This paper aims to determine the optimal fines content of coarse-grained soil required to simultaneously achieve weaker frost susceptibility and better bearing capacity. We studied the frost susceptibility and strengt...This paper aims to determine the optimal fines content of coarse-grained soil required to simultaneously achieve weaker frost susceptibility and better bearing capacity. We studied the frost susceptibility and strength properties of coarse-grained soil by means of frost heaving tests and static triaxial tests, and the results are as follows:(1) the freezing temperature of coarse-grained soil decreased gradually and then leveled off with incremental increases in the percent content of fines;(2) the fines content proved to be an important factor influencing the frost heave susceptibility and strength properties of coarse-grained soil. With incremental increases in the percent content of fines, the frost heave ratio increased gradually and the cohesion function of fines effectively enhanced the shear strength of coarse-grained soil before freeze-thaw, but the frost susceptibility of fines weakened the shear strength of coarse-grained soil after freeze-thaw;(3) with increasing numbers of freeze-thaw cycles, the shear strength of coarse-grained soil decreased and then stabilized after the ninth freeze-thaw cycle, and therefore the mechanical indexes of the ninth freeze-thaw cycle are recommended for the engineering design values; and(4) considering frost susceptibility and strength properties as a whole, the optimal fines content of 5% is recommended for railway subgrade coarse-grained soil fillings in frozen regions.展开更多
Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvan-tages are addressed, and a fast segmentation approach based on simple Markov random field (MRF) for SA...Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvan-tages are addressed, and a fast segmentation approach based on simple Markov random field (MRF) for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach.展开更多
The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous ...The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous fine-grained texture segments present in the images. At first, an initial seed point is found for the largest and most homogeneous segment of the image. This initial seed point of the segment is expanded using a region growing method. Other texture segments of the image are extracted analogously in turn. At the second stage, the procedure of merging the extracted segments belonging to the same texture class is performed. Then, the detected texture segments are input to a neural network with competitive layers which accomplishe</span><span style="font-family:Verdana;">s</span><span style="font-family:""><span style="font-family:Verdana;"> more accurate delineation of the shapes of the extracted texture segments. The proposed segmentation procedure is fully unsupervised, <i></span><i><span style="font-family:Verdana;">i.e.</span></i><span style="font-family:Verdana;"></i>, it does not use any a priori knowledge on either the type of textures or the number of texture segments in the image. The research results in development of the segmentation algorithm realized as a computer program tested in a series of experiments that demonstrate its efficiency on grayscale natural scenes.展开更多
基金supported by the National Key Technology Support Program of China (No.2012BAG05B00)the National Natural Science Foundation of China (Nos. 51208320 and 51178281)the Key Subject of China Railway Corporation (Nos. 2014G003-F and 2014G003-A)
文摘This paper aims to determine the optimal fines content of coarse-grained soil required to simultaneously achieve weaker frost susceptibility and better bearing capacity. We studied the frost susceptibility and strength properties of coarse-grained soil by means of frost heaving tests and static triaxial tests, and the results are as follows:(1) the freezing temperature of coarse-grained soil decreased gradually and then leveled off with incremental increases in the percent content of fines;(2) the fines content proved to be an important factor influencing the frost heave susceptibility and strength properties of coarse-grained soil. With incremental increases in the percent content of fines, the frost heave ratio increased gradually and the cohesion function of fines effectively enhanced the shear strength of coarse-grained soil before freeze-thaw, but the frost susceptibility of fines weakened the shear strength of coarse-grained soil after freeze-thaw;(3) with increasing numbers of freeze-thaw cycles, the shear strength of coarse-grained soil decreased and then stabilized after the ninth freeze-thaw cycle, and therefore the mechanical indexes of the ninth freeze-thaw cycle are recommended for the engineering design values; and(4) considering frost susceptibility and strength properties as a whole, the optimal fines content of 5% is recommended for railway subgrade coarse-grained soil fillings in frozen regions.
基金supported by the Specialized Research Found for the Doctoral Program of Higher Education (20070699013)the Natural Science Foundation of Shaanxi Province (2006F05)the Aeronautical Science Foundation (05I53076)
文摘Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvan-tages are addressed, and a fast segmentation approach based on simple Markov random field (MRF) for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach.
文摘The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous fine-grained texture segments present in the images. At first, an initial seed point is found for the largest and most homogeneous segment of the image. This initial seed point of the segment is expanded using a region growing method. Other texture segments of the image are extracted analogously in turn. At the second stage, the procedure of merging the extracted segments belonging to the same texture class is performed. Then, the detected texture segments are input to a neural network with competitive layers which accomplishe</span><span style="font-family:Verdana;">s</span><span style="font-family:""><span style="font-family:Verdana;"> more accurate delineation of the shapes of the extracted texture segments. The proposed segmentation procedure is fully unsupervised, <i></span><i><span style="font-family:Verdana;">i.e.</span></i><span style="font-family:Verdana;"></i>, it does not use any a priori knowledge on either the type of textures or the number of texture segments in the image. The research results in development of the segmentation algorithm realized as a computer program tested in a series of experiments that demonstrate its efficiency on grayscale natural scenes.
