Probability density evolution analysis of stochastic seismic response of structures with dependent random parameters

Performance evaluation and reliability assessment of real-world structures under earthquakes is of paramount importance. Generally, different mechanical property parameters of a structure are usually not independent, nor completely dependent, but partly dependent or correlated. Therefore, how to reasonably characterize such partial dependency and whether such partial dependency real matters in the stochastic response and reliability of structures under earthquakes are crucial issues. For this purpose, in the present paper, a novel physically-guided data-driven methodology of capturing the correlation configuration of basic random variables and the probability density evolution method are synthesized. The physically-guided data-driven methodology is firstly outlined. In this methodology, the underlying physical mechanism between dependent random variables is firstly involved to establish a random function model, and then the available observed data are adopted to identify the parameters in this model. What is more, physical constraints are also revealed for the initial modulus of elasticity and compressive strength of concrete. The probability density evolution method is then adopted, and the point selection by minimizing the GF-discrepancy is adjusted according to the correlation configuration and physical constraints. A reinforced concrete frame structure subjected to earthquake input is studied. It is found that when the structure is in the strongly nonlinear stage, the correlation configuration has considerable effects on the standard deviation of the stochastic responses, by a factor of nearly 2. In addition, whether the mechanical parameters in different floors are independent or not has great effects on the stochastic responses as well. Problems to be further studied are also outlined.     

随机结构反应概率密度演化分析的切球选点法

发展了随机结构反应概率密度演化分析中随机参数空间的切球选点法。密度演化方法是一类直接获取随机结构动力反应概率密度函数及其演化过程的有效方法。在多个随机变量时，随机变量空间中的离散代表点选点规则直接关系到密度演化方法的精度和效率。本文构造了平面内等半径相切圆圆心分布定位的算法，以此为基础，建立了三维空间中等半径相切球球心坐标定位的计算公式。从而给出随机变量空间中的离散代表点及其赋得概率。计算表明，基于空间切球法的选点规则具有良好的精度和效率，在2个和3个随机变量情况下是较为理想的选点方法。     

Sampling of closely‐spaced ordered set of uniformly distributed random variables

When the individual PDFs of closely‐spaced random variables such as natural frequencies of a structure overlap, generation of sample sets by assuming the frequencies to be independent random variables can lead to incorrect sets of frequencies in the sense that the frequencies do not remain as ordered sets. Rejection of such disordered sample sets results in individual density functions that are significantly different from the distributions initially assumed for sampling each random variable. One way to overcome this constraint in the simulation of an ordered set of random variables is to consider them in an implicit manner using a joint PDF. In this paper, we present a formulation for a joint density function that is developed using fundamental probability approaches. The formulation ensures that the sampled random variables always remain as ordered sets and maintain the individual density functions for each variable. Application of the proposed formulation is illustrated for cases with not just two closely‐spaced variables but also for a case with multiple closely‐spaced variables such that the PDFs of more than two random variables overlap with each other. An expression is presented to determine the exact number of terms needed in the formulation. However, it is also illustrated that only two terms are sufficient in most applications even when the exact number of terms needed is very high. Copyright © 2011 John Wiley & Sons, Ltd.     

Reliability assessment models for dependent competing failure processes considering correlations between random shocks and degradations

This article develops reliability models for systems subject to two dependent competing failure processes, considering the correlation between additional damage size on degradation in soft failure process and stress magnitude of shock load in hard failure process, both of which are caused by the same kth random shock. The generalized correlative reliability assessment model based on copulas is proposed, which is then extended to three different shock patterns: (1) δ‐shock, (2) m‐shock, and (3) m‐run shocks. There are some statistical works to be introduced in reliability modeling, including data separation of total degradation amount, inferring the distribution of amount of aging continuous degradation at time t, and fitting copula to the specific correlation. The developed reliability models are demonstrated for an application example of a micro‐electro‐mechanical system.     

Error estimate of point selection in uncertainty quantification of nonlinear structures involving multiple nonuniformly distributed parameters

The error analysis for the selection of representative point sets (RPSs) in multidimensional random-variate space assigned with arbitrary nonuniform distribution with compact support for uncertainty quantification is developed by extending the Koksma-Hlawka inequalities, which bound the worst error with some kind of discrepancy of the RPS and the variation of the integrand. The novel concepts of the EF-discrepancy and the GF-discrepancy are introduced, and the connection and equivalence between them are inquired into. Based on such theoretical basis, the error estimate of selecting RPSs in the standardized space instead of that in the original physical space is studied, showing that the standardization of the input random variables does not increase, but usually reduce, the error bound by GF-discrepancy. The extended Koksma-Hlawka inequality also establishes the theoretical basis for the error estimate of the probability density evolution method. Besides, the closed-form expressions for the EF-discrepancy are given in the Appendix. A numerical example involving a complex nonlinear engineering structure modeled by the finite element method is studied, showing the accuracy of the proposed approach.     

The dimension-reduction strategy via mapping for probability density evolution analysis of nonlinear stochastic systems

Dynamic response analysis of nonlinear structures involving random parameters has for a long time been an important and challenging problem. In recent years, the probability density evolution method, which is capable of capturing the instantaneous probability density function (PDF) of the dynamic response and its evolution, has been proposed and developed for nonlinear stochastic dynamical systems. In the probability density evolution method, the strategy of selecting representative points is of critical importance to the efficiency especially when the number of random parameters is large. Enlightened by Cantor’s set theory, a strategy of dimension-reduction via mapping is proposed in the present paper. In the strategy, a two-dimensional domain is firstly considered and discretized such that the grid points are assigned with probabilities associated to the joint PDF. These points are then sorted and set on a virtual line according to a certain principle. Partitioning the sorted points on the virtual line into a certain number of intervals and selecting one single point in each interval, the two random variables can be transformed to a single comprehensive random variable. The associated probability of each point is simultaneously transformed accordingly. In the case of multiple random parameters, the above dimension-reduction procedure from two to one could be used recursively such that the random vector is finally transformed to one single comprehensive random variable. Numerical examples are investigated, showing that the proposed method is of high efficiency and fair accuracy.     

Convergence of the fixed point theorem with random parameters

Solving stochastic non‐linear dynamical problems represents a formidable task which, in many cases, can be achieved solely through numerical simulation techniques. This is true for high dimensional as well as low dimensional problems. One method to deal with the non‐linearity is to use the fixed point theorem which gives the convergence conditions of the iterative scheme towards the equilibrium point of the equation. In this paper we look at the particular case where the equilibrium equation depends on a random variable. This case arises for instance in the study of coupled non‐linear dynamical systems when structural uncertainties are introduced in the dynamical systems. We give almost sure and L ^p convergence conditions for the simulation iterative scheme. Copyright © 2003 John Wiley & Sons, Ltd.     

Partition of the probability-assigned space in probability density evolution analysis of nonlinear stochastic structures

Based on a partition of probability-assigned space, a strategy for determining the representative point set and the associated weights for use in the probability density evolution method (PDEM) is developed. The PDEM, which is capable of capturing the instantaneous probability density function of responses of linear and nonlinear stochastic systems, was developed in the past few years. The determination of the representative point set and the assigned probabilities is of paramount importance in this approach. In the present paper, a partition of probability-assigned space related to the representative points and the assigned probabilities are first examined. The error in the resulting probability density function of the stochastic responses is then analyzed, leading to two criteria on strategies for determining the representative points and a set of indices in terms of discrepancy of the point sets. A two-step algorithm is proposed, in which an initial uniformly scattered point set is mapped to an optimal set. The implementation of the algorithm is elaborated. Two methods for generating the initial point set are outlined. These are the lattice point sets and the Number-Theoretical nets. A density-related transformation yielding the final point set is then analyzed. Numerical examples are investigated, where the results are compared to those obtained from the standard Monte Carlo simulation and the Latin hyper-cube sampling, demonstrating the accuracy and efficiency of the proposed approach.     

Energy-based collapse assessment of concrete structures subjected to random damage evolutions

The assessment of structural capacity against collapse is conducive to the optimal design of new structures as well as checking the safety of existing structures. However, the evaluation cannot be typically carried out by means of destructive tests on prototype or reduced scale structures. In this regard, the numerical models that adequately represent the prototype structures can be alternatively used. Specifically, both the nonlinearities and randomness as well as their coupling effect of materials need to be represented in a unified manner in structural analysis. The present paper aims at providing an effective approach to incorporate the stochastic nature of damage constitutive relationships in collapse analysis and assessment of concrete structures subjected to earthquake ground motions. Within the framework of stochastic damage mechanics, the spatial variability of concrete is represented by a two-scale stationary random fields. The concept of covariance constraint is introduced to bridge the two-scale random fields such that the scale-of-fluctuation of the random material property is satisfied at both scales. Random damage evolution induced structural collapse analysis is achieved via the nonlinear stochastic finite element method. To address the randomness propagation across scales, the probability density evolution method is employed. By exerting the absorbing boundary condition associated with an energy-based collapse criterion on the generalized probability density evolution equation, the anti-collapse reliability of concrete structures can be evaluated with fair accuracy and efficiency. Numerical investigation regarding an actual high-rise reinforced concrete frame-shear wall structure indicates that the random damage evolution of concrete dramatically affects the structural nonlinear behaviors and even leads to entirely different collapse modes. The proposed method provides a systematic treatment of both uncertainties and nonlinearities in collapse assessment of complex concrete structures.     

非平稳地震动过程模拟的谱表示-随机函数方法

在Priestley演变谱理论的基础上,采用随机函数的思想,建议了一类新的全非平稳过程模拟的谱表示-随机函数方法。在谱表示-随机函数方法中,实现了用2个基本随机变量即可精确表达原随机过程的目的。通过选取基本随机变量的离散代表点集,可以直接由演变功率谱密度函数生成具有给定赋得概率的代表性样本集合。以全非平稳地震动加速度过程的演变功率谱为例,验证了本文方法的有效性和优越性。最后,结合概率密度演化方法,进行了Duffing振子的随机地震反应分析与抗震可靠度计算。     

A new frequency domain method for random fatigue life estimation in a wide‐band stationary Gaussian random process

A new frequency domain method for random fatigue life estimation in a wide‐band stationary Gaussian random process was proposed for application in fatigue analysis. Simulations of the power spectral densities of different types were firstly performed; the simulated results showed that the accuracy and applicability for the current frequency domain methods are not only related to the spectral type but also associated with the types of the analysed materials. Compared with the current methods, the proposed method, in which the rain‐flow amplitude obeys Nakagami distribution, has better universality and could significantly reduce the error for the random fatigue life estimation with simulated and actual spectra. Verified application in cast‐steel fatigue life analysis were performed between random fatigue life and constant amplitude fatigue life. It is shown that the fatigue life analysis under random load cannot be ignored and the proposed new method can serve as a recommended method.     

基于模型结构振动台试验的概率密度演化方法验证

以广义概率密度演化方程为核心的概率密度演化方法可应用于一般随机动力系统的反应分析与可靠度评价。该文基于随机地震动作用下模型结构振动台试验实测数据,将试验模型典型动力响应的样本集合直接统计结果与概率密度演化分析结果进行对比,以从试验角度验证概率密度演化方法的正确性。研究结果表明,概率密度演化分析结果,无论从均值与标准差过程,还是典型时刻的概率分布上,均分别与样本统计结果吻合良好,从而证明了概率密度演化方法在随机动力系统分析中的精确性与可靠性。     

An improved sieve point method for the reliability analysis of structures

The efficiency of existing stochastic analysis method depends on the discretization of the random variables domain. The number theoretical method has been proposed to discretize the random variable space and solve the generalized density evolution equation via sampling strategy. This method traditionally involves hyper-ball sieving (HS) algorithm to sample the representative point set. However, the sieving radius of the hyper-ball is determined subjectively, and the efficiency and accuracy of the analysis depend on the selected radius. To avoid this subjective selection, an equal volume hyper-ball sieving method is presented in this paper. By transforming the hypercube spatial volume of random variables into that of an equivalent hyper-ball, the radius of the equal volume hyper-ball is obtained analytically. This radius is further optimized with a minimum star discrepancy in the representative point set. The performance and accuracy of the proposed method are checked in four numerical examples, and the representative point set such obtained is more uniform with smaller NRP leading to more accurate and efficient subsequent stochastic analysis than the HS method.     

基于概率密度演化理论的结构抗震可靠性分析

运用随机过程的正交展开方法,将地震动加速度过程表示为由10个左右的独立随机变量所调制的确定性函数的线性组合形式。结合概率密度演化方法和等价极值事件的基本思想,研究了非线性结构的抗震可靠度分析问题。以具有滞回特性的非线性结构为例,对某一多自由度的剪切型框架结构进行了抗震可靠性分析。结果表明：按照复杂失效准则计算的结构抗震可靠度较之结构各层抗震可靠度均低。这一研究为基于概率密度函数的、精细化的抗震可靠度计算提供了新的途径。     

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.     

A global single-loop deterministic approach for reliability-based design optimization of truss structures with continuous and discrete design variables

A single-loop deterministic method (SLDM) has previously been proposed for solving reliability-based design optimization (RBDO) problems. In SLDM, probabilistic constraints are converted to approximate deterministic constraints. Consequently, RBDO problems can be transformed into approximate deterministic optimization problems, and hence the computational cost of solving such problems is reduced significantly. However, SLDM is limited to continuous design variables, and the obtained solutions are often trapped into local extrema. To overcome these two disadvantages, a global single-loop deterministic approach is developed in this article, and then it is applied to solve the RBDO problems of truss structures with both continuous and discrete design variables. The proposed approach is a combination of SLDM and improved differential evolution (IDE). The IDE algorithm is an improved version of the original differential evolution (DE) algorithm with two improvements: a roulette wheel selection with stochastic acceptance and an elitist selection technique. These improvements are applied to the mutation and selection phases of DE to enhance its convergence rate and accuracy. To demonstrate the reliability, efficiency and applicability of the proposed method, three numerical examples are executed, and the obtained results are compared with those available in the literature.     

考虑轮轨非线性接触的车辆-轨道-桥梁垂向耦合系统随机振动分析

基于列车-轨道-桥梁耦合动力学理论,考虑轮轨接触非线性,采用广义概率密度演化理论建立了列车-轨道-桥梁垂向耦合系统非线性随机振动方程。采用数论选点法结合谱表示-随机函数法生成轨道随机不平顺样本,实现了用两个随机变量和少量样本较精确地反映轨道不平顺功率谱的随机特性。以高速列车-简支梁桥上CRTSⅠ型板式无砟轨道为例,从概率及可靠度角度,考虑非线性轮轨关系中跳轨现象以及轨道随机平顺影响,对比分析了线性与非线性轮轨对车辆运行安全性的影响;计算了不同轨道谱、车辆运行速度下轮重减载率概率密度演化规律及其对车辆运行安全性影响。结果表明,建议的方法可通过较少的随机变量和样本计算得到车辆-轨道-桥梁耦合系统非线性随机动力响应及其概率分布,可为车辆运行安全性评价提供更好的指导。     

任意分布随机序列的产生方法

随机序列已经被广泛地使用在信号处理中。通常都是由已知概率密度得到分布函数,通过分布函数产生随机序列。然而在难以通过概率密度求得分布函数时,产生随机序列则变得十分困难。提出了一种随机数生成的新方法——基于频数的随机重构方法,即由已知的概率密度直接产生随机序列;以K分布概率密度产生随机序列为例,并用K分布的特征函数以及特征函数的标准差进行验证,通过仿真分析证明该方法的正确性。最后通过统计对比,此方法不仅简捷,而且解决了传统方法的一些局限性。     

风力发电高塔系统风致随机动力响应分析

介绍了一种高精度且高效的随机动力系统分析方法-广义概率密度演化方法.基于广义概率密度演化方法,结合随机脉动风场物理模型和“桨叶-机舱-塔体-基础”一体化有限元模型,分别对1.25 MW风力发电高钢塔和钢筋混凝土风力发电高塔进行了风致随机动力响应分析,并将分析结果同确定性动力响应分析结果进行比较.研究表明,随机性对风力发电高塔系统结构风致动力响应分析的影响非常显著.     

随机-区间型天线结构有限元及可靠性分析

构建了对随机-区间混合型天线结构的有限元及可靠性分析模型,提出了一种新的处理不确定性因素的结构有限元分析方法,给出了结构保精度和保强度两工况的概率描述。同时考虑了结构的物理参数、几何参数的随机性和作用风载荷的区间性。首先将随机变量固定,利用区间因子法求得结构位移和应力响应的区间范围,然后在区间内任意点处利用随机因子法求结构响应的随机分布范围。构造了天线反射面位移响应和结构单元应力响应不确定变量的数字特征计算公式,进而得到结构各响应量的可靠性指标。对一8m口径天线结构进行了分析,分析结果表明文中所提方法具有合理性和可行性。