联合分布函数构造的Copula函数方法及结构可靠度分析

不完备概率信息条件下变量联合分布函数的确定及其对结构可靠度的影响还缺少系统地研究。为此,提出了基于Copula函数的变量联合概率分布函数构造方法,并分析了不同Copula函数类型对结构可靠度的影响规律。首先,简要介绍了基于Copula函数的变量联合分布函数构造方法。其次,提出了构件失效概率计算的直接积分方法。最后以构件可靠度问题为例研究了Copula函数的类型对结构可靠度的影响规律。结果表明:不完备概率信息条件下构件可靠度是不唯一的,表征变量间相关性的Copula函数类型对构件可靠度具有明显的影响,不同Copula函数计算的构件失效概率存在明显的差别,这种差别随构件可靠指标的增大(或失效概率的减小)而增大。Copula函数尾部相关性对结构可靠度具有重要的影响。当功能函数的失效区域位于Copula函数尾部时,计算的失效概率明显比没有尾部相关性的Copula函数的失效概率大。基于功能函数的均值和标准差计算的可靠指标不能反映Copula函数的类型对结构可靠度的影响,而基于功能函数实际分布求得的失效概率则可以有效反映不同Copula函数对结构可靠度的影响。     

联合分布函数蒙特卡罗模拟及结构可靠度分析

针对复杂极限状态方程可靠度计算问题,提出了基于理论联合分布函数以及2 种近似联合分布函数的结构失效概率蒙特卡罗模拟方法,并给出了计算流程图.采用2 个算例证明了所提方法的有效性.结果表明:所提的失效概率模拟方法的计算精度很高,尤其适用于复杂极限状态方程的可靠度计算问题.2 种联合分布函数近似构造方法得到的失效概率精度相当,近似方法与精确方法结果的差异随失效概率的减小而增大,而且随着变量间相关性的增加而增加.当失效概率小于10-3时,近似方法的失效概率误差较大.     

二维联合概率密度函数构造方法及结构并联系统可靠度分析

该文目的在于研究二维联合概率密度函数构造方法对结构系统可靠度的影响规律。首先简要介绍了2种构造联合分布函数的近似方法:基于Pearson相关系数的近似方法P和基于Spearman相关系数的近似方法S。提出了基于直接积分方法的并联系统失效概率计算方法。算例结果表明2种近似方法计算的系统失效概率误差取决于系统失效概率的大小、功能函数的形式以及功能函数间相关程度。系统失效概率越小,近似方法计算的系统失效概率误差越大。当系统失效概率小于10?3量级时,近似方法计算的系统失效概率误差较大,工程应用中应该引起足够的重视。功能函数间负相关时近似方法的误差明显大于功能函数间正相关时的误差。此外,系统失效概率误差并不是随着功能函数间相关性的增加而单调增加。     

二维联合分布函数构造方法及其对结构可靠度的影响分析

研究了2种构造联合分布函数的近似方法:基于Pearson相关系数的近似方法P和基于Spearman相关系数的近似方法S。推导了基于直接积分方法的构件失效概率计算公式。结果表明近似方法P与近似方法S构造的联合概率密度函数的唯一差别是相关标准正态空间中Pearson 相关系数的不同。2 种近似方法的构件失效概率的计算精度取决于失效概率大小、功能函数形式及变量间相关程度。总体来说2种近似方法的计算精度较高,近似方法的失效概率的误差随着失效概率的减小而增加。只有当失效概率小于10-3且失效区域刚好可以反映失效概率差异时,近似方法得到的失效概率才会有较大的误差。     

桥梁系统地震易损性分析的混合Copula函数方法

为了在桥梁系统易损性分析中考虑构件地震需求之间相关性的影响,采用贝叶斯加权平均方法构造混合Copula函数,将构件地震需求之间的相关结构和各构件的边缘分布函数进行分离;结合增量动力分析,建立了桥墩、支座等单个构件的易损性曲线及联合分布函数,提出了考虑构件地震需求相关性的桥梁系统易损性分析方法。结果表明:混合Copula函数能够准确描述构件地震需求间上、下尾相关并存的非对称相关结构,简化构件地震需求联合分布函数的建模过程;与Monte Carlo抽样方法相比,二者得到的桥梁系统易损性吻合良好,且混合Copula函数方法避免了大量的数值抽样,显著提高系统易损性分析的计算效率。     

考虑变量相关性的正交异性板细节疲劳可靠性评估

传统的疲劳可靠性评估方法忽略了功能函数中变量的相关性,致使计算得到疲劳可靠度指标精度降低。针对这一问题,基于既有悬索桥主梁细节应变和环境温度大样本监测数据,讨论疲劳功能函数变量之间的相关性关系。提出基于Copula函数的相关性变量的联合分布模型,解决联合概率分布建模中多重积分求解困难的问题。研究表明,受环境温度的影响,靠近顶板焊接细节的日等效应力幅和应力循环次数存在较强的相关性,Gaussian Copula函数可作为相关性变量的最优连接函数,对于U肋-顶板细节,桥梁结构服役100年时,考虑与不考虑变量相关性的可靠度指标值分别5.3和6.9,两者之间相差约1.3倍。当年交通增长率分别等于3%和5%时,可靠度指标在服役年限为94.6年和67.1年时间到达目标可靠度指标。对于U肋-U肋对接细节,考虑和不考虑变量的相关性对可靠性指标影响相对顶板细节较小。     

随机非线性系统失效模式动态相关性分析

基于结构破坏的首次穿越模型,应用随机结构分析概率摄动法确定系统响应及系统不同失效模式之间动态相关性.数值算例表明,随机非线性系统不同位移之间、位移与速度之间均存在相关性;系统不同失效模式间同样具有相关性,并且在考察的大部分状态下,这种相关性具有动态、强相关等特点,相关失效是系统失效的主要模式.数值算例中计算结果应用Monte Carlo随机模拟方法进行验证均较好吻合,表明了该方法是能够满足工程计算要求的,从而为非线性随机振动系统不同失效模式间动态相关系数的确定提供方法参考.     

响应面法在结构体系可靠度分析中的应用

一个失效模式由许多的失效单元构成,它是一个并联系统;而所有的失效模式构成一个串联系统。整个结构体系可看成是许多并联系统(失效模式)组成的一个串联系统。首先,利用基于响应面的随机有限元法来获得失效模式中各个单元的极限状态方程,这些方程都是二次多项式;第二步,利用结构可靠度分析中的几何法得到这些方程的等效线性化方程从而可逐步得到该失效模式的等效线性化方程;第三步,计算各失效模式间的相关系数;最后,由Ditlevsen界限法来计算结构的体系可靠度。算例表明,利用该方法来获得大型、复杂结构的体系可靠度具有高效、实用的特点。     

基于蒙特卡罗模拟的高效边坡可靠度修正方法

传统的蒙特卡罗模拟方法在分析由于参数不确定性修正而引起的可靠度修正问题时效率较低。为此,提出了一种基于蒙特卡罗模拟的高效边坡可靠度修正方法,该方法主要包括2个关键步骤:1)根据参数初始分布利用蒙特卡罗模拟方法计算边坡的失效概率,并输出蒙特卡罗模拟的失效样本;2)利用参数统计特征值修正后的联合概率密度函数和蒙特卡罗模拟失效样本计算修正后边坡的失效概率。以两个边坡问题为例说明了所提方法的有效性。结果表明:所提出的方法在计算修正的失效概率过程中无需重新执行蒙特卡罗模拟,计算过程简单、计算效率高。此外,所提方法能够适用于隐式表达功能函数的边坡可靠度修正问题,并能够有效地解决单变量和多变量修正的边坡可靠度修正问题。     

基于损伤控制函数与失效概率的结构抗震性能多目标优化与评估

为避免高层建筑结构由于薄弱层破坏而引起整体倒塌,该文提出基于楼层组损伤控制函数与失效概率的结构抗震性能多目标优化方法。该方法通过增量动力分析选择结构失效概率达到50%的峰值加速度为目标地震动,定义楼层组损伤控制函数及失效概率两个性能指标,以构件截面尺寸为优化变量对结构进行优化分析。对一30层钢筋混凝土框筒结构进行优化,并基于PACT（Performance Assessment Calculation Tool）平台对优化前后结构的抗震性能进行评估。结果表明,优化后结构各层层间位移角分布趋于均匀且自上而下损伤逐渐减小,倒塌储备系数增加29.8%;外框架与核心筒修复费用均降低,墙与框架协同作用加强,优化后结构的抗震性能显著提高。     

A system reliability estimation method by the fourth moment saddle point approximation and copula functions

Despite many advances in the field of computational system reliability analysis, estimating the joint probability distribution of correlated non-normal state variables on the basis of incomplete statistical data brings great challenges for engineers. To avoid multidimensional integration, system reliability estimation usually requires the calculation of marginal failure probability and joint failure probability. The current article proposed an integrated approach for estimating system reliability on the basis of the high moment method, saddle point approximation, and copulas. First, the statistic moment estimation based on the stochastic perturbation theory is presented. Thereafter, by constructing CGF (concise cumulant generating function) for the state variable with its first four statistical moments, a fourth moment saddle point approximation method is established for the component reliability estimation. Second, the copula theory is briefly introduced and extensively utilized two-dimensional copulas are presented. The best fit copula for estimating the probability of system failure is selected according to the AIC (Akaike Information Criterion). Finally, the derived method is applied to three numerical examples for the sake of a comprehensive validation.     

Data-driven reliability assessment with scarce samples considering multidimensional dependence

This study proposes a data-driven method for assessing reliability, based on the scarce input dataset with multidimensional correlation. Since considering the distribution parameters estimated from the scarce dataset as those of the population may lead to epistemic uncertainty, the bootstrap resampling algorithm is adopted to infer the distribution parameters as interval parameters. To account for the variable dependence, vine copula theory is utilized to construct the joint probability density function (PDF) of input variables, and maximum likelihood estimation (MLE) and Akaike information criterion (AIC) analysis are employed to select optimal copulas based on the samples for the vine structure. Subsequently, the failure probability bounds of a response function are calculated based on the constructed joint PDF with interval distribution parameters by the active learning Kriging (AK) method combining the sparse grid integration (SGI) method. Finally, several examples are provided to demonstrate the feasibility and efficiency of the proposed method.     

样本数目对岩土体参数联合分布模型识别精度的影响

目前样本数目对岩土体参数联合概率分布模型识别精度的影响还缺少研究。该文提出了样本数目对岩土体参数联合分布模型识别精度的影响分析方法,给出了基于蒙特卡洛模拟的统计量AIC值变异性模拟步骤,定义了描述岩土体参数联合概率分布模型识别精度的正确识别概率,采用蒙特卡洛模拟方法分别研究了样本数目对岩土体参数最优边缘分布函数和最优Copula函数识别精度的影响规律。结果表明:基于有限岩土体参数数据估计的边缘分布函数和Copula函数的AIC值存在较大的变异性。岩土体参数样本数目对最优边缘分布函数和Copula函数的识别精度具有重要的影响,边缘分布函数和Copula函数的正确识别概率随样本数目的增加而增大。岩土体参数变异系数对最优边缘分布函数的识别精度影响相对较小,岩土体参数间相关系数对最优Copula函数的识别精度影响较大。此外,岩土体参数二维分布模型识别比一维边缘分布模型识别需要更多的数据。因此,为了提高岩土体参数联合概率分布模型的识别精度,建议尽可能多地收集岩土体参数试验数据。     

System-level load–strength interference based reliability modeling of k-out-of-n system

The issue of information loss in the process of system reliability modeling through conventional load–strength interference analysis is discussed first, and the reason why it is impossible to construct dependent system reliability model simply by means of component reliability index is demonstrated. Then, an approach to modeling the reliability of dependent system with common cause failure (CCF) is presented. The approach is based on system-level load–strength interference analysis and a concept of ‘conditional failure probability of component’ as well. With the opinion that load randomness is the direct cause of failure dependence, a discrete type system reliability model is developed via the conditional component failure probability concept. At last, the model's capabilities to estimate system reliability with CCF effect and to predict high multiplicity failure probability based on low multiplicity failure event data are proved.     

System Reliability and Component Importance Under Dependence: A Copula Approach

System reliability and component importance are of great interest in reliability modeling, especially when the components within the system are dependent. We characterize the influence of dependence structures on system reliability and component importance in coherent systems with discrete marginal distributions. The effects of dependence are captured through copula theory. We extend our framework to coherent multi-state system. Applications of the derived results are demonstrated using a Gaussian copula, which yields simple interpretations. Simulations and two examples are presented to demonstrate the importance of modeling dependence when estimating system reliability and ranking of component importance. Proofs, algorithms, code, and data are provided in supplementary materials available online.     

Multivariate Degradation Modeling of Smart Electricity Meter with Multiple Performance Characteristics via Vine Copulas

For smart electricity meter with multiple performance characteristics (PCs) with coupling relationships because of amounts of components experiencing multiple deteriorating processes, we develop a multivariate degradation modeling method via vine copulas to estimate the reliability of products with multiple PCs reflecting degradation states. In the multivariate model, pair‐copula construction and vine graphical representation are used to describe the mutual relationship among PCs to overcome the lack of multivariate copula in high‐dimensional cases. Each PC model of smart electricity meter is built by using drift Brownian motion to describe degradation processes of each PC on the basis of degradation mechanism analysis, and parameters are estimated by using likelihood estimation method. The Pearson correlation coefficient, Kendall's τ and product information are used to analyze correlation among those PCs. Furthermore, on the basis of conditional probability theory, the vine graph is used to construct a multivariate copula which can be decomposed into pair copulas. Akaike information criterion principle is utilized to choose the forms of pair‐copula functions in the correlation model. Finally, the reliability joint distribution of all PCs of smart electricity meter is obtained with combining all PCs' marginal distributions and copula functions. Copyright © 2016 John Wiley & Sons, Ltd.     

Reliability modeling of correlated competitions and dependent components with random failure propagation time

Function dependence takes place in systems where the malfunction of certain trigger component(s) causes other system components (referred to as dependent components) to become unusable or inaccessible. Systems undergoing function dependence often exhibit diverse statuses due to competitions in the time domain between propagated failure from a dependent component and local failure of the corresponding trigger component. If the former wins (ie, occurring first), a failure propagation effect is induced, crashing the entire system. If the latter wins, a failure isolation effect may be induced quarantining the damage from the propagated failure (the isolation effect can occur in a deterministic or probabilistic manner depending on applications). Existing models addressing such competitions have restrictive assumptions such as uncorrelated competitions from multiple function dependence groups, zero or negligible failure propagation time, and deterministic failure isolation effect. This paper advances the state of the art by proposing a combinatorial reliability model for systems undergoing correlated, probabilistic competitions and random failure propagation time for dependent components. A case study of a wireless body area network system for patient monitoring is performed to illustrate the proposed methodology and effects of different model parameters on the system reliability.     

基于Copula函数的桥梁系统地震易损性方法研究

桥梁系统地震易损性分析的关键是建立桥墩、支座等多个构件的联合概率分布函数。然而,由于构件地震需求之间的相关性,直接建立构件之间的联合概率分布函数较为困难。为此,引入Copula函数方法,将构件地震需求之间的相关性和各构件的边缘概率分布函数进行分离,从而简化了联合分布函数的建模过程。在桥墩、支座地震易损性的基础上,基于Copula联合概率分布函数,建立了桥梁系统的易损性曲线,并将其和一阶界限法及Monte Carlo方法的分析结果进行对比。结果表明:基于Copula函数得到的桥梁系统易损性在整个地震动强度范围内均位于一阶界限法的上、下界之间;和Monte Carlo方法相比,Copula函数方法不仅考虑了构件地震需求之间的非线性相关关系,而且避免了大量的数值抽样,使计算效率显著提高。     

Reliability assessment for failure-dependent and uncertain systems: A Bayesian network based on copula method and probability-box

Managing failure dependence of complex systems with hybrid uncertainty is one of the hot problems in reliability assessment. Epistemic uncertainty is attributed to complex working environment, system structure, human factors, imperfect knowledge, etc. Probability-box has powerful characteristics for uncertainty analysis and can be effectively adopted to represent epistemic uncertainty. However, arithmetic rules on probability-box structures are mostly used among structures representing independent random variables. In most practical engineering applications, failure dependence is always introduced in system reliability analysis. Therefore, this paper proposes a developed Bayesian network combining copula method with probability-box for system reliability assessment. There are four main steps involved in the reliability computation process: marginal distribution identification and estimation, copula function selection and parameter estimation, reliability analysis of components with correlations and Bayesian forward analysis. The benefits derived from the proposed approach are used to overcome the computational limitations of n-dimensional integral operation, and the advantages of useful properties of copula function in reliability analysis of systems with correlations are adopted. To demonstrate the effectiveness of the developed Bayesian network, the proposed method is applied to a real large piston compressor.