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排序方式: 共有6509条查询结果,搜索用时 15 毫秒
1.
为建立高精度的边坡位移预测模型,采用相空间重构(PSR)将边坡位移时间序列数据转换为多维数据,同时构造小波核函数改进的支持向量机模型,建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机(SVM)模型、小波支持向量机(WSVM)模型和基于相空间重构的支持向量机(PSR-SVM)模型预测结果进行对比,通过平均绝对误差(MAE)、平均绝对误差百分比(MAPE)和均方根误差(RMSE)3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明,PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型,边坡位移预测的精度明显提升。 相似文献
2.
Difficulties are involved in discrete element method (DEM) modelling of the flexible boundary, that is, the membranes covering the soil sample, which can be commonly found in contemporary laboratory soil tests. In this paper, a novel method is proposed wherein the finite difference method (FDM) and DEM are coupled to simulate the rubber membrane and soil body, respectively. Numerical plane strain and triaxial tests, served by the flexible membrane, are implemented and analysed later. The effect of the membrane modulus on the measurement accuracy is considered, with analytical formulae derived to judge the significance of this effect. Based on an analysis of stress-strain responses and the grain rotation field, the mechanical performances produced by the flexible and rigid lateral boundaries are compared for the plane strain test. The results show that (1) the effect of the membrane on the test result becomes more significant at larger strain level because the membrane applies additional lateral confining pressure to the soil body; (2) the tested models reproduce typical stress and volumetric paths for specimens with shear bands; (3) for the plane strain test, the rigid lateral boundary derives a much higher peak strength and larger bulk dilatation, but a similar residual strength, compared with the flexible boundary. The latter produces a more uniform (or ‘diffuse') rotation field and more mobilised local kinematics than does the former. All simulations show that the proposed FDM-DEM coupling method is able to simulate laboratory tests with a flexible boundary membrane. 相似文献
3.
A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted. 相似文献
4.
Peng Yue Fan Gao Boyi Shangguan Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274
ABSTRACT High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning. 相似文献
5.
Majid T. Manzari 《国际地质力学数值与分析法杂志》2004,28(10):1011-1032
A micropolar elastoplastic model for soils is formulated and a series of finite element analyses are employed to demonstrate the use of a micropolar continuum in overcoming the numerical difficulties encountered in application of finite element method in standard Cauchy–Boltzmann continuum. Three examples of failure analysis involving a deep excavation, shallow foundation, and a retaining wall are presented. In all these cases, it is observed that the length scale introduced in the polar continuum regularizes the incremental boundary value problem and allows the numerical simulation to be continued until a clear collapse mechanism is achieved. The issue of grain size effect is also discussed. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
6.
We have developed a method for analytically solving the porous medium flow equation in many different geometries for horizontal (two‐dimensional), homogeneous and isotropic aquifers containing impermeable boundaries and any number of pumping or injection wells located at arbitrary positions within the system. Solutions and results are presented for rectangular and circular aquifers but the method presented here is easily extendible to many geometries. Results are also presented for systems where constant head boundary conditions can be emulated internal to the aquifer boundary. Recommendations for extensions of the present work are briefly discussed. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献
7.
The finite‐element formulation and integration algorithms developed in Part I are used to analyse a number of practical problems involving unsaturated and saturated soils. The formulation and algorithms perform well for all the cases analysed, with the robustness of the latter being largely insensitive to user‐defined parameters such as the number of coarse time steps and error control tolerances. The efficiency of the algorithms, as measured by the CPU time consumed, does not depend on the number of coarse time steps, but may be influenced by the error control tolerances. Based on the analyses presented here, typical values for the error control tolerances are suggested. It is also shown that the constitutive modelling framework presented in Part I can, by adjusting one constitutive equation and one or two material parameters, be used to simulate soils that expand or collapse upon wetting. Treating the suction as a strain variable instead of a stress variable proves to be an efficient and robust way of solving suction‐dependent plastic yielding. Moreover, the concept of the constitutive stress is a particularly convenient way of handling the transition between saturation and unsaturation. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献
8.
The frequent use of soils and earth materials for hydraulic capping and for geo‐environmental waste containment motivated our interest in detailed modelling of changes in size and shape of macro‐pores to establish links between soil mechanical behaviour and concurrent changes in hydraulic and transport properties. The objective of this study was to use finite element analysis (FEA) to test and extend previous analytical solutions proposed by the authors describing deformation of a single macro‐pore embedded in linear viscoplastic soil material subjected to anisotropic remote stress. The FEA enables to consider more complex pore geometries and provides a detailed picture of matrix yield behaviour to explain shortcomings of approximate analytical solutions. Finite element and analytical calculations agreed very well for linear viscous as well as for viscoplastic materials, only limited for the case of isotropic remote stress due to the simplifications of the analytical model related to patterns and onset of matrix‐yielding behaviour. FEA calculations were compared with experimental data obtained from a compaction experiment in which pore deformation within a uniform modelling clay sample was monitored using CAT scanning. FEA predictions based on independently measured material properties and initial pore geometry provided an excellent match with experimentally determined evolution of pore size and shape hence lending credence to the potential use of FEA for more complex pore geometries and eventually connect macro‐pore deformation with hydraulic properties. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
9.
有限元法与伪谱法混合求解弹性波动方程 总被引:6,自引:0,他引:6
在地震波场数值模拟中,有限差分法、有限元法和伪谱法都是常用的基本方法,但它们各有不同的适应性和优缺点,如有限差分法、有限元法都存在减弱网格频散和提高计算效率的矛盾,而伪谱法的网格频散小且计算效率高.有限差分法和伪谱法在处理地表结构复杂或地表剧烈起伏以及地下结构复杂的情况时存在较大的难度,而有限元法可较为理想地拟合起伏地表和任意弯曲界面,且可方便地处理自由边界条件和界面边界条件.尝试将有限元法和伪谱法相结合,形成地震波场数值模拟的一种混合方法,利用二者的优点,克服二者的缺点,达到既减弱网格频散又提高计算精度和效率的目的.并采用所谓的‘过度区域‘技术解决两种不同算法的衔接问题.模拟实例表明,给出的混合模拟方法不失为弹性波场数值模拟的一种有效方法. 相似文献
10.
Olac Fuentes 《Experimental Astronomy》2001,12(1):21-31
In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data. 相似文献