基于改进混沌粒子群的混合神经网络和蒙特卡洛法的结构随机灵敏度分析方法

针对不确定性参数对结构力学性能的随机影响,该文利用混合神经网络良好的小样本学习和泛化能力构建结构响应复杂的函数关系,采用改进的混沌粒子群算法优化网络寻址结构。结合蒙特卡洛法对结构进行随机性分析,并根据该文提出的新的灵敏度度量参数计算随机变量的全局灵敏度系数。通过数学算例和工程算例验证了所提方法的可行性,且结构响应的概率分布曲线也可以真实的反应实际情况。同时,利用该文所提出的随机灵敏度计算方法可以更好的反应各随机变量对结构响应的相关性和敏感性。     

随机结构在随机载荷下的动力可靠度分析

提出了综合考虑动载荷和结构参数双重随机性的动力可靠度分析方法.首先基于随机振动的首次超越破坏准则,参照随机变量功能函数模式,建立了随机结构动力可靠度功能函数;然后引入序列响应面法,对功能函数进行拟合;最后用JC法求解可靠指标.算例分析验证了本文方法具有较好的计算精度和计算效率,尤其在复杂工程结构的动力可靠度分析中有广阔的应用前景.     

平稳随机激励下线性随机桁架结构动力响应分析

考虑桁架结构的物理参数、几何尺寸的随机性，利用求解随机变量函数矩的方法和求解随机变量数字特征的代数综合法，从结构平稳随机响应在频域上的表达式出发，导出了随机桁架结构在平稳随机激励下位移响应均方值和应力响应均方值的均值、方差和变异系数的计算表达式。通过算例考察了结构物理参数和几何尺寸的随机性对结构位移响应均方值和应力响应均方值随机变量随机性的影响，并获得了一些有意义的结论。     

随机参数结构在随机荷载激励下的动力响应分析

研究了基于概率的工程结构动力响应分析方法。利用振型迭加法导出了结构物理参数和作用荷载幅值同时具有随机性时结构动力响应随机变量的数字特征计算表达式，并提出了求解方法。通过算例考察了结构物理参数和作用荷载的随机性对结构动力响应的影响，表明文中提出的计算模型和方法是正确与可行的。     

刚架结构物理参数的随机性对其非平稳随机响应的影响

文中研究了随机刚架结构的非平稳随机响应问题。考虑结构在非平稳随机激励下其物理参数的随机性,从结构非平稳随机响应的表达式出发,利用求解随机变量函数矩的方法和求解随机变量数字特征的代数综合法,导出了结构位移响应均方值和应力响应均方值的均值、方差和变异系数的计算表达式。通过算例,考察了结构物理参数的随机性对结构动力响应均方值随机性的影响,获得了若干有意义的结论。     

分片响应面法在随机振动响应分析中的应用

在分析结构的随机振动响应时,响应面法(Response Surface Method)可有效地降低随机仿真的计算代价。然而,当随机变量存在大变异系数时,传统的响应面法无法满足所要求的精度。分片响应面基于对随机变量变异系数进行合理分块的原则,缩小响应面的近似范围,并对分割后的响应面进行独立分析,从而提高了响应面在该空间的近似精度。首先采用分块响应面法结合蒙特卡洛MC抽样技术,以单质点振子模型的随机振动响应为算例,对分块响应面法的正确性进行验证。计算结果表明,在随机变量的变异系数不大时,分片响应面法的计算精度不低于传统响应面法,而当随机变量具有大变异系数时,分片响应面法的近似精度远高于传统响应面法。此外,以随机地震动作用下的桥墩随机振动为背景,将该方法进行了进一步地推广及应用。     

基于等概率近似变换的 向量型层递响应面法分析结构可靠度

基于正交变换和等概率近似变换,研究建立了随机变量为非高斯互相关的工程结构可靠度分析的向量型层递响应面法。首先利用正交变换将非高斯互相关随机变量变换为互不相关的非高斯标准随机变量,建立结构总体刚度矩阵和荷载列阵,据此定义预处理器并形成预处理随机Krylov子空间,进而利用该空间的层递基向量将结构总体节点位移向量近似展开,建立关于互不相关非高斯标准随机变量的层递响应面;然后根据等概率近似变换,将独立标准正态空间的样本点转换为层递响应面在非高斯空间中的概率配点;最后通过回归分析确定层递响应面待定系数,并利用层递响应面建立极限状态方程求解结构可靠度。分析表明:该文提出的等概率近似变换方法不仅成功地将层递响应面法拓展到非高斯互相关随机变量下的结构可靠度分析,而且方法简便、适用范围广、计算精度和效率较高,具有良好的全域性。     

大跨隔震结构基于结构随机因子法的响应分析

结合等价线性化方法,将随机因子法拓展应用到非线性多自由度结构中。选取隔震支座的屈服位移、面积、面压和高度等四个参数为随机变量来考虑隔震层刚度和结构质量的随机性,采用结构随机因子法建立大跨隔震结构的四个随机变量与结构刚度和质量之间的关系,应用代数综合法推导了结构位移响应的数字特征计算表达式。最后通过算例研究了多维地震下大跨隔震结构中结构随机性对位移响应的影响,并获得了一些有意义的结论。     

区间随机桁架结构动力特性分析方法研究

摘 要：针对区间随机桁架结构的动力特性分析,提出了一种区间随机有限元方法。当结构的物理参数和几何尺寸同时具有区间随机性时,利用区间因子法和随机因子法建立了结构的刚度矩阵和质量矩阵;从结构振动的瑞利商表达式出发,利用区间运算推导了结构动力特性区间随机变量的计算式;进而利用随机变量的矩法和代数综合法,推导出了结构特征值的数字特征的计算式。最后通过算例分析了区间随机桁架结构参数的区间随机性对其动力特性的影响,计算结果表明该方法是可行和有效的。
     

模糊随机桁架结构在模糊随机激励下的动力响应

研究了模糊随机参数桁架结构在模糊随机荷载激励下的复合模糊随机振动动力响应的问题。同时考虑结构的物理参数、几何尺寸和外载荷幅值的模糊随机性,从Duham e l积分式出发,利用振型迭加法求出了结构动力响应模糊随机变量的表达式;再由随机函数的矩法推导出结构模糊随机动力响应的模糊数字特征。最后,通过算例考察了结构参数和作用荷载的模糊随机性对结构动力响应的影响,并用M on te C arlo数值法对算例进行模拟,验证了文中模型和分析方法是可行有效的。     

脉动风速连续随机场的降维模拟

基于标准正交随机变量的波数谱表示,通过定义标准正交随机变量集的随机函数形式,建立了连续时空随机场模拟的波数谱-随机函数方法。同时,引入快速傅里叶变换（FFT）的算法,极大地提高了波数谱-随机函数方法的模拟效率。在波数谱-随机函数模拟方法中,仅需两个基本随机变量即可在概率密度层次上描述时空随机场的概率特性,并利用数论方法选取基本随机变量的代表性点集,实现对连续时空随机场模拟的降维表达。数值算例表明,当模拟相同数量的样本时,综合考虑模拟的效率和精度两方面,该文方法与传统的波数谱表示方法不分伯仲,但该文方法所需的基本随机变量最少,生成的代表性样本数量少且构成一个完备的概率集,从而可结合概率密度演化理论实现结构随机动力反应及动力可靠度的精细化分析。最后,结合Kaimal风速谱及Davenport空间相干函数模型,模拟了水平向脉动风速连续随机场,验证了该文方法的有效性和优越性。     

Statistical reconstruction and Karhunen–Loève expansion for multiphase random media

Termed as random media, rocks, composites, alloys and many other heterogeneous materials consist of multiple material phases that are randomly distributed through the medium. This paper presents a robust and efficient algorithm for reconstructing random media, which can then be fed into stochastic finite element solvers for statistical response analysis. The new method is based on nonlinear transformation of Gaussian random fields, and the reconstructed media can meet the discrete‐valued marginal probability distribution function and the two‐point correlation function of the reference medium. The new method, which avoids iterative root‐finding computation, is highly efficient and particularly suitable for reconstructing large‐size random media or a large number of samples. Also, benefiting from the high efficiency of the proposed reconstruction scheme, a Karhunen–Loève (KL) representation of the target random medium can be efficiently estimated by projecting the reconstructed samples onto the KL basis. The resulting uncorrelated KL coefficients can be further expressed as functions of independent Gaussian random variables to obtain an approximate Gaussian representation, which is often required in stochastic finite element analysis. Copyright © 2015 John Wiley & Sons, Ltd.     

动力可靠度约束下基于概率测度变换-全局收敛移动渐近线法的结构优化设计EI北大核心CSCD

基于动力可靠度的结构优化是实现随机动力系统优化设计的重要途径。针对设计变量为系统中部分随机变量分布均值的情形,提出了一种基于动力可靠度的结构优化设计方法。在该方法中,通过概率密度演化理论实现了结构动力可靠度的高效分析。在此基础上,结合概率测度变换,可以在不增加任何确定性结构分析的前提下,实现动力可靠度对设计变量的灵敏度分析。进而,通过将上述概率密度演化-测度变换方法嵌入全局收敛移动渐近线法,实现了基于动力可靠度的结构优化设计问题的高效求解。数值算例的结果表明,所提方法可以显著降低结构分析次数,具有较高的效率与稳健性。     

Time-variant reliability global sensitivity analysis with single-loop Kriging model combined with importance sampling

This paper develops a novel failure probability-based global sensitivity index by introducing the Bayes formula into the moment-independent global sensitivity index to approximate the effect of input random variables or stochastic processes on the time-variant reliability. The proposed global sensitivity index can estimate the effect of uncertain inputs on the time-variant reliability by comparing the difference between the unconditional probability density function of input variables and the conditional probability density function in failure state of input variables. Furthermore, a single-loop active learning Kriging method combined with metamodel-based importance sampling is employed to improve the computational efficiency. The accuracy of the results obtained by Kriging model is verified by the reference results provided by the Monte Carlo simulation. Four examples are investigated to demonstrate the significance of the proposed failure probability-based global sensitivity index and the effectiveness of the computational method.     

Neumann enriched polynomial chaos approach for stochastic finite element problems

An enrichment scheme based upon the Neumann expansion method is proposed to augment the deterministic coefficient vectors associated with the polynomial chaos expansion method. The proposed approach relies upon a split of the random variables into two statistically independent sets. The principal variability of the system is captured by propagating a limited number of random variables through a low-ordered polynomial chaos expansion method. The remaining random variables are propagated by a Neumann expansion method. In turn, the random variables associated with the Neumann expansion method are utilised to enrich the polynomial chaos approach. The effect of this enrichment is explicitly captured in a new augmented definition of the coefficients of the polynomial chaos expansion. This approach allows one to consider a larger number of random variables within the scope of spectral stochastic finite element analysis in a computationally efficient manner. Closed-form expressions for the first two response moments are provided. The proposed enrichment method is used to analyse two numerical examples: the bending of a cantilever beam and the flow through porous media. Both systems contain distributed stochastic properties. The results are compared with those obtained using direct Monte Carlo simulations and using the classical polynomial chaos expansion approach.     

简化三阶矩拟正态变换及其在结构可靠度分析中的应用

针对三阶矩拟正态变换理论公式系数形式复杂及现有相关系数的转换公式适用范围未明确的问题,通过对公式系数进行简化和对相关系数的讨论,提出了独立随机变量和相关随机变量的简化三阶矩拟正态变换模型,并给出了相关系数转换公式的简明适用范围。通过将提出的简化三阶矩拟正态变换模型与一阶可靠度分析方法(FORM)结合,发展了随机变量分布未知条件下的可靠度分析方法,并采用数值算例验证了该方法的准确性和适用性。研究结果表明,所提出的简化三阶矩拟正态变换模型具有较高的准确性和适用性,能够与FORM分析方法结合,实现随机变量分布未知条件下的结构可靠度分析。     

Variable screening and ranking using sampling-based sensitivity measures

This paper presents a methodology for screening insignificant random variables and ranking significant important random variables using sensitivity measures including two cumulative distribution function (CDF)-based and two mean-response based measures. The methodology features (1) using random samples to compute sensitivities and (2) using acceptance limits, derived from the test-of-hypothesis, to classify significant and insignificant random variables. Because no approximation is needed in either the form of the performance functions or the type of continuous distribution functions representing input variables, the sampling-based approach can handle highly nonlinear functions with non-normal variables. The main characteristics and effectiveness of the sampling-based sensitivity measures are investigated using both simple and complex examples. Because the number of samples needed does not depend on the number of variables, the methodology appears to be particularly suitable for problems with large, complex models that have large numbers of random variables but relatively few numbers of significant random variables.     

涉及离散变量的函数统计矩点估计法

点估计法对于仅包含连续随机变量的函数和系统的随机分析具有原理简洁清晰、操作简单易行的优点,并可以直接给出除均值和标准差之外的其他低阶统计矩。然而,对于客观存在的或者是需处理为的涉及离散随机变量的系统,现有的点估计法无能为力。为解决这一问题,该文基于一般随机系统的形式解析解,导出了涉及离散变量函数和系统的统计矩估计的理论表达式;然后,将其与现有的点估计法相结合,给出了涉及离散变量的函数和系统的低阶矩估计的点估计法;最后,通过理论推导和算例分析两种方式验证了建议方法的合理性和有效性,且指出该方法对包含离散变量的一般工程随机系统分析的适用性。     

Efficient structural reliability analysis via a weak-intrusive stochastic finite element method

This paper presents a novel methodology for structural reliability analysis by means of the stochastic finite element method (SFEM). The key issue of structural reliability analysis is to determine the limit state function and corresponding multidimensional integral that are usually related to the structural stochastic displacement and/or its derivative, e.g., the stress and strain. In this paper, a novel weak-intrusive SFEM is first used to calculate structural stochastic displacements of all spatial positions. In this method, the stochastic displacement is decoupled into a combination of a series of deterministic displacements with random variable coefficients. An iterative algorithm is then given to solve the deterministic displacements and the corresponding random variables. Based on the stochastic displacement obtained by the SFEM, the limit state function described by the stochastic displacement (and/or its derivative) and the corresponding multidimensional integral encountered in reliability analysis can be calculated in a straightforward way. Failure probabilities of all spatial positions can be obtained at once since the stochastic displacements of all spatial points have been known by using the proposed SFEM. Furthermore, the proposed method can be applied to high-dimensional stochastic problems without any modification. One of the most challenging problems encountered in high-dimensional reliability analysis, known as the curse of dimensionality, can be circumvented with great success. Three numerical examples, including low- and high-dimensional reliability analysis, are given to demonstrate the good accuracy and the high efficiency of the proposed method.