The paper presents a case study for numerical analysis of the consolidation behavior of an instrumented trial embankment constructed on a soft soil foundation. Details are given to the geological profile, field instrumentation, laboratory test results, and determination of soil parameters for numerical modeling. Embankment settlement is estimated based on one-dimensional consolidation analysis and nonlinear finite-element analysis following Biot’s consolidation theory. Finite-element results are calibrated against the measured field data for a period of more than 3?years. Development and dissipation of excess pore pressure, long-term settlement, and horizontal displacement are predicted and discussed in light of sensitivity of embankment performance to some critical factors through a parametric study. 相似文献
The neutron probe has proven to be an effective means for monitoring long term in situ soil moisture variations. However, it is difficult to experimentally correlate neutron probe data (i.e., neutron counts) with accurate estimates of absolute soil moisture content, particularly for unsaturated clay soils. In this paper, a numerical model based on multigroup neutron diffusion theory is employed to predict the distribution of neutron flux in a neutron probe system. The model discretizes the neutron energy spectrum into seven intervals, with energy-dependent diffusion coefficients and parameters for each energy interval. The finite element method is employed to solve the coupled seven-group neutron diffusion equations. It is demonstrated that the numerical results compare very well with both laboratory experimental results and field measurements. The theoretical approach to neutron probe calibration described herein offers significant time and cost savings over traditional calibration methods, and potentially opens up new applications for neutron probe monitoring. 相似文献
With the application of intelligent manufacturing becoming more and more widely, the losses caused by mechanical faults of equipment increase. Identifying and troubleshooting faults in an early stage are important. The process of traditional data-driven fault diagnosis method includes data acquisition, fault classification, and feature extraction, in which classification accuracy is directly affected by the result of feature extraction. As a common deep learning method in image recognition, the convolutional neural network (CNN) demonstrates good performance in fault diagnosis. CNN can adaptively extract features from original signals and eliminate the effect of conventional handcrafted features. In this study, a multiscale learning neural network that contains one-dimension (1D) and two-dimension (2D) convolution channels is proposed. The network can learn the local correlation of adjacent and nonadjacent intervals in periodic signals, such as vibration data. The Paderborn data set is came into use to demonstrate the classification accuracy of the method which is brought forward, which includes three conditions of healthy, outer ring (OR) damage and inner ring (IR) damage. The classification accuracy of the method which is put forward is up to 98.58%. The same dataset was applied to test the classification accuracy of support vector machine (SVM) for comparison. And the proposed multiscale learning neural network demonstrates considerable improvements.
The problem of granular column collapse is investigated by means of an axisymmetric version of the particle finite element method (PFEM). The granular medium is represented by a simple rate-independent plasticity model and the frictional contact between the granular flow and its rigid basal surface is accounted for. In the version of the PFEM developed for this study, the governing equations of the boundary value problem are cast in terms of an optimization problem and solved using mathematical programming tools. The agreement between model and experiment is generally satisfactory, quantitatively as well as qualitatively. However, the friction angle of the granular material, as well as the exact interface conditions between the base and granular material, are shown to have a relatively significant influence on the results. 相似文献
To deliver drug locally and relieve the syndrome of pain after uterine artery embolization, N-[tris (hydroxymethyl) methyl] acrylamide-gelatin microspheres were prepared based on inverse suspension polymerization and
then separated into a number of subgroups (150–350, 350–560, 560–710, 710–1,000, and 1,000–1,430 μm) by wet-sieving. The microspheres
were dried by lyophilization or by washing with anhydrous ethanol. And ketoprofen was loaded by soaking dried blank microspheres
into concentrated ketoprofen ethanol solution. The ketoprofen loading level in different subgroups of microspheres was measured
and found higher when the microspheres were dried by lyophilization. Equilibrium water content and mean diameters of microspheres
decreased after drug loading, especially in subgroups with larger size. The microspheres went through the catheter without
any difficulty. Compression and relaxation tests were performed on microspheres before lyophilization, embosphere™, microspheres
after lyophilization and ketoprofen loading microspheres. The Young’s moduli were 54.74, 64.19, 98.15, and 120.44 kPa, respectively.
The release of ketoprofen from microspheres in different subgroups was studied by using the USPII method and T-cell apparatus,
respectively. The results indicate that the release rate of ketoprofen depends upon the diameter of microspheres, the type
of dissolution apparatus and the flow rate of media in the case that T-cell apparatus was applied. The CH50 test shows that
the activation of complement by ketoprofen-loaded microspheres was lower than by blank ones. 相似文献