A Bayesian Updating Method for Non-Probabilistic Reliability Assessment of Structures with Performance Test Data

For structures that only the predicted bounds of uncertainties are available,this study proposes a Bayesianmethod to logically evaluate the nonprobabilistic reliability of structures based on multi-ellipsoid convex model and performance test data.According to the given interval ranges of uncertainties,we determine the initial characteristic parameters of a multi-ellipsoid convex set.Moreover,to update the plausibility of characteristic parameters,a Bayesian network for the information fusion of prior uncertainty knowledge and subsequent performance test data is constructed.Then,an updated multi-ellipsoid set with the maximum likelihood of the performance test data can be achieved.The credible non-probabilistic reliability index is calculated based on the Kriging-based surrogate model of the performance function.Several numerical examples are presented to validate the proposed Bayesian updating method.

MODIFIED SCHEME BASED ON SEMI-ANALYTIC APPROACH FOR COMPUTING NON-PROBABILISTIC RELIABILITY INDEX

A new computation scheme proposed to tackle commensurate problems is devel- oped by modifying the semi-analytic approach for minimizing computational complexity. Using the proposed scheme, the limit state equations, usually referred to as the failure surface, are obtained from transformation of an interval variable to a normalized one. In order to minimize the computational cost, two algorithms for optimizing the calculation steps have been proposed. The monotonicity of the objective function can be determined from narrowing the scope of interval variables in normalized infinite space by incorporating the algorithms into the computational scheme. Two examples are used to illustrate the operation and computational efficiency of the approach. The results of these examples show that the proposed algorithms can greatly reduce the computation complexity without sacrificing the computational accuracy. The advantage of the proposed scheme can be even more efficient for analyzing sophistic structures.

Combination of structural reliability and interval analysis

In engineering applications, probabilistic reliability theory appears to be presently the most important method, however, in many cases precise probabilistic reliability theory cannot be considered as adequate and credible model of the real state of actual affairs. In this paper, we developed a hybrid of probabilistic and non-probabilistic reliability theory, which describes the structural uncertain parameters as interval variables when statistical data are found insufficient. By using the interval analysis, a new method for calculating the interval of the structural reliability as well as the reliability index is introduced in this paper, and the traditional probabilistic theory is incorporated with the interval analysis. Moreover, the new method preserves the useful part of the traditional probabilistic reliability theory, but removes the restriction of its strict requirement on data acquisition. Example is presented to demonstrate the feasibility and validity of the proposed theory.

Theoretical analysis of non-probabilistic reliability based on interval model

The aim of this paper is to propose a theoretical approach for performing the nonprobabilistic reliability analysis of structure.Due to a great deal of uncertainties and limited measured data in engineering practice,the structural uncertain parameters were described as interval variables.The theoretical analysis model was developed by starting from the 2-D plane and 3-D space.In order to avoid the loss of probable failure points,the 2-D plane and 3-D space were respectively divided into two parts and three parts for further analysis.The study pointed out that the probable failure points only existed among extreme points and root points of the limit state function.Furthermore,the low-dimensional analytical scheme was extended to the high-dimensional case.Using the proposed approach,it is easy to find the most probable failure point and to acquire the reliability index through simple comparison directly.A number of equations used for calculating the extreme points and root points were also evaluated.This result was useful to avoid the loss of probable failure points and meaningful for optimizing searches in the research field.Finally,two kinds of examples were presented and compared with the existing computation.The good agreements show that the proposed theoretical analysis approach in the paper is correct.The efforts were conducted to improve the optimization method,to indicate the search direction and path,and to avoid only searching the local optimal solution which would result in missed probable failure points.

Sealing reliability modeling of aviation seal based on interval uncertainty method and multidimensional response surface

Many variables affect the sealing performance, and their distribution characteristics are difficult to obtain with probabilistic methods owing to the high cost involved. Numerous problems in engineering are similar due to the appearance of small-sample parameters. In this study, the sealing reliability of an aviation seal was defined as the research object, and an interval uncertainty method and multidimensional response surface were proposed to calculate the sealing reliability.Based on this, we first analyzed the failure mechanism of the aviation seal and established a leakage rate model. Then, based on the non-probabilistic interval model, an interval uncertainty method was proposed to construct the analytical model. With reference to the limit state equation from the structural reliability theory, the multidimensional response surface was used for fast calculation.Then, we chose the single-cylinder gas steering gear used in aircraft as the case study, its sealing reliability in working and non-working statuses were calculated, and the results were verified with the actual maintenance records. By analyzing the sensitivity of some variables, we can improve the sealing reliability of the aviation seal by improving the surface roughness only if the cost allows.Finally, we consider that the method proposed in this study realizes the application of smallsample uncertainty analysis in reliability analysis, and could provide a feasible way to solve the similar problems in engineering with multidimensional and small-sample parameters.

Interval design point method for calculating the reliability of structural systems

The evaluation of reliability for structural system is important in engineering practices.In this paper,by combining the design point method,JC method,interval analysis theory,and increment load method,we propose a new interval design point method for the reliability of structural systems in which the distribution parameters of random variables are described as interval variables.The proposed method may provide exact probabilistic interval reliability of structures whose random variables can have either a normal or abnormal distribution form.At last,we show the feasibility of the proposed approach through a typical example.

A Bootstrapping Approach for Software Reliability Measurement Based on a Discretized NHPP Model

Discrete software reliability measurement has a proper characteristic for describing a software reliability growth process which depends on a unit of the software fault-detection period, such as the number of test runs, the number of executed test cases. This paper discusses discrete software reliability measurement based on a discretized nonhomogeneous Poisson process (NHPP) model. Especially, we use a bootstrapping method in our discrete software reliability measurement for discussing the statistical inference on parameters and software reliability assessment measures of our model. Finally we show numerical examples of interval estimations based on our bootstrapping method for the several software reliability assessment measures by using actual data.

Bias and Mean Square Error of Reliability Estimators under the One and Two Random Effects Models: The Effect of Non-Normality

The coefficient of reliability is often estimated from a sample that includes few subjects. It is therefore expected that the precision of this estimate would be low. Measures of precision such as bias and variance depend heavily on the assumption of normality, which may not be tenable in practice. Expressions for the bias and variance of the reliability coefficient in the one and two way random effects models using the multivariate Taylor’s expansion have been obtained under the assumption of normality of the score (Atenafu et al. [1]). In the present paper we derive analytic expressions for the bias and variance, hence the mean square error when the measured responses are not normal under the one-way data layout. Similar expressions are derived in the case of the two-way data layout. We assess the effect of departure from normality on the sample size requirements and on the power of Wald’s test on specified hypotheses. We analyze two data sets, and draw comparisons with results obtained via the Bootstrap methods. It was found that the estimated bias and variance based on the bootstrap method are quite close to those obtained by the first order approximation using the Taylor’s expansion. This is an indication that for the given data sets the approximations are quite adequate.

小样本条件下基于Bootstrap方法的边坡非概率可靠度分析

针对小样本条件下边坡可靠度分析所面临的岩土参数统计特征不确定性问题,提出一种基于Bootstrap抽样的边坡非概率可靠度分析方法。依据有限的岩土参数样本数据,无需假设具体的概率分布形式,利用Bootstrap方法生成大样本,并确定用于非概率可靠度分析的参数取值区间,再基于区间数学理论和极限平衡法计算边坡功能函数的值域,最后根据非概率可靠性理论分析边坡的稳定性。研究表明:该方法在小样本条件下具有可行性;采用Bootstrap方法对岩土参数进行抽样时,建议取生成样本的第2.5—97.5或第5—95百分位数作为参数的取值区间;不同Bootstrap方法得到的边坡非概率可靠度之间的差异受边坡容许安全系数的影响;当样本容量达到一定数值时,边坡的非概率可靠度趋于稳定。

Non-Probabilistic Reliability Research on Multi-Variable Hydraulic Turbine Blade Model

Based on the interval mathematics and possibility theory, the variables existing in hydraulic turbine blade are described. Considering the multi-failure mode in turbine blade, multi-variable model is established to meet the actual situation. Thus, non-probabilistic reliability index is presented by comparing with the output range and the given range.

基于区间变量的响应面可靠性分析方法

通过对响应面方法、区间变量和地下结构不确定性特点的研究发现:(1)响应面方法在具有不确定性结构的静力响应分析中具有相当高的准确性;(2)地下岩体结构物理、力学参数及力学响应的不确定性特征非常适合通过区间数进行表达,但结构功能函数解析表达式往往很难直接求出。因此,对基于数理统计特征的响应面方法进行了改进,提出了基于区间变量的响应面函数形式选取,试验设计方法及函数解析式的拟合程序。在此基础上,根据函数中自变量的特点,研究了区间变量函数值域区间的求解方法。其中结合响应面函数形式,重点研究了同一自变量多次出现的函数形式的值域求解方法和具体计算程序。从而形成了完整的基于区间变量的响应面非概率可靠性分析方法,利用该方法分析了某一工程实例,展示了其有效性和可行性。

基于区间不确定分析方法的边坡稳定性分析

影响边坡稳定性的岩土参数大多具有随机性、模糊性和可变性等不确定性的特点,因此,需要引入不确定性分析模型进行分析。本文在综合分析岩土参数区间性的基础上,将区间分析模型应用于边坡的稳定性分析。通过引用区间数学的思想,运用区间极限平衡法推导出边坡最小安全系数区间,并在此基础上对边坡进行非概率可靠度分析。

考虑负荷转移限制的配电系统区间可靠性评估

提出一种考虑线路容量和电压约束的配电系统区间可靠性评估方法。应用区间理论计算了由于元件和负荷参数的不确定性对配电系统可靠性的影响。考虑到网络运行条件对负荷转移的限制,通过区间潮流计算确定故障恢复过程中的网络状态,如果违反网络约束,则以最小化停电损失费用和网损费用为目标函数切除网络中的负荷。结合馈线分区方法,简化了系统结构,提高了计算速度。算例中,分析了参数不确定性条件下负荷转移限制对配电系统可靠性的影响,证明了该方法的有效性。

基于区间层次分析法的数控机床可靠性预计

针对将传统相似比较法用于数控机床可靠性预计时,评价对象和相似产品之间可靠性水平的差异程度很难准确评定的问题,提出了一种引入区间层次分析的数控机床可靠性预计方法。首先分析了整机和子系统的可靠性关系,建立了整机的可靠性预计模型。在子系统可靠性预计过程中,充分利用了相似产品的可靠性数据。全面分析了评价对象和相似产品之间的差异,建立了可靠性修正因子评估模型。使用区间层次分析法求出了可靠性修正因子,实现了确定性信息和模糊信息的互补。结合实例进行了数控机床的可靠性预计,验证了该方法的可行性。

单维测验合成信度三种区间估计的比较

已有许多研究建议使用合成信度来估计测验信度,并报告其置信区间。有三种方法或途径可以计算单维测验合成信度的置信区间,包括Bootstrap法、Delta法和直接用统计软件(如LISREL)输出的标准误进行计算。本文通过模拟研究进行比较,发现Delta法与Bootstrap法得到的置信区间相当接近,但用LISREL输出的标准误计算的与Bootstrap法得到的结果相差很大。推荐用Delta法估计合成信度的置信区间(使用Mplus容易实现),但不能直接用LISREL输出的标准误来计算。举例说明了如何计算单维测验的合成信度以及用Delta法计算其置信区间。

基于四分位法的含储能光伏电站可靠性置信区间计算方法

为提高光伏电站接入系统能力,采用光储联合运行方式,研究加入储能前后光伏电站发电特性变化。为评估光储系统接入对电网可靠性的贡献,提出一种可靠性置信区间计算方法。首先对青海省格尔木某光伏电站历史数据做模糊C均值聚类,将其分为晴天、少云、多云、阴雨4种天气类型,利用四分位法对历史数据做统计分析,排除异常值。在此基础上提出一种光储系统功率带提取方法,搭建IEEE-RTS79可靠性测试系统,利用序贯蒙特卡罗法计算光储发电系统功率带对应的可靠性指标,从而得到含储能光伏电站可靠性置信区间。该可靠性区间计算结果为评价光储系统接入电网的可靠性贡献提供理论依据,对发电调度具有一定指导意义。

基于区间组合法的边坡稳定非概率模糊可靠性分析方法

根据边坡稳定性影响因素的随机性、模糊性以及岩土材料力学参数取值的区间性特征,采用非对称三角模糊数表示岩土力学参数取值可能性分布,并对试验数据进行区间模糊优化分析。将所得非对称三角模糊数转化成区间数,运用边坡稳定性分析的简化Bishop法与区间组合法建立边坡安全系数区间值及可能滑动面求解方法,避免了区间函数运算的区间扩张问题。将安全系数区间计算函数与给定安全系数值之差作为功能函数,利用非概率模糊可靠性分析方法计算不同截集水平下的模糊可靠性指标,进而得到其可能性分布曲线,并由可能性理论求得边坡失效可能度,从而建立了基于区间组合法的边坡稳定非概率模糊可靠性分析方法。工程算例表明,该方法具有一定的合理性,为边坡工程稳定性评价提供了新的途径。

稳态热传导结构非概率可靠性拓扑优化设计

研究具有区间参数的稳态热传导结构在散热弱度非概率可靠性约束下的拓扑优化设计问题。建立了以单元相对导热系数为设计变量,导热材料体积极小化为目标函数,满足散热弱度非概率可靠性为约束条件的稳态热传导结构的拓扑优化设计数学模型。基于区间因子法,推导出散热弱度的均值及离差的计算表达式。采用渐进结构优化法的求解策略与方法,并利用过滤技术消除优化过程中的数值不稳定性现象。通过算例验证所述模型及求解策略、方法的合理性和有效性。

非概率区间模型可靠性指标的梯度投影算法

考虑不确定参数为区间变量,研究求解非概率可靠性指标的有效搜索算法。基于函数梯度法的基本思想,构造搜索方向,建立迭代算法格式,将传统的用于概率可靠性分析的梯度投影法用于非概率可靠性指标的求解。当收敛点为非最可能失效点时,提出了空间降维算法,并给出了整个搜索算法的计算步骤。通过数值算例,验证了本文提出的搜索迭代算法的有效性和正确性。

边坡模糊随机可靠性分析

为合理描述边坡稳定性分析中存在的多种不确定性,应同时考虑随机和模糊两种因素带来的影响。用L-R型模糊数描述随机变量的均值和方差,建立了模糊随机可靠性分析模型。为简化计算,将模糊数通过分解定理,取一系列λ截集,转换为一系列区间数进行计算。在一次二阶矩法基础上,推导了基于瑞典条分法及毕肖普法计算模糊随机可靠度的具体公式。算例表明,该法是合理实用的。