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桁架杆分析

于 2020-11-02 发布

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下载积分: 1 下载次数: 3

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代码说明：

说明：基于matlab的桁架有限元分析，对理解位移法的有限元分析非常有帮助(finite element analysis with matlab)

文件列表：

桁架杆分析, 0 , 2020-11-02
桁架杆分析\form_KK.m, 273 , 2020-09-25
桁架杆分析\form_truss_F.m, 237 , 2020-09-24
桁架杆分析\print_truss_model.m, 1865 , 2020-09-24
桁架杆分析\print_truss_results.m, 1207 , 2020-09-24
桁架杆分析\truss.asv, 2260 , 2020-09-24
桁架杆分析\truss.m, 2467 , 2020-09-24
桁架杆分析\truss_1_data.asv, 1077 , 2020-09-25
桁架杆分析\truss_1_data.m, 1089 , 2020-09-25
桁架杆分析\truss_1_results.txt, 1898 , 2020-09-25
桁架杆分析\truss_c.m, 586 , 2020-09-24
桁架杆分析\truss_g.m, 180 , 2020-09-24
桁架杆分析\truss_kl.m, 355 , 2020-09-24

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本文基于毕肖普法和蒙特卡洛法,利用MATLAB编程软件计算了边坡结构的失效概率Pf和对应可靠指标β.在计算上述Pf和对应β时,考虑了土体三个主要基本参数(重度γ、内摩擦角(Ψ)、粘聚力c)的随机特性.利用MATLAB程序的计算结果表明,随机变量对边坡可靠度的影响与基本随机参数对抗滑反力的贡献大小有关,即,贡献大的基本随机参数,它的随机波动对结构失效概率Pf的影响也较大,反之亦然.另外,基于MATLAB的算例计算结果还表明:边坡结构失效概率Pf对内摩擦角(Ψ)较敏感,而对重度y和粘聚力c次之.其次,当同时考虑三个随机变量的联合波动对Pf的影响时发现,当它们在小范围内随机波动时,其联合特性对Pf的影响较敏感,但当它们在大范围内随机波动时,Pf主要受敏感随机变量的影响,而三个随机变量联合作用对Pf的影响和敏感随机变量(Ψ)单独作用时相差不大.由此可见,在实际边坡工程可靠性分析中,土体内摩擦角起主要作用,不容忽视.(Based Bishop method and Monte Carlo method, using MATLAB programming software to calculate the failure probability Pf and the corresponding slope reliability index β structure in the calculation of the above Pf and the corresponding β, consider the three main basic soil parameters (severe γ internal friction angle (Ψ), cohesion c) of the random nature of calculation results using MATLAB program shows that the influence of random variables on the reliability of the basic random slope parameters against anti- slip contribution to the size of the force , that is , the contribution the basic parameters of large random , random fluctuations which affect the probability of structural failure Pf is also larger , and vice versa addition, the calculation based on MATLAB numerical results also show that : the structure of slope failure probability Pf internal friction angle (Ψ) more sensitive , while severe and cohesion c y followed . Secondly, when taking into account the impact of the joint fl)

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The local binary pattern (LBP) is a simple yet very efficient operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. Perhaps the most important property of the LBP operator in real-world applications is its invariance against monotonic gray level changes caused, for example, by illumination variations. Another equally important is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. LBP is also very flexible: it can be easily adapted to different types of problems and used together with other image descriptors

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桁架杆分析

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