一座现有拱桥面内失稳的可靠度随机有限元分析

用基于一次可靠度方法的可靠度随机有限元对一座现有的钢筋混凝土拱桥面内稳定性进行剩余可靠度计算,并对影响稳定性可靠度的主要参数进行了灵敏度分析。以随机变量和随机场表示现状荷载(汽车荷载、人群荷载、桥面二期恒载和结构自重)及结构参数(主拱圈弹性模量)。用作者提出的基于线性回归的随机场离散方法离散上述随机场,以有限元稳定分析的解作为复杂结构的隐式功能函数。上述功能以及失稳特征值对基本变量的梯度计算均已包含在作者开发的可靠度随机有限元程序RESFEP中。分析给出此桥稳定性可靠度值。灵敏度分析表明:在各随机变量中,拱肋弹性模量随机场离散变量对拱桥可靠度指标影响最大,汽车荷载随机场离散变量次之;在各随机变量的均值和标准差中,拱肋弹性模量随机场离散变量均值和标准差对拱桥可靠度影响最大,汽车荷载随机场离散变量的均值和标准差次之。     

板壳结构非线性随机有限元法研究

采用摄动随机有限元法研究了具有随机参数的板壳结构大挠度动力响应问题。基于Mindlin-Reissner板理论,采用全量Lagrangian法推导了具有板壳结构的大变形、大转动的动力响应有限元列式;通过基于等参变换的局部模型对随机场进行离散,结合摄动技术,建立了基于摄动技术的增量形式的随机有限元列式,计算结果与Monte-Carlo法相比较表明了该方法的有效性和精确性。通过该方法,为进一步进行结构可靠性分析提供了依据和方便。     

A spectral stochastic element free Galerkin method for the problems with random material parameter

This paper presents a spectral stochastic element free Galerkin method (SSEFGM) for the problems involving a random material property. The random material property and resulting system response quantity are represented by a probabilistic spectral expansion techniques (Karhunen–Loeve expansion and Polynomical Chaos series, respectively) and implemented into the element free Galerkin (EFG) analysis. Numerical solutions in 1D linear elastic problem with random elastic modulus are introduced, and compared with those of Monte Carlo simulation (MCS) so as to provide the validation of the proposed approach. The present SSEFGM approach can produce a probabilistic density distribution as well as a first‐ and second‐order statistical moments (mean and variance) of response quantity by a single calculation, which is distinguished from an iterative MCS. Moreover, the method is based on an element free analysis so that there is no need of nodal connectivities, which usually require more time and labourious task than main calculations. Thus the proposed SSEFGM approach can provide an alternative analysis tool for the problems contains a stochastic material property, and demands complex mesh structures. Copyright © 2004 John Wiley & Sons, Ltd.     

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。     

双随机分布细观分析模型与复合材料性能预报

发展了一种细观力学有限元分析方法——拟真实的参数化双随机分布模型, 该模型综合考虑了纤维增强树脂基复合材料的真实微结构特点和纤维单丝综合力学性能测试结果的离散性特征, 模拟了复合材料中纤维排列和强度分布的随机性。借助移动窗口法研究了该参数化双随机分布模型的可靠性, 确定了其代表性体积单元的尺寸。基于能量法原理推导了单向复合材料的弹性模量预测公式, 结合能量法和渐进失效分析方法, 利用该细观力学有限元方法分别预测了单向纤维增强树脂基复合材料T300/5228的弹性模量和强度性能。数值模拟结果和大部分试验结果吻合良好, 表明发展的细观力学有限元方法能够较好地预测复合材料的力学性能。      

Stochastic finite element analysis of a cable-stayed bridge system with varying material properties

Stochastic seismic finite element analysis of a cable-stayed bridge whose material properties are described by random fields is presented in this paper. The stochastic perturbation technique and Monte Carlo simulation (MCS) method are used in the analyses. A summary of MCS and perturbation based stochastic finite element dynamic analysis formulation of structural system is given. The Jindo Bridge, constructed in South Korea, is chosen as a numerical example. The Kocaeli earthquake in 1999 is considered as a ground motion. During the stochastic analysis, displacements and internal forces of the considered bridge are obtained from perturbation based stochastic finite element method (SFEM) and MCS method by changing elastic modulus and mass density as random variable. The efficiency and accuracy of the proposed SFEM algorithm are evaluated by comparison with results of MCS method. The results imply that perturbation based SFEM method gives close results to MCS method and it can be used instead of MCS method, especially, if computational cost is taken into consideration.     

混凝土材料细观单元弹模非均匀统计特性研究

假定混凝土是由骨料颗粒及砂浆基质组成的复合材料, 基于Voigt 并联模型对混凝土细观单元进行等效化, 对单元的等效弹性模量进行统计分析。以Weibull 分布为假设分布, 采用图解法结合逐步回归优选法进行参数估计, 探讨了混凝土细观单元弹性模量的分布形式, 并根据Kolmogorov非参数检验, 对假设分布进行了检验;对不同尺度下的细观单元弹性模量进行统计分析, 并对骨料空间分布随机性的影响作了初步分析;最后对不同级配下混凝土材料的特征单元尺度问题进行了研究。结果表明:1) 混凝土细观单元弹性模量的随机分布形式及参数具有尺度效应, 且并不完全服从Weibull模型;2) 混凝土细观单元弹性模量的变异性与尺度相关, 随单元尺度的减小, 变异性先随之增大后逐渐趋于稳定, 它反映了混凝土材料细观不均匀程度存在一个合理的细观尺度表述的事实, 对应于变异系数向平稳段过渡的拐点所对应的单元尺度称为特征尺度;3) 二级、三级、四级配下混凝土材料的特征单元尺度分别为10mm、15mm 和18mm;4) 骨料空间分布的随机性对细观单元弹性模量随机分布特征影响可以忽略。     

Tensor-valued random fields for meso-scale stochastic model of anisotropic elastic microstructure and probabilistic analysis of representative volume element size

The main objective of this paper is to present a generic meso-scale probability model for a large class of random anisotropic elastic microstructures in order to perform a parametric analysis of the Representative Volume Element (RVE) size. This new approach can be useful for a direct experimental identification of random anisotropic elastic microstructures when the standard method cannot easily be applied to anisotropic elastic microstructures. Such a RVE is used to construct the macroscopic properties in the context of stochastic homogenization. The probability analysis is not performed as usual for a given particular random microstructure defined in terms of its constituents. Instead, it is performed for a large class of random anisotropic elastic microstructures. For this class, the probability distribution of the random effective stiffness tensor is explicitly constructed. This allows a full probability analysis of the RVE size to be carried out and its convergence to be studied. The procedure of homogenization is based on a homogeneous Dirichlet condition on the boundary of the RVE. The probability model used for the stiffness tensor-valued random field of the random anisotropic elastic microstructure is an extension of the model recently introduced by the author for elliptic stochastic partial differential operators. The stochastic boundary value problem is numerically solved by using the stochastic finite element method. The probability analysis of the RVE size is performed by studying the probability distribution of the random operator norm of the random effective stiffness tensor with respect to the spatial correlation length of the random microstructure.     

Error bounds for the reliability index in finite element reliability analysis

This work presents an extension of the goal‐oriented error estimation techniques to the reliability analysis of a linear elastic structure. We use a first‐order reliability method in conjunction with a finite element analysis (FEA) to compute the failure probability of the structure. In such a situation the output of interest that is computed from the FEA is the reliability index β. The accuracy of this output, and thus of the reliability analysis, depends, in particular, on the accuracy of the FEA. In this paper, upper and lower bounds of the reliability index are proposed, as well as simple bounds of the failure probability. An application to linear fracture mechanics is presented. Copyright © 2011 John Wiley & Sons, Ltd.     

Effects of spatially variable weathered rock properties on tunnel behavior

Design parameters commonly used in numerical modeling for tunnel stability analyses tend to be representative (or average) values of global-scale properties. However, the spatial variability of design parameters, such as geotechnical and geological properties, greatly affects the behavior of tunnels during and after construction as well as their long-term responses. Thus, this study presents a simple but robust procedure for stochastic numerical analyses using the finite difference method (FDM) and explores the effects of spatially variable weathered rock properties on various tunnel behaviors, such as deformation, elastic-plastic interface, ground reaction curve, and failure mechanism. It was found that the inherent spatial variability of stiffness and strength parameters affects the deformation behavior of tunnels and even changes its failure mechanism: Elastic modulus for the Mohr-Coulomb model and geological strength index (GSI) for the Hoek-Brown model play a key role in deformation characteristics. Considering the wide range of spatial variability in in-situ deposits, the accurate estimation of elastic modulus and GSI is very important. The spatial variability of the ground can affect the ground reaction behavior and can bring on an unfavorable ground reaction curve (GRC). It can cause an increase in the tunnel support pressure, and can induce a larger displacement than the homogeneous case. The shear failure mechanism of the tunnel can be significantly affected by a large relative correlation length. It is suggested that we should estimate and consider the variability of rock properties accurately as part of a routine tunnel design framework.     

基于统计模型的结构损伤识别

提出了一种基于递推随机有限元方法(RSFEM)的随机结构损伤识别方法。在定义了随机损伤指数概念的基础上,考虑模型误差的不确定性和测量噪声的影响,建立了关于随机损伤指数的控制方程。然后,利用RSFEM得到了结构随机损伤指数的统计特性。数值算例的结果显示,新的方法能在考虑模型误差和测量噪声的情况下对结构损伤进行有效识别,且在结构随机参数有较大涨落情况下,该方法仍能有效识别出结构损伤,识别结果与蒙特卡洛模拟解非常吻合。     

大面积混凝土梁板结构温度应力分析的徐变应力折减系数法

本文对大面积混凝土梁板结构温度应力分析方法进行探讨,从混凝土徐变计算的龄期调整有效模量法(T-B法)出发,通过对比典型框架在混凝土收缩及温度变化作用下的弹性和徐变解析解,提出了框架结构约束系数和徐变应力折减系数的概念及计算方法,结合框架结构空间弹性有限元分析,得到了大面积混凝土梁板结构温度应力分析的徐变应力折减系数法。与现场长期测试结果的比较表明,该法计算简便,计算结果与测试结果有较好的一致性,可以满足工程设计精度要求。     

A new stochastic residual error based homotopy approach for stability analysis of structures with large fluctuation of random parameters

The large fluctuation of uncertain parameters introduces a great challenge in the stability analysis of structures. To address this problem, a novel stochastic residual error based homotopy method is proposed in this article. This new method used the concept of homotopy to reconstruct a new governing equation for stochastic elastic buckling analysis, and the closed-form solutions of the isolated buckling eigenvalues and eigenvectors are obtained by the stochastic homotopy analysis method. On this basis, a pth order origin moment of the stochastic residual error with respect to the elastic buckling equation is defined. Then, the optimal form of the homotopy series can be determined automatically by minimizing the pth order origin moment, which overcomes the disadvantage of highly relying on sample values of the existing homotopy stochastic finite element method. Moreover, the proposed method is developed to deal with the stochastic closely spaced buckling eigenvalue problem. Three mathematical examples and three buckling eigenvalue examples, including a variable cross-section column, a 7-story frame, and a Kiewitt single-layer latticed spherical shell, are performed to illustrate the accuracy and effectiveness of the proposed method by comparing with the existing methods when dealing with large fluctuation of random parameters.     

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.     

Processing radio frequency ultrasound images: a robust method for local spectral features estimation by a spatially constrained parametric approach

Spectral estimation is a major component in studies aiming at characterizing biological tissues through the analysis of backscattered radio frequency (RF) ultrasonic signals and images. However, conventional spectral estimation techniques yield a well-known trade-off between spatial resolution and variance. The backscattered signals are stochastic by nature, so short-term local analysis results in a high variance of the estimates, which cannot efficiently be reduced through conventional spatial averaging. We address this issue by describing a spectral estimation technique that reduces the variance of the estimates (by smoothing the local estimates in spectrally homogeneous regions) while preserving spectral discontinuities (i.e., the smoothing is not performed across regions with different spectral contents). The proposed approach is set in a Bayesian framework and is based on local autoregressive (AR) estimation, constrained by smoothness priors. These smoothness priors are introduced through a Markov random field in which the associated potential functions are nonquadratic, allowing thereby to preserve discontinuity. The method is validated on simulated RF images and tested on echocardiographic images acquired in vivo. The results are compared to the estimates provided by the conventional Burg technique. These results clearly demonstrate the ability of the proposed approach to improve spectral estimation in terms of variance reduction and discontinuity detection.     

随机结构弹性屈曲的递推求解方法

采用随机收敛的非正交的多项式展式表示未知的随机屈曲特征值和屈曲模态,利用摄动技巧,建立了随机结构弹性屈曲的递推求解方法。算例表明,和基于泰勒展开的摄动随机有限元方法相比,方法的结果能在较宽的随机涨落范围内更好地逼近蒙特卡洛模拟结果,即使只采用前四阶非正交多项式展式,逼近的结果仍然较好。     

Analysis of correlated multivariate degradation data in accelerated reliability growth

Modern engineering systems have become increasingly complex and at the same time are expected to be developed faster. To shorten the product development time, organizations commonly conduct accelerated testing on a small number of units to help identify failure modes and assess reliability. Many times design changes are made to mitigate or reduce the likelihood of such failure modes. Since failure-time data are often scarce in reliability growth programs, existing statistical approaches used for predicting the reliability of a system about to enter the field are faced with significant challenges. In this work, a statistical model is proposed to utilize degradation data for system reliability prediction in an accelerated reliability growth program. The model allows the components in the system to have multiple failure modes, each associated with a monotone stochastic degradation process. To take into account unit-to-unit variation, the random effects of degradation parameters are explicitly modeled. Moreover, a mean-degradation-stress relationship is introduced to quantify the effects of different accelerating variables on the degradation processes, and a copula function is utilized to model the dependency among different degradation processes. Both a maximum likelihood (ML) procedure and a Bayesian alternative are developed for parameter estimation in a two-stage process. A numerical study illustrates the use of the proposed model and identifies the cases where the Bayesian method is preferred and where it is better to use the ML alternative.     

Reliability predictions of laminated composite plates with random system parameters

Reliability predictions of laminated composite plates with random system parameters subjected to transverse loads are performed using different methods. System parameters such as material properties, layer thicknesses, and lamina strengths of a laminated composite plate are treated as base-line random variables and an appropriate failure criterion is used to construct the limit state equation of the plate in the reliability analysis. Based on the statistics of the base-line random variables obtained from experiments, different methods, namely, Monte Carlo method, β method, and first-order second moment method, are used to calculate the reliability of the laminated composite plates. In the first-order second moment method, the stochastic finite element method is used to derive for the statistics of the first-ply failure load of the laminated composite plates from those of the base-line random variables. The reliability of the laminated plate is then computed using the theoretically determined statistics together with an assumed probability distribution function of the first-ply failure load. The feasibility and accuracy of the different methods are studied by means of the experimental data of centrally loaded laminated composite plates with different lay-ups. The suitability of several commonly used failure criteria for reliability analysis of laminated composite plates is also investigated by means of several examples.     

Fault-tolerant embedded system design and optimization considering reliability estimation uncertainty

In this paper, we model embedded system design and optimization, considering component redundancy and uncertainty in the component reliability estimates. The systems being studied consist of software embedded in associated hardware components. Very often, component reliability values are not known exactly. Therefore, for reliability analysis studies and system optimization, it is meaningful to consider component reliability estimates as random variables with associated estimation uncertainty. In this new research, the system design process is formulated as a multiple-objective optimization problem to maximize an estimate of system reliability, and also, to minimize the variance of the reliability estimate. The two objectives are combined by penalizing the variance for prospective solutions. The two most common fault-tolerant embedded system architectures, N-Version Programming and Recovery Block, are considered as strategies to improve system reliability by providing system redundancy. Four distinct models are presented to demonstrate the proposed optimization techniques with or without redundancy. For many design problems, multiple functionally equivalent software versions have failure correlation even if they have been independently developed. The failure correlation may result from faults in the software specification, faults from a voting algorithm, and/or related faults from any two software versions. Our approach considers this correlation in formulating practical optimization models. Genetic algorithms with a dynamic penalty function are applied in solving this optimization problem, and reasonable and interesting results are obtained and discussed.     

随机结构模态频率与物理参数的相关分析

本文应用Monte-Carlo随机有限元方法进行了随机结构系统的模态频率与随机物理参数的相关分析。数值试验表明，在一定的变异范围内（8=1％-30％），随着物理参数变异性增大，模态密集程度降低，模态频率与随机物理参数互相关系数发生变化。