应力为复合χ^2—更新过程时结构可靠度的一致估计

本文讨论强度为随机变量Ｘ,应力为复合χ＾２－更新过程Ｙ（ｔ）的半随机过程可靠性模型的结构可靠度一致估计问题。获得在设计基准期［０,Ｔ］内结构可靠度表达式和结构可靠度的一致估计。     

指数—复合Weibull过程模型结构可靠度的最小方差无偏估计

本文讨论强度为指数分布、应力为复合Weibull过程模型结构可靠度的估计问题．获得指数－复合Weibull过程模型结构可靠度的最小方差无偏估计．     

应力S(t)为复合χ^2更新过程时结构可靠度渐近正态估计

该文讨论强度为随机变量X,应力为复合χ２ 更新过程｛Y（t）,t∈ ［０,T］｝时结构可靠度的渐近正态性问题．获得在设计基准期［０,T］内结构可靠度表达式和结构可靠度渐近正态估计．     

应力S(t)为复合x~2-更新过程时结构可靠度渐近正态估计

该文讨论强度为随机变量Ｘ　应力为复合ｘ２－更新过程｛Ｙ（ｔ）,ｔ ∈［０,Ｔ］｝时结构可靠度的 渐近正态性问题．获得在设计基准期［０,Ｔ］ 内结构可靠度表达式和结构可靠度渐近正态估计．     

多个独立同分布应力作用下的极值型─极值型(Ⅰ-Ⅲ)模式的结构可靠性估计

本文讨论了在多个独立同分布应力作用下的极值型－极值型（Ⅰ－Ⅲ）模式结构可靠性估计，给出了分布参数及结构可靠度的短估计．极大似然估计及量信下限．     

正态—平稳二项过程模型结构可靠度的最小方差无偏估计

本文讨论强度随机变量Ｘ服从正态分布,应力｛Ｙ（ｔ）,ｔ∈［０,Ｔ］｝为平稳二项随机过程模型结构可靠度的最小方差无偏估计．     

基于无失效数据的可靠度的估计

对二项分布无失效数据，在可靠度的先验密度为且时，给出了可靠度的多层Bayes估计．     

多元指数分布下的串联结构系统可靠度估计

本文探讨由k个相关元件组成的串联结构系统的可靠度计算与估计问题。当元件的随机强度服从多元指数分布,随机应力为相互独立的指数分布时,导出了可靠度表达式并提出它的一种估计量。当k=2时,本文还证明了该估计量是系统可靠度的相合渐近正态估计。     

指数分布场合系统应力强度模型可靠度的统计推断

利用最大似然估计和广义区间估计方法,研究了元件的强度和承受的应力均服从指数分布时系统应力强度模型可靠度的估计问题,导出了可靠度的最大似然估计和广义区间估计.同时也讨论了模型的拟合检验问题.利用模拟方法研究了提出的广义置信区间的覆盖率和拟合检验的功效,模拟结果表明提出的广义置信区间的覆盖率与名义置信系数是一致的,提出的拟合检验的功效是好的.最后用一个例子说明提出的方法.     

无失效数据情形失效率的估计及其应用

该文对指数分布的无失效数据,在失效率　的先验密度的核为　ａ－１时,给出了失效率　的Ｂａｙｅｓ估计和多层Ｂａｙｅｓ估计．并对某液压电动机,在寿命眼从指数分布时,给出了该液压电动机无失效数据情形可靠度的估计．     

Uncertainty Quantification of Stochastic Simulation for Black-box Computer Experiments

Stochastic simulations applied to black-box computer experiments are becoming more widely used to evaluate the reliability of systems. Yet, the reliability evaluation or computer experiments involving many replications of simulations can take significant computational resources as simulators become more realistic. To speed up, importance sampling coupled with near-optimal sampling allocation for these experiments is recently proposed to efficiently estimate the probability associated with the stochastic system output. In this study, we establish the central limit theorem for the probability estimator from such procedure and construct an asymptotically valid confidence interval to quantify estimation uncertainty. We apply the proposed approach to a numerical example and present a case study for evaluating the structural reliability of a wind turbine.     

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.     

Support vector regression based metamodeling for seismic reliability analysis of structures

The present study deals with support vector regression-based metamodeling approach for efficient seismic reliability analysis of structure. Various metamodeling approaches e.g. response surface method, Kriging interpolation, artificial neural network, etc. are usually adopted to overcome computational challenge of simulation based seismic reliability analysis. However, the approximation capability of such empirical risk minimization principal-based metamodeling approach is largely affected by number of training samples. The support vector regression based on the principle of structural risk minimization has revealed improved response approximation ability using small sample learning. The approach is explored here for improved estimate of seismic reliability of structure in the framework of Monte Carlo Simulation technique. The parameters necessary to construct the metamodel are obtained by a simple effective search algorithm by solving an optimization sub-problem to minimize the mean square error obtained by cross-validation method. The simulation technique is readily applied by random selection of metamodel to implicitly consider record to record variations of earthquake. Without additional computational burden, the approach avoids a prior distribution assumption about approximated structural response unlike commonly used dual response surface method. The effectiveness of the proposed approach compared to the usual polynomial response surface and neural network based metamodels is numerically demonstrated.     

Measurement errors in multivariate measurement scales

Our aim is to construct a general measurement framework for analyzing the effects of measurement errors in multivariate measurement scales. We define a measurement model, which forms the core of the framework. The measurement scales in turn are often produced by methods of multivariate statistical analysis. As a central element of the framework, we introduce a new, general method of estimating the reliability of measurement scales. It is more appropriate than the classical procedures, especially in the context of multivariate analyses. The framework provides methods for various topics related to the quality of measurement, such as assessing the structural validity of the measurement model, estimating the standard errors of measurement, and correcting the predictive validity of a measurement scale for attenuation. A proper estimate of reliability is a requisite in each task. We illustrate the idea of the measurement framework with an example based on real data.     

The reliability/cost trade-off for a class of ODE solvers

In the numerical solution of ODEs, it is now possible to develop efficient techniques that will deliver approximate solutions that are piecewise polynomials. The resulting methods can be designed so that the piecewise polynomial will satisfy a perturbed ODE with an associated defect (or residual) that is directly controlled in a consistent fashion. We will investigate the reliability/cost trade off that one faces when implementing and using such methods, when the methods are based on an underlying discrete Runge-Kutta formula. In particular we will identify a new class of continuous Runge-Kutta methods with a very reliable defect estimator and a validity check that reflects the credibility of the estimate. We will introduce different measures of the “reliability” of an approximate solution that are based on the accuracy of the approximate solution; the maximum magnitude of the defect of the approximate solution; and how well the method is able to estimate the maximum magnitude of the defect of the approximate solution. We will also consider how methods can be implemented to detect and cope with special difficulties such as the effect of round-off error (on a single step) or the ability of a method to estimate the magnitude of the defect when the stepsize is large (as might happen when using a high-order method at relaxed accuracy requests). Numerical results on a wide selection of problems will be summarized for methods of orders five, six and eight. It will be shown that a modest increase in the cost per step can lead to a significant improvement in the quality of the approximate solutions and the reliability of the method. For example, the numerical results demonstrate that, if one is willing to increase the cost per step by 50%, then a method can deliver approximate solutions where the reported estimated maximum defect is within 1% of its true value on 95% of the steps.     

The reliability analysis of probabilistic and interval hybrid structural system

The aim of this paper is to evaluate the reliability of probabilistic and interval hybrid structural system. The hybrid structural system includes two kinds of uncertain parameters—probabilistic parameters and interval parameters. Based on the interval reliability model and probabilistic operation, a new probabilistic and interval hybrid reliability model is proposed. Firstly, we use the interval reliability model to analyze the performance function, and then sum up reliability of all regions divided by the failure plane. Based on the presented optimal criterion enumerating the main failure modes of hybrid structural system and the relationship of failure modes, the reliability of structure system can be obtained. By means of the numerical examples, the hybrid reliability model and the traditional probabilistic reliability model are critically contrasted. The results indicate the presented reliability model is more suitable for analysis and design of these structural systems and it can ensure the security of system well, and it only needs less uncertain information.     

Parameter estimation of varying coefficients structural EV model with time series

In this paper, the parameters of a p-dimensional linear structural EV (error-in-variable) model are estimated when the coefficients vary with a real variable and the model error is time series. The adjust weighted least squares (AWLS) method is used to estimate the parameters. It is shown that the estimators are weakly consistent and asymptotically normal, and the optimal convergence rate is also obtained. Simulations study are undertaken to illustrate our AWLSEs have good performance.     

半参数回归模型独立情形的分离法估计

对半参数回归模型,用L2最佳逼近加矩估计的方法,推出其非参数部分的依L2与强相合联合收敛意义下的估计,及参数部分的强相合与相合渐近正态估计,并设计实行了一个模拟实验.     

Nonparametric iterative estimation of multivariate binary density

In this paper, an iterative estimate of the multivariate density is proposed when the variables are binary in nature. Some properties of this estimate are also discussed. Finally, applications of this estimate are discussed in the areas of pattern recognition and reliability.