基于两阶段局部抽样策略的结构可靠性分析

工程实践中存在着各种不确定性因素，影响着工程结构的安全运行。结构可靠性分析以失效概率的形式考虑了不确定性的影响，可为结构的安全设计提供指导。然而，失效概率的评估往往涉及昂贵功能函数的调用，导致难以负担的计算成本。为解决该问题，基于Kriging模型的可靠性分析法在近年来受到了广泛的关注。该方法以训练良好的Kriging模型近似真实功能函数，从而在失效概率的计算中达到减少功能函数评价次数的目的。本文在主动学习Kriging模型的框架下，提出了基于两阶段局部抽样策略的结构可靠性分析法，以提高失效概率的估计精度和计算效率。在该方法中，Kriging模型的训练样本以两阶段局部抽样的方式从候选样本池中被逐渐添加。第一阶段以输入变量的均值点为抽样中心，利用概率密度函数确定抽样区域。当所估计失效概率满足基于置信区间的阶段划分阈值时，则开始第二阶段的局部抽样。第二阶段则以最可能失效点为抽样中心，以目标可靠度和功能函数的非线性度确定抽样区域。应用案例表明：所提方法能平衡有效抽样区域的全局探索和局部搜索，实现高精度失效概率估计的同时提高计算效率。     

基于Copula函数的屏蔽数据并-串联系统可靠性分析

讨论了失效相依屏蔽数据系统的可靠性分析问题.通过引入Copula函数描述部件寿命变量之间的相依关系,建立屏蔽数据并-串联系统可靠性模型,推导出并-串联系统的一些概率结果.在此基础上,基于逐步Ⅰ型混合截尾的系统失效数据,获得了模型参数及可靠性指标的极大似然估计和bootstrap区间估计.最后,运用蒙特卡罗模拟验证了方法的可行性和有效性.     

多模式广义失效率概率计算的数值模拟法及其工程应用

提出了安全与失效状态含有模糊信息时,广义失效概率计算的数值模拟,及相应的方差估算,并提出了对应的数值积分方法。当状态变量服从于正态分布,且其对模糊安全域的隶属函数为正态型时,单个模式的广义失效概率具有精确解。首先利用这种特殊情况检验了所提数值模拟的精度,结果表明对于数值模拟法,随抽样次数的增加,估计值逐渐收敛于真实值。然后利用扩展原理和概率定理,提出两个以上失效模式数广义失效概率的数值模拟计算方法     

机械可靠性分析的高精度响应面法

通过对已有可靠性分析中的响应面法的研究,提出了一种高精度的响应面法,该方法通过迭代线性插值的策略,来保证确定响应面的抽样点比经典的响应面法更接近真实的极限状态方程,并且该方法通过序列线性插值的方法来控制抽样点与插值中心点的距离,保证随着插值中心点收敛于真实设计点,抽样点提供更多的关于设计点附近真实极限状态方程的信息,进而保证了收敛的响应面能够在设计点附近更好地拟合真实的极限状态方程,并得到高精度的失效概率计算结果．算例充分说明了所提方法的合理性与适用性．     

高维投资组合风险的估计

在大数据时代,如何估计高维投资组合的风险是金融机构面临的一大难题.针对这一难题,文章主要做了两方面研究:首先,将非线性收缩法和QuEST函数应用到BEKK模型中,提出BEKK-NS模型,以估计和预测在资产组合中扮演着重要角色的资产协方差阵.该模型同时适用于估计正态分布和厚尾分布数据的协方差阵,并且能够很好地解决维数诅咒问题,提高协方差阵的估计效率.其次,构造了基于循环分块bootstrap方法的极限误差U(α)来评价高维投资组合的风险.通过模拟和实证研究发现:BEKK-NS模型明显优于BEKK,将其应用在投资组合时,降低了组合风险,使得投资者获得了更高的收益;并且极限误差U(α)非常接近于真实的误差,由其构造的组合风险的置信区间较为精确.     

基于Bregman距离函数的可靠性分析

针对概率结构可靠性问题,引入Bregman距离函数,建立了基于同伦算法(HM)的可靠性分析模型.利用极限状态方程,将可靠性指标求解转化为一个非线性约束优化问题.结合同伦思想的基本理论和Bregman距离函数,构造同伦方程组,采用路径跟踪算法对该方程组进行求解.通过相应的数值算例探讨了不同函数形式以及不同程度非线性问题的可靠性计算,并与其他方法计算结果进行了对比,分析结果表明该模型能够有效求解概率结构可靠性问题.     

基于失效域重构和重要抽样法的结构动力学系统首穿失效概率

对于线性动力学系统，重构系统失效域，利用基本失效域概率构造重要抽样密度函数，提出了基于重要抽样技术的首穿失效概率估计方法；对于非线性动力学系统，构建等效线性系统，线性化原理为线性与非线性系统对安全域边界具有相同的平均上穿率.最后给出Gauss(高斯)白噪声激励的线性与非线性系统的数值算例，并与Monte-Carlo(蒙特 卡洛)方法及区域分解方法比较，结果显示该文方法是正确有效的.     

利用局部加权拟合方法检验线性回归关系

利用局部加权技术拟合变参数回归模型,提出了一个检验线性回归关系的方法.基于残差平方和,构造适当的检验统计量,给出了计算检验p-值的精确方法及三阶矩x2逼近方法.随机模拟与实例分析表明计算p-值的逼近方法具有较高的精度,所提出的检验统计量在检测回归函数非线性性方面有满意的功效和可靠性.     

经验似然重抽样下回归模型的Bootstrap逼近

本文提出用经验似然重抽样来bootstrap逼近线性回归模型中的学生化最小二乘估计．我们证明了该方法具有一般s－2项Edgeworth展开，它是二阶相合的而且比经典的方法损失更小．     

卫生系统的评价模型(英文)

构造了一个模糊数学模型和一个灰色系统预测模型来评估卫生系统的好坏并预测其发展趋势.在第一个模型中,我们通过作用于隶属向量上的灵敏度创造出一种计算权重的特殊方法.从本质上讲,作用于隶属向量上的灵敏可以通过作用于隶属函数上的灵敏度求得.基于上述基本思想,我们创造出两种计算作用于隶属向量上的灵敏度的方法.     

A new method for reliability analysis of structures with mixed random and convex variables

This paper proposes a method combining projection-outline-based active learning strategy with Kriging metamodel for reliability analysis of structures with mixed random and convex variables. In this method, it is determined that the approximation accuracy of projection outlines on the limit-state surface is crucial for estimation of failure probability instead of the whole limit-state surface. To efficiently improve the approximation accuracy of projection outlines, a new projection-outline-based active learning strategy is developed to sequentially obtain update points located around the projection outlines. Taking into account the influence of metamodel uncertainty on the estimation of failure probability, a quantification function of metamodel uncertainty is developed and introduced in the stopping condition of Kriging metamodel update. Finally, Monte Carlo simulation is employed to calculate the failure probability based on the refined Kriging metamodel. Four examples including the Burro Creek Bridge and a piezoelectric energy harvester are tested to validate the performance of the proposed method. Results indicate that the proposed method is accurate and efficient for reliability analysis of structures with mixed random and convex variables.     

M5 model tree and Monte Carlo simulation for efficient structural reliability analysis

Practically, the performance of many engineering problems can be defined using a complex implicit limit state function. Approximation of the accurate failure probability is very time-consuming and inefficient based on Monte Carlo simulation (MCS) for complex performance functions. M5 model tree (M5Tree) model is robust approach for simulation and prediction phenomena, which provides ability to dealing with complex implicit problems by dividing them into smaller problems. By improving the efficiency of reliability method using accurate approximated failure probability, an efficient reliability method using the MCS and M5Tree is proposed to calibrate the performance function and estimate the failure probability, respectively. The superiorities including simplicity and accuracy of M5Tree meta-model are investigated to evaluate the actual performance function through five nonlinear complex mathematical and structural reliability problems. The proposed reliability method-based MCS and M5Tree improved the computational efforts for evaluating the performance function in reliability analysis. The M5Tree significantly increased the efficiency of reliability analysis with accurate failure probability.     

Adaptive surrogate-based design optimization with expected improvement used as infill criterion

A prominent advantage of using surrogate models in structural design optimization is that computational effort can be greatly reduced without significantly compromising model accuracy. The essential goal is to perform the design optimization with fewer evaluations of the typically finite element analysis and ensuring accuracy of the optimization results. An adaptive surrogate based design optimization framework is proposed, in which Latin hypercube sampling and Kriging are used to build surrogate models. Accuracy of the models is improved adaptively using an infill criterion called expected improvement (EI). It is the anticipated improvement that an interpolation point will lead to the current surrogate models. The point that will lead to the maximum EI is searched and used as infill points at each iteration. For constrained optimization problems, the surrogate of constraint is also utilized to form a constrained EI as the corresponding infill criterion. Computational trials on mathematical test functions and on a three-dimensional aircraft wing model are carried out to test the feasibility of this method. Compared with the traditional surrogate base design optimization and direct optimization methods, this method can find the optimum design with fewer evaluations of the original system model and maintain good accuracy.     

Adaptive Kriging coupled with importance sampling strategies for time-variant hybrid reliability analysis

For the time-variant hybrid reliability problem under random and interval uncertainties, the upper bound of time-variant failure probability, as a conservative index to quantify the safety level of the structure, is highly concerned. To efficiently estimate it, the adaptive Kriging respectively combined with design point based importance sampling and meta-model based one are proposed. The first algorithm firstly searches the design point of the hybrid problem, on which the candidate random samples are generated by shifting the sampling center from mean value to design point. Then, the Kriging model is iteratively trained and the hybrid problem is solved by the well-trained Kriging model. The second algorithm firstly utilizes the Kriging-based importance sampling to approximate the quasi-optimal importance sampling samples and estimate the augmented upper bound of time-variant failure probability. After that, the Kriging model is further updated based on these importance samples to estimate the correction factor, on which the hybrid failure probability is calculated by the product of augmented upper bound of time-variant failure probability and correction factor. Meanwhile, an improved learning function is presented to efficiently train an accurate Kriging model. The proposed methods integrate the merits of adaptive Kriging and importance sampling, which can conduct the hybrid reliability analysis by as little as possible computational cost. The presented examples show the feasibility of the proposed methods.     

The locally extrapolated exponential splitting scheme for multi-dimensional nonlinear space-fractional Schrödinger equations

An efficient local extrapolation of the exponential operator splitting scheme is introduced to solve the multi-dimensional space-fractional nonlinear Schrödinger equations. Stability of the scheme is examined by investigating its amplification factor and by plotting the boundaries of the stability regions. Empirical convergence analysis and calculation of the local truncation error exhibit the second-order accuracy of the proposed scheme. The performance and reliability of the proposed scheme are tested by implementing it on two- and three-dimensional space-fractional nonlinear Schrödinger equations including the space-fractional Gross-Pitaevskii equation, which is used to model optical solitons in graded-index fibers.     

A novel single-loop procedure for time-variant reliability analysis based on Kriging model

This paper proposes a novel single-loop procedure for time-variant reliability analysis based on a Kriging model. A new strategy is presented to decouple the double-loop Kriging model for time-variant reliability analysis, in which the extreme value response in double-loop procedure is replaced by the best value in the current sampled points to avoid the inner optimization loop. Consequently, the extreme value response surface for time-variant reliability analysis can be directly established through a single-loop Kriging surrogate model. To further improve the accuracy of the proposed Kriging model, two methods are provided to adaptively choose a new sample point for updating the model. One method is to apply two commonly used learning functions to select the new sample point that resides as close to the extreme value response surface as possible, and the other is to apply a new learning function to select the new point. Synchronously, the corresponding different stopping criteria are also provided. It is worth nothing that the proposed single-loop Kriging model for time-variant reliability analysis is for a single time-variant performance function. To verify the proposed method, it is applied to four examples, two of which have with random process and others have not. Other popular methods for time-variant reliability analysis including the existing single-loop Kriging model are also used for the comparative analysis and their results testify the effectiveness of the proposed method.     

Adaptive reliability analysis based on a support vector machine and its application to rock engineering

The response surface method (RSM), a simple and effective approximation technique, is widely used for reliability analysis in civil engineering. However, the traditional RSM needs a considerable number of samples and is computationally intensive and time-consuming for practical engineering problems with many variables. To overcome these problems, this study proposes a new approach that samples experimental points based on the difference between the last two trial design points. This new method constructs the response surface using a support vector machine (SVM); the SVM can build complex, nonlinear relations between random variables and approximate the performance function using fewer experimental points. This approach can reduce the number of experimental points and improve the efficiency and accuracy of reliability analysis. The advantages of the proposed method were verified using four examples involving random variables with different distributions and correlation structures. The results show that this approach can obtain the design point and reliability index with fewer experimental points and better accuracy. The proposed method was also employed to assess the reliability of a numerically modeled tunnel. The results indicate that this new method is applicable to practical, complex engineering problems such as rock engineering problems.     

A computational framework of kinematic accuracy reliability analysis for industrial robots

A new computational method to evaluate comprehensively the positional accuracy reliability for single coordinate, single point, multipoint and trajectory accuracy of industrial robots is proposed using the sparse grid numerical integration method and the saddlepoint approximation method. A kinematic error model of end-effector is constructed in three coordinate directions using the sparse grid numerical integration method considering uncertain parameters. The first-four order moments and the covariance matrix for three coordinates of the end-effector are calculated by extended Gauss–Hermite integration nodes and corresponding weights. The eigen-decomposition is conducted to transform the interdependent coordinates into independent standard normal variables. An equivalent extreme value distribution of response is applied to assess the reliability of kinematic accuracy. The probability density function and probability of failure for extreme value distribution are then derived through the saddlepoint approximation method. Four examples are given to demonstrate the effectiveness of the proposed method.     

New maximum entropy-based algorithm for structural design optimization

A new algorithm based on nonlinear transformation is proposed to improve the classical maximum entropy method and solve practical problems of reliability analysis. There are three steps in the new algorithm. Firstly, the performance function of reliability analysis is normalized, dividing by its value when each input is the mean value of the corresponding random variable. Then the nonlinear transformation of such normalized performance function is completed by using a monotonic nonlinear function with an adjustable parameter. Finally, the predictions of probability density function and/or the failure probability in reliability analysis are achieved by looking the result of transformation as a new form of performance function in the classical procedure of maximum entropy method in which the statistic moments are given through the univariate dimension reduction method. In the proposed method, the uncontrollable error of integration on the infinite interval is removed by transforming it into a bounded one. Three typical nonlinear transformation functions are studied and compared in the numerical examples. Comparing with results from Monte Carlo simulation, it is found that a proper choice of the adjustable parameter can lead to a better result of the prediction of failure probability. It is confirmed in the examples that result from the proposed method with the arctangent transformation function is better than the other transformation functions. The error of prediction of failure probability is controllable if the adjustable parameter is chosen in a given interval, but the suggested value of the adjustable parameter can only be given empirically.     

Augmented line sampling for approximation of failure probability function in reliability-based analysis

An efficient approach, called augmented line sampling, is proposed to locally evaluate the failure probability function (FPF) in structural reliability-based design by using only one reliability analysis run of line sampling. The novelty of this approach is that it re-uses the information of a single line sampling analysis to construct the FPF estimation, repeated evaluations of the failure probabilities can be avoided. It is shown that, when design parameters are the distribution parameters of basic random variables, the desired information about FPF can be extracted through a single implementation of line sampling. Line sampling is a highly efficient and widely used reliability analysis method. The proposed method extends the traditional line sampling for the failure probability estimation to the evaluation of the FPF which is a challenge task. The required computational effort is neither relatively sensitive to the number of uncertain parameters, nor grows with the number of design parameters. Numerical examples are given to show the advantages of the approach.