复合随机振动系统的动力可靠性矩独立重要性测度及态相关参数解法

针对激励与结构参数同时存在不确定性的复合随机振动系统,由随机结构无条件动力可靠度表达式出发,利用条件概率密度函数解析变换给出衡量基本随机变量对动力可靠性影响的矩独立重要性测度指标。该指标可表征不确定性随机变量对动力可靠度响应量分布的平均影响程度,可全面反映随机变量对响应分布影响。基于状态依存参数模型,提出求解矩独立重要性测度指标的态相关参数(SDP)法。利用算例分析结构动力可靠性参数的矩独立重要性测度,并与直接Monte-Carlo法对比。所提方法可在保证计算精度同时大幅度提高计算效率,适用于分析复合随机振动系统非线性可靠性响应。     

连续梁弯曲振动系统可靠性频率灵敏度研究

将机械动力学与可靠性理论结合,依据固有频率与激振频率之差绝对值不超过规定值的关系准则,提出随机动态结构系统避免共振的频率可靠性分析方法;在研究基础上结合灵敏度技术,提出随机动态结构系统的频率可靠性灵敏度分析方法,推导出参数为正态随机变量时可靠性灵敏度的表达式,获得可靠度与随机变量分布参数波动间的量化关系,通过研究设计参数的改变对系统可靠性影响,对动态结构系统共振的可靠性灵敏度分析方法进行探索。并以实际工程简化模型随机连续梁弯曲振动系统为例,将理论方法与工程实践相结合,对该模型进行频率可靠性灵敏度研究,理论计算与工程实际完全一致,表明该方法的有效性、实用性。     

基于扩展型共轭无迹变换的随机不确定性传播分析方法

响应的统计矩是描述随机结构系统响应的主要方式之一,相对于响应的概率密度函数,结构响应的统计矩能够较容易获取,因而颇受研究人员的关注,而其中结构响应统计矩的高效计算方法一直是研究的热点。该文以可兼顾精度与效率的共轭无迹变换方法为基础,通过引入正态-非正态变换,发展了可适用于涉及任意随机变量分布类型统计矩估计的第Ⅰ类扩展型共轭无迹变换方法;将第Ⅰ类扩展型共轭无迹变换方法与高维分解模型相结合,发展了可适用于任意维度随机系统统计矩估计的第Ⅱ类扩展型共轭无迹变换方法;通过3个数值算例对建议方法进行了验证。算例分析结果表明:建议的两类方法均可以在拓展共轭无迹变换方法适用范围的基础上兼顾计算精度和效率;对于低维和高维问题,分别建议采用第Ⅰ类和第Ⅱ类扩展型共轭无迹变换方法进行响应统计矩估计。     

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。     

结构可靠度分析中变量相关时三种变换方法的比较

介绍了Orthogonal变换、Rosenblatt变换和Nataf变换三种变换方法的基本原理,并比较了三种变换方法的优缺点及其适用范围.采用算例详细地比较了三种变换方法对可靠度结果的影响.结果表明,Nataf变换和Orthogonal变换的根本区别在于Nataf变换考虑了相关变量变换到相关标准正态空间后相关系数的变化...     

基于车辆荷载效应截尾分布的桥梁限载分析方法

根据车辆荷载效应右截尾分布模型, 提出一种基于结构可靠度理论的中、小跨度桥梁限载分析方法。首先以规范规定的车辆荷载效应分布为原始分布, 构造右截尾的车辆荷载效应概率密度函数;然后, 假定抗力、恒载效应及车辆荷载效应为相互独立的随机变量, 建立了考虑车辆荷载效应右截尾分布特征的限载系数反演模型;继而, 通过分析车辆荷载效应均值变化对限载系数的影响规律, 提出了理想抗力桥梁条件限载系数的确定方法, 并讨论了条件限载系数对车辆荷载效应变异系数与分布类型的敏感程度;最后, 根据原桥梁规范受弯构件承载能力设计表达式, 计算了按原规范设计桥梁的条件限载系数。结果表明, 桥梁限载取值仅与设计荷载等级、容许失效概率以及设计采用的活恒载比值有关, 而与车辆荷载效应的统计参数关系不大。所提限载分析方法可为中、小跨径桥梁提供具有一致可靠度水平的限载取值。     

包装件在非高斯随机载荷下响应特征分析方法研究EI北大核心CSCD

在运输过程中,包装件经常受到非高斯随机振动的作用,在进行包装系统优化时,经常需要重复确定包装件加速度响应的统计特征和振动可靠性,该研究提出一种高效准确确定非高斯随机振动条件下非线性包装件加速度响应统计特征的分析方法。采用非高斯Karhunen-Loeve展开将非高斯随机振动表示为非高斯随机变量的线性组合,用一阶泰勒展开估计包装件加速度响应,确定加速度响应的统计矩参数,根据包装件加速度响应的前四阶矩参数,应用鞍点估计法确定包装件加速度响应的概率密度函数(probability density function, PDF)和累积分布函数(cumulative distribution function, CDF)。由于采用随机变量的线性组合模拟非高斯随机振动激励,避免了随机变量非线性变换,采用一阶泰勒展开估计包装件加速度响应具有良好的准确性,鞍点估计法分析包装件加速度响应的PDF和CDF,避免了大量蒙特卡洛或拟蒙特卡洛分析,提高了分析效率。     

非高斯风压时程的矩模型变换与峰值因子计算公式

利用Hermite矩模型理论建立了非高斯过程与高斯过程之间的单调变换关系;非高斯过程与高斯过程的极值发生概率相等,界限穿越率相等,这为非高斯风压峰值因子、风压极值的计算奠定了基础。在介绍软化过程、硬化过程和偏斜过程的Hermite矩模型理论的基础上,采用偏斜系数、峰态系数表明了矩模型的单调变换范围,由此可根据偏斜系数、峰态系数预先确定Hermite矩模型的变换公式和变换阶数。建立了非高斯过程峰值因子的概率分布表达式,明确了非高斯峰值因子与高斯峰值因子之间的一一对应关系。将非高斯极值概率分布及峰值因子计算方法应用于平屋盖局部风压峰值因子、风压系数极值的计算。结果表明:非高斯风压的峰值因子、风压系数极值的计算值的平均值与实测值的平均值吻合,风压系数极值的吻合程度优于峰值因子的吻合程度。     

钢坯吊具主连杆可靠性优化设计

应用摄动分析理论,论述了钢坯吊具主连杆结构的可靠性问题.在不确定变量分布形式未知情况下,利用Edgeworth级数,将任意分布的随机变量转化为标准的正态分布形式,建立其分布函数,结合摄动分析方法计算标准化正态变量及结构失效状态函数的均值和方差,得到结构的可靠度指标,可用于描述结构的可靠性程度.以可靠度指标为约束构建优化...     

基于模型修正的空间变异边坡可靠度分析方法

空间变异边坡可靠度计算需要进行多次重复性边坡稳定性分析,常用的边坡稳定性分析极限平衡方法（LEM）计算效率较高而有限元方法（FEM）可捕捉真实的边坡失效机制,边坡可靠度评价中如能充分利用这两者的优势将具有重要的工程价值。该文在发展考虑参数空间变异性边坡可靠度分析的一阶可靠度方法（FORM）基础上,提出基于模型修正的空间变异边坡可靠度分析方法,引入一修正系数将基于LEM的简化极限状态面逐渐修正为基于FEM的准确极限状态面,最后基于修正系数和LEM安全系数计算公式采用线抽样法计算边坡失效概率。通过一个考虑参数空间变异性的摩擦/粘性土坡算例验证提出方法的有效性,并探讨土体参数空间变异性和黏聚力与内摩擦角之间互相关性对边坡可靠度的影响。结果表明:提出方法的边坡可靠度计算精度与基于FEM子集模拟方法一致,但是计算效率远远大于后者,尤其对于低概率水平边坡可靠度问题,从而为解决考虑土体参数空间变异性的低概率水平边坡可靠度问题提供一条新的途径。     

An orthogonal normal transformation of correlated non-normal random variables for structural reliability

In this paper, an efficient and explicit technique is proposed for transforming correlated non-normal random variables into independent standard normal variables based on the three-parameter (3P) lognormal distribution. In contrast with the classic Nataf transformation, the derived equivalent correlation coefficient in non-orthogonal standard normal space of the proposed transformation is expressed as an explicit formula, thereby avoiding tedious iteration algorithm or multifarious empirical formulas. Meanwhile, the applicable range of the original correlation coefficient is determined based on fundamental properties of the proposed expression of correlation distortion and the definition of correlation coefficient. The proposed transformation requires only the first three moments (i.e., mean, standard deviation, and skewness) of basic random variables, as well as their correlation matrix. Therefore, the proposed transformation can also be applied even when the joint distribution or marginal distributions of the basic random variables are unknown. Several numerical examples are presented to demonstrate the user-friendliness, efficiency, and accuracy of the proposed transformation applied in structural reliability analysis involving correlated non-normal random variables.     

Multivariate distribution models with prescribed marginals and covariances

Two multivariate distribution models consistent with prescribed marginal distributions and covariances are presented. The models are applicable to arbitrary number of random variables and are particularly suited for engineering applications. Conditions for validity of each model and applicable ranges of correlation coefficients between the variables are determined. Formulae are developed which facilitate evaluation of the model parameters in terms of the prescribed marginals and covariances. Potential uses of the two models in engineering are discussed.     

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。     

Dynamic reliability analysis for non-stationary non-Gaussian response based on the bivariate vector translation process

Calculation of probability of exceedance for nonstationary non-Gaussian responses remains a great challenge to researchers in the field of structural reliability. In this paper, an analytical solution is proposed for calculating the mean upcrossing rate (MCR) of the non-stationary non-Gaussian responses by approximating the displacement and velocity responses with the bivariate vector translation process, in which the unified Hermite polynomial model (UHPM) is selected as the mapping function. The first four moments (i.e., mean value, standard deviation, skewness, and kurtosis) and cross-correlation function of the displacement and velocity responses needed in UHPM are estimated from some representative samples generated by random function-spectral representation method (RFSRM) and time-domain analysis. Under the Poisson assumption of the upcrossing events, the calculation of extreme value distribution or probability of exceedance for structural response can be determined with the proposed method. The proposed method is applicable to a wide range of structural responses, including asymmetric and hardening or softening responses. Three numerical examples are provided to demonstrate the efficiency and accuracy of the proposed method. It can be concluded that the proposed method provides an accurate and useful tool for dynamic reliability assessment in engineering applications.     

Reliability analysis of structures based on a probability‐uncertainty hybrid model

The traditional reliability analysis method based on probabilistic method requires probability distributions of all the uncertain parameters. However, in practical applications, the distributions of some parameters may not be precisely known due to the lack of sufficient sample data. The probabilistic theory cannot directly measure the reliability of structures with epistemic uncertainty, ie, subjective randomness and fuzziness. Hence, a hybrid reliability analysis (HRA) problem will be caused when the aleatory and epistemic uncertainties coexist in a structure. In this paper, by combining the probability theory and the uncertainty theory into a chance theory, a probability‐uncertainty hybrid model is established, and a new quantification method based on the uncertain random variables for the structural reliability is presented in order to simultaneously satisfy the duality of random variables and the subadditivity of uncertain variables; then, a reliability index is explored based on the chance expected value and variance. Besides, the formulas of the chance theory‐based reliability and reliability index are derived to uniformly assess the reliability of structures under the hybrid aleatory and epistemic uncertainties. The numerical experiments illustrate the validity of the proposed method, and the results of the proposed method can provide a more accurate assessment of the structural system under the mixed uncertainties than the ones obtained separately from the probability theory and the uncertainty theory.     

A new reliability analysis method for uncertain structures with random and interval variables

In this paper, a new reliability analysis method is developed for uncertain structures with mixed uncertainty. In our problem, the uncertain parameters with sufficient information are treated by random distributions, while some ones with limited information can only be given variation intervals. A complex nesting optimization will be involved when using the existing methods to compute such a hybrid reliability, which will lead to extremely low efficiency or instable convergence performance. In this paper, an equivalent model is firstly created for the hybrid reliability, which is a conventional reliability analysis problem with only random variables. Thus only through computing the reliability of the equivalent model the original hybrid reliability can be easily evaluated. Based on the above equivalent model, an algorithm with high efficiency and robust convergence performance is then constructed for computation of the above hybrid reliability with both random and interval variables. Two numerical examples are provided to demonstrate the effectiveness of the present method.     

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.     

Translation vectors with non-identically distributed components

A model for non-Gaussian random vectors is presented that relies on a modification of the standard translation transformation which has previously been used to model stationary non-Gaussian processes and non-Gaussian random vectors with identically distributed components. The translation model has the ability to exactly match target marginal distributions and a broad variety of correlation matrices. Joint distributions of the new class of translation vectors are derived, as are upper and lower bounds on the target correlation that depend on the target marginal distributions. Examples are presented that demonstrate the applicability of the approach to the modelling of heterogeneous material properties, and also illustrate the possible shortcomings of using second moment characterizations for such random vectors. Lastly, an outline is given of a method under development for extending the model to non-stationary, non-Gaussian random processes.     

Reliability sensitivity analysis with random and interval variables

In reliability analysis and reliability‐based design, sensitivity analysis identifies the relationship between the change in reliability and the change in the characteristics of uncertain variables. Sensitivity analysis is also used to identify the most significant uncertain variables that have the highest contributions to reliability. Most of the current sensitivity analysis methods are applicable for only random variables. In many engineering applications, however, some of uncertain variables are intervals. In this work, a sensitivity analysis method is proposed for the mixture of random and interval variables. Six sensitivity indices are defined for the sensitivity of the average reliability and reliability bounds with respect to the averages and widths of intervals, as well as with respect to the distribution parameters of random variables. The equations of these sensitivity indices are derived based on the first‐order reliability method (FORM). The proposed reliability sensitivity analysis is a byproduct of FORM without any extra function calls after reliability is found. Once FORM is performed, the sensitivity information is obtained automatically. Two examples are used for demonstration. Copyright © 2009 John Wiley & Sons, Ltd.