结构动力可靠度计算的区域分解法和重要抽样法

Lambros Katafygiotis提出的区域分解法和S.K.Au提出的重要抽样法,被用来计算受高斯随机过程作用的线性动力体系的失效概率.在以上两种方法的基础上,给出了非平稳随机地震作用下,单自由度线性体系动力可靠度的区域分解法和重要抽样法的计算步骤,对以上两种方法补充给出了选取失效域下标的措施;其中区域分解法将结构反应用离散脉冲响应函数表示,补充给出了极限状态函数系数的具体确定方法.通过算例对比分析了区域分解法和重要抽样法的计算效率,结果表明当计算小失效概率时,区域分解法的计算效率同重要抽样法一样高效,而对比Monte-Carlo法有显著提高,且两种方法的计算精度接近.     

基于子集模拟法的非线性结构动力可靠度计算

子集模拟法是计算结构动力可靠度的新方法。该文在子集模拟法的基本思想上,适应性的选取子失效域的反应界限,合理的选取建议概率密度函数,应用修正的Metropolis算法产生计算条件失效概率时所需的随机变量样本,给出了所需样本的具体个数,并通过上述关键点总结给出了子集模拟法的具体计算步骤。最后应用子集模拟法首先计算了受平稳高斯白噪声作用的单自由度Duffing体系的失效概率,当确定落入失效域的样本时,需计算结构的非线性反应,该文采用迭代的数值逐步积分法进行计算;随后计算了受平稳高斯白噪声作用的采用三线型恢复力模型的三自由度结构的失效概率,结果表明:子集模拟法计算非线性结构动力可靠度时具有高效及高精度的特点,尤其适用于小失效概率的计算。     

结构动力可靠度计算的修正条件反应法

基于反应时程分解的子集模拟法是计算受随机激励作用的结构小失效概率的高效算法,该法将失效域划分为子集序列,通过分解反应时程来生成子集计算所需的样本。条件反应法计算动力可靠度时,通过线性反应来估计非线性反应,或通过单自由度结构的反应来估计多自由度结构的反应。该文将基于反应时程分解的子集模拟法用于条件反应法中,提出修正的条件...     

基于失效概率积分和马尔可夫链模拟的可靠性灵敏度分析

可靠性灵敏度可以被表达为失效概率对基本随机变量分布参数的偏导数的形式,利用失效概率为基本变量的联合概率密度函数在失效域上的积分表达式,并且利用马尔可夫链能够高效模拟感兴趣区域样本的性质,一种针对单个失效模式和系统多个失效模式的可靠性灵敏度分析方法被提出。由于可靠性参数灵敏度可以表达为一个与联合概率密度函数相关的函数在失效域中的数学期望的形式,所提方法采用马尔可夫链来高效模拟失效域中的样本,进而采用样本均值替代总体均值的方法来得到可靠性灵敏度的估计值。与已有的基于Monte-Carlo模拟的可靠性灵敏度分析方法相比,所提方法在保证计算精度的基础上计算效率有显著提高,尤其是针对小失效概率的可靠性灵敏度分析问题。该算例充分说明了所提方法的合理可行性。     

基于响应面法和Morgenstern-Price法土坡可靠度计算方法

基于响应面法,建立了一种高效的边坡可靠度指标和失效概率近似计算方法。该法在构造响应面函数时,抽样点计算采用Morgenstern-Price法取代传统费时的有限单元法,大大降低了计算工作量。利用Monte-Carlo随机抽样原理,提出了一种能同时确定边坡最危险非圆弧滑动面和最小可靠度指标的随机搜索新算法。该文给出的两个算例验证了方法的实用性和可靠性,其计算结果同时表明:当分别以最小可靠度指标和最小中值安全系数为目标函数时,搜索到的边坡最危险滑动面相差较大。最后,探讨了土性指标(c,φ)的分布概型及相关性对边坡可靠度计算结果的影响。     

运动方程时程单元先验步长估计初探

基于单元能量投影(element energy projection,EEP)法和边值问题固端法的思想,将其扩展至运动方程问题。该文以单自由度线性元为例,采用Taylor级数渐近展开,对问题的求解进行实质性简化计算;探讨了不经有限元求解便可进行先验定量误差估计的算法;进而实现了自适应单元步长的先验估计和确定。该文给出初步算例,验证了该方法的可行性和有效性。     

自适应重要抽样方法的改进算法

失效概率的计算是结构可靠度分析的核心问题之一,发展精确高效的失效概率估计方法渐成国际学术与工程界关注的焦点。该文提出了一种基于样本概率密度加权的采样中心确定方法,该方法兼顾了以下2个目标:1) 增加有效抽样中对失效概率贡献大的样本出现的概率;2) 提高有效抽样比例。通过将该方法与基于主动引导技术的自适应抽样方法相集成,得到了一种改进的自适应重要抽样方法。理论分析与数值算例表明:该文提出的自适应重要抽样算法具有精度高、计算量小的优点。     

运动方程自适应步长求解的一个新进展——基于EEP超收敛计算的线性有限元法

该文采用最简单的Galerkin型线性单元,对运动方程构建了简捷高效的单步法递推公式;进而基于EEP超收敛计算技术,开发了单元步长自动优化和结点位移精度修正两项关键技术,可在整个时域上得到误差分布均匀且逐点满足给定的误差限的解答——堪称数值解析解。该文给出了单自由度和多自由度的数值算例以验证本法的有效性。     

非平稳随机激励下结构动力可靠度时域显式子集模拟法

基于动力响应显式表达式,时域显式随机模拟法可以通过减少单次样本计算时间有效提高动力可靠度的计算效率。然而,对于小失效概率问题,由于需要大量次样本计算,该法的计算量仍相当可观。为了克服上述困难,在时域显式随机模拟法基础上引入子集模拟法的基本思想,把小失效概率表示为一系列较大的条件概率的乘积,其中各条件概率采用时域显式随机模拟法计算,条件域内的样本采用Metropolis-Hastings抽样方法生成,从而实现了减少随机模拟所需的样本数,进一步提高了计算效率。算例结果表明改进的方法具有更高的计算效率,更适用于小失效概率和多自由度结构的动力可靠度问题。     

基于拒绝抽样算法的结构体系可靠度更新

该文着重研究基于观测信息的结构体系可靠度更新模型及其拒绝抽样算法。基于Bayesian理论建立考虑观测信息的结构体系失效概率更新模型,根据观测信息事件类型建立不等式和等式观测信息条件下随机变量的似然函数并推导其后验概率密度函数;基于观测信息域确定随机变量后验样本的拒绝抽样策略,探究拒绝抽样算法的抽样效率,推导更新后结构体系失效概率估计值及其标准差的计算公式;将上述方法应用于刚架结构发生塑性失效时体系可靠度更新计算。研究表明:考虑观测信息的结构体系条件失效概率更新模型可转化为随机变量后验概率密度在失效域上的积分,构造满足观测信息域的先验样本作为随机变量后验样本的抽样策略是可行的,该抽样策略可以处理多随机变量、多观测信息条件下结构体系可靠度更新;与抗力相关随机变量检测值增大及验证荷载值提高均可以降低更新后结构体系的失效概率,与抗力相关的随机变量还需控制其检测误差的标准差,以降低观测信息的不确定性。     

First excursion probabilities for linear systems by very efficient importance sampling

An analytical study of the failure region of the first excursion reliability problem for linear dynamical systems subjected to Gaussian white noise excitation is carried out with a view to constructing a suitable importance sampling density for computing the first excursion failure probability. Central to the study are ‘elementary failure regions’, which are defined as the failure region in the load space corresponding to the failure of a particular output response at a particular instant. Each elementary failure region is completely characterized by its design point, which can be computed readily using impulse response functions of the system. It is noted that the complexity of the first excursion problem stems from the structure of the union of the elementary failure regions. One important consequence of this union structure is that, in addition to the global design point, a large number of neighboring design points are important in accounting for the failure probability. Using information from the analytical study, an importance sampling density is proposed. Numerical examples are presented, which demonstrate that the efficiency of using the proposed importance sampling density to calculate system reliability is remarkable.     

Decomposition methods for structural reliability analysis

A new class of computational methods, referred to as decomposition methods, has been developed for predicting failure probability of structural and mechanical systems subject to random loads, material properties, and geometry. The methods involve a novel function decomposition that facilitates univariate and bivariate approximations of a general multivariate function, response surface generation of univariate and bivariate functions, and Monte Carlo simulation. Due to a small number of original function evaluations, the proposed methods are very effective, particularly when a response evaluation entails costly finite-element, mesh-free, or other numerical analysis. Seven numerical examples involving elementary mathematical functions and solid-mechanics problems illustrate the methods developed. Results indicate that the proposed methods provide accurate and computationally efficient estimates of probability of failure.     

An active learning Kriging model combined with directional importance sampling method for efficient reliability analysis

In this paper, the active learning Kriging model (ALK), which has been studied extensively in recent years, has been expanded by combining with the directional importance sampling (DIS) method. The directional sampling method can reduce the dimensionality of the variable space by random sampling or interpolation in the direction of vector diameter, which can improve the efficiency of reliability analysis. It is especially suitable for the surfaces whose limit state is spherical or near-spherical. By introducing the control coefficient and constructing the directional importance sampling density function, the sampling efficiency can be further improved in the design point domain. A novel reliability analysis method called ALK-DIS method is proposed. The greatest advantage of the proposed method is its ability on great computational efficiency and dealing with small failure probability problem In addition, due to the excellent performance of directional sampling method in dealing with multi-failure model reliability problems, the ALK-DIS method has the advantage of being applied to system reliability analysis in this paper successfully. The applicability, feasibility and efficiency of the proposed method are proved on examples which contain linearity equation, non-linear numerical example, non-linear oscillator and system reliability engineering problems.     

A new directional simulation method for system reliability. Part I: application of deterministic point sets

To assess structural system reliability accurately, simulation is often the only feasible method because of dimensionality, highly nonlinear limit states, small failure probability and other factors. However, simulations can be computationally inefficient when the reliability assessment involves finite element analysis and the cost of structural analysis is large. Efficient directional simulation, as well as other simulation techniques, often can be improved in terms of accuracy and efficiency if the sample points are identified by deterministic point sets rather than generated randomly. This article introduces and investigates various deterministic point sets theoretically and experimentally in conjunction with directional simulation, and identifies one particular point set (Fekete point set) as being especially useful in this regard. New test measures are proposed to evaluate the quality and uniformity of point sets, which are essential in preserving the underlying probability distribution. A companion paper presents a point set refinement scheme using neural networks, which is a technique parallel to importance sampling.     

模糊可靠性灵敏度分析的线抽样方法

依据失效域具有模糊性时模糊失效概率的定义,提出了模糊可靠性灵敏度分析方法。推导了线性功能函数、独立正态基本变量和正态型隶属函数情况下,模糊可靠性灵敏度的解析表达式。给出了模糊可靠性灵敏度的Monte Carlo数字模拟方法,该方法结果在模拟次数趋于无穷时,收敛于真值,但效率较低,尤其是针对高维和小失效概率问题。为解决数字模拟法效率低的问题,提出了模糊可靠性灵敏度分析的线抽样方法。通过离散模糊失效概率积分区域,建立了模糊可靠性灵敏度与离散区域随机可靠性灵敏度的关系,进而利用随机可靠性灵敏度分析的线抽样方法求得模糊可靠性灵敏度。该方法的基本原理、计算公式及实现步骤被详细给出,适用于高维问题和小失效概率、精度高及收敛快等优点则由该文的算例进行验证。     

Importance sampling for stochastic systems under stationary noise having a specified power spectrum

We develop an importance sampling simulation scheme for estimating an extremely small probability of system failure with respect to a time-dependent stochastic system excited by stationary random noise having a specified power spectrum. First, we construct a system of random differential equations driven by a Wiener process, which can approximately give such a stationary random noise by the use of an extended version of the well-known Ornstein–Uhlenbeck process. Next, we suppose a stochastic response system driven by the constructed stationary random noise. Next, formulating the probability of system failure, we give an importance sampling scheme through the probability measure transformation based upon the Girsanov theorem, where multi design times are introduced to cope with stationary or almost stationary behavior of the system. Finally, we give numerical examples to demonstrate the efficiency of the proposed scheme.     

基于失效概率偏导数的局部与全局混合灵敏度分析

基于失效概率偏导数的局部灵敏度与矩独立的全局灵敏度定义了一种新的混合灵敏度指标,该灵敏度指标不仅继承了传统的矩独立灵敏度的优点,而且反映了矩独立灵敏度和基于方差的灵敏度之间的内在联系。针对该矩独立的混合灵敏度指标计算量大的问题,该文首先将其转化为基于方差的混合灵敏度指标,然后利用能够高效计算条件矩的态相关参数(SDP)法进行求解。为了进一步提高计算效率,该文建立了基于重要抽样和截断重要抽样的SDP方法。算例结果验证了该文所提指标的合理性及所提方法的准确性。方法方法     

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。