normal estimation and alternate vertex updating strategy, we investigate a new feature-preserving mesh denoising method. To accurately capture local structures around features, we propose a corner-aware neighborhood （CAN） scheme. By combining both overall normal distribution of all faces in a CAN and individual normal influence of the interested face, wc give a new consistency measuring method, which greatly improves the reliability of the estimated guided normals. As the noise level lowers, we take as guidance the previous filtered normals, which coincides with the emerging roUing guidance idea. In the vertex updating process, we classify vertices according to filtered normals at each iteration and reposition vertices of distinct types alternately with individual regularization constraints. Experiments on a variety of synthetic and real data indicate that our method adapts to various noise, both Gaussian and impulsive, no matter in the normal direction or in a random direction, with fcw triangles flippcd.展开更多
基金supported by the National Natural Fundation of China (41202212)Independent Subject Foundation of SKLGP (SKLGP2012Z006)
文摘There exist many fluvio-glacial deposits in the valley of Dadu River, Southwest China, which dates back to the Pleistocene. As some of the deposits are located within the seasonal water fluctuation zone of reservoirs, the seepage of groundwater acts as one of the key factors influencing their stability. Investigation into the sediment properties and permeability is, therefore, crucial for evaluating the sediment stability. In this study, in-situ permeability and sieving tests have been carried out to determine grain size distribution, correlations of permeability and hydraulic gradients, and relations between permeability and sedimentation properties. Test results indicate that the deposits are composed mostly of sands, gravels, cobbles and boulders, and the percentage of fines is less than 5%. The sediments have high densities, low porosities and natural moisture contents. At low hydraulic gradients, the seepage velocity obeys the Darcy＇s law, while a non- Darcy permeability is observed with hydraulic gradient exceeding a certain value （about 0.5 - 0.7）. The linear permeability coefficient ranges from 0.003 to 0.009 cm/s. Seepage failure occurs above a threshold between 1.1 and 1.5. The test data fit well with the non-linear permeability equations suggested by Forchheimer and Izbash. The non-Darcy permeability proves to be in accordance with the seepage equation suggested by Izbash with the power ＇m＇ of about 0.6 - 0.7. The characteristic grain sizes of the studied deposits are found in a narrow range between 0.024 and o.o31 mm, which is much lowerthan the effective grain size （dlo）.
基金Project supported by the National Natural Science Foundation of China （Nos. 61402224 and 61222206）, the Natural Science Foundation of Jiangsu Province, China （No. BK2014833）, and the Natural Science Foundation of Suzhou University of Science and Technology, China （No. XKZ201611）.Acknowledgements The authors would like to appreciate Wang-yu ZHANG for providing executable programs. The models used in this paper are courtesy of the AIM Shape Repos- itory, the Stanford 3D Scanning Repository, and Laser Design.
文摘The most challenging problem in mesh denoising is to distinguish features from noise. Based on the robust guided normal estimation and alternate vertex updating strategy, we investigate a new feature-preserving mesh denoising method. To accurately capture local structures around features, we propose a corner-aware neighborhood （CAN） scheme. By combining both overall normal distribution of all faces in a CAN and individual normal influence of the interested face, wc give a new consistency measuring method, which greatly improves the reliability of the estimated guided normals. As the noise level lowers, we take as guidance the previous filtered normals, which coincides with the emerging roUing guidance idea. In the vertex updating process, we classify vertices according to filtered normals at each iteration and reposition vertices of distinct types alternately with individual regularization constraints. Experiments on a variety of synthetic and real data indicate that our method adapts to various noise, both Gaussian and impulsive, no matter in the normal direction or in a random direction, with fcw triangles flippcd.
