基于区间的土体参数敏感性分析方法研究

将一种新的工程结构不确定性分析方法——区间分析方法溶入工程参数的敏感性分析之中,获得了一种新的工程参数敏感性分析方法,进一步拓宽了区间分析方法理论的应用领域．给出了土体参数敏感性因子矩阵求解的区间分析过程,依据区间分析给出了参数区间和决策目标区间的确定方法．基于MARC软件进行了二次开发,实现了Duncan-Chang非线性弹性模型以及与Fortran程序的相互调用功能．通过工程算例验证了该方法的合理可行性,并与文献的结果进行了对比．     

区间运算和静力区间有限元

用均值和离差两参数表征区间变量的不确定性,根据区间运算规则,论证了区间变量的运算特性.将区间分析和有限元方法相结合,提出了非概率不确定结构的一种区间有限元分析方法.将区间有限元静力控制方程中n自由度不确定位移场特征参数的求解归结为求解一2n阶线性方程组.实例分析表明文中方法是有效和可行的.     

区间参数结构振动问题的矩阵摄动法

当结构的参数具有不确定性时，结构的固有频率也将具有某种程度的不确定性．本文讨论了区间参数结构的振动问题，将区间参数结构的特征值问题归结为两个不同的特征值问题来求解．提出了求解区间参数结构振动问题的矩阵摄动方法．数值运算结果表明，本文所提出方法具有运算量小，结果精度高等优点．     

数字化预案中基于中介逻辑的不确定事件表示

突发公共事件本身的不确定性以及紧急状态下信息采集的时空局限性,要求数字化预案对不确定事件进行有效地表达.基于中介逻辑,研究了不确定事件的有效表示及不确定程度的度量,采用区间代数方法对不确定事件进行了表示,建立了一维和n维情形下不确定事件的不确定程度的度量模型.在此基础上,利用中介逻辑对数字化预案中应急响应规则进行表达,对具体算例进行了分析,初步验证了该模型的有效性.     

决策依赖不确定条件下基于复合实物期权估值的工程系统投资决策研究

不确定性是复杂工程系统的内在属性,在决策依赖不确定条件下对工程系统的投资决策需考虑不确定性与决策过程之间的交互作用,使得投资决策问题的求解非常困难.提出了决策依赖不确定条件下复合实物期权估值的最小二乘模拟算法,方法较好地解决了在决策依赖不确定条件下由于不同期权价值相互耦合所带来的计算复杂性,进一步拓展了最小二乘模拟算法在期权估值中的应用,基于该方法,可以比较方便地解决决策依赖不确定条件下工程系统投资决策问题.     

化工园区应急设施区间规划选址模型研究

应急设施选址受应急物资需求量的影响。为优化应急设施选址布局,提高突发事件应急处置能力,以化工园区突发事件为研究背景,对化工园区突发事故下应急设施选址进行研究。考虑到化工园区突发事件的随机性和复杂性、突发事件应急物资需求的不确定性等特点,以应急设施选址安全性最大、经济性和服务效益最好为目标,基于传统确定性应急设施选址模型,构建了不确定需求条件下化工园区应急设施选址区间规划数学模型。模型中应急物资需求量是一个区间值,通过引入区间规划理论和模糊理论对模型进行求解,不仅避免了不确定参数随机概率分布的波动率,而且也降低了模型求解过程中的不确定性。最后,以园区各企业潜在事故为工程背景进行实例分析,得到园区应急设施的布局方案。结果表明,模型的求解效果较好,可为园区应急设施选址决策提供参考依据。     

在模糊随机因素影响下的多层圆筒结构的稳态热传导问题的区间数解法

在多层圆筒结构稳态热传导分析中,根据给定固体壁两侧表面温度总传热量公式,首先推导出当边界温度为随机变量情况下总传热量函数统计参数的均值和方差;然后推导出在导热系数为模糊数,边界温度为随机数下的总传热量的区间表达式．通过比较可以知道由区间数算法得到的区间最大,由概率统计算法得到的区间最小．并给出了两者的相对误差公式．最后引进粗糙集中的上、下近似集,提出用一个参数来统一定义模糊和随机区间进行稳态结构的热传导分析．     

飞艇姿态跟踪系统的研究

研究了具有参数不确定和外部干扰的飞艇姿态跟踪控制问题．飞艇姿态运动的数学模型为一个多输入/多输出不确定非线性系统,根据该系统的特点,采用了一个基于不确定项上界的鲁棒输出跟踪控制器设计方法,应用输入/输出反馈线性化法和李雅普诺夫方法,设计了飞艇姿态鲁棒控制律,它可确保系统输出按指数规律跟踪期望输出．该控制器设计简单,易于实现．仿真结果表明:即使系统存在不确定性和外界干扰,仍可在闭环系统中实现精确的姿态控制．     

基于集对分析的多种不确定偏好形式大群体决策方法

针对具有多种不确定偏好形式的多方案大群体决策问题,提出一种基于集对分析的群决策方法。将区间数、三角模糊数以及语言值三种形式的不确定偏好转换为联系数,保留了不确定偏好信息中的确定性与不确定性。提出一种区间聚类算法,在决策成员权重未知的情况下对成员进行赋权。利用加权综合联系数对大群体偏好进行集结,根据方案的集对势大小给出方案的排序。该方法避免了确定权重时的主观性,同时考虑决策信息的确定性与不确定性,提高了决策结果的可信度。通过实例分析验证了方法的有效性和实用性。     

基于SPA的D-U空间的区间数多属性决策模型及应用

区间数多属性决策既具有一定的确定性,又具有一定的不确定性.利用集对分析(SPA)的不确定性系统理论,将区间数映射到二维确定-不确定空间(D-U空间),把区间数转换为向量,建立区间数的模、幅角及三角函数表达式的概念,进而建立区间数的综合(基本、一般、主值)决策模型.实例计算表明:该模型能较为客观地反映出区间数多属性决策的确定性与不确定性,同时,更具有对不确定性分析的合理性.     

An iteration method for predicting static response of nonlinear structural systems with non-deterministic parameters

Confident numerical method is a crucial issue in the field of structural health monitoring. This paper focuses on uncertainty propagation in nonlinear structural systems with non-deterministic parameters. An interval-based iteration method is proposed on the basis of interval analysis and Taylor series expansion. The proposed method aims to improve the bounds of static response calculated by the point-based iteration method. In the proposed method, the iterative interval of static response is updated by revising the lower and upper bounds, respectively, which is the essential difference in comparison with the previous point-based iteration method. In this paper, interval parameters are employed to quantify the non-deterministic parameters instead of random parameters in the case of insufficient sample data. Iterative scheme is established based on the first-order Taylor series expansion. For the implementation of interval-based iteration method, a general procedure is formulated. Moreover, the important source of the limitation of point-based iteration method is revealed profoundly, and the good performance of the proposed method is demonstrated by three numerical comparisons.     

A feasible implementation procedure for interval analysis method from measurement data

An uncertain quantification and propagation procedure via interval analysis is proposed to deal with the uncertain structural problems in the case of the small sample measurement data in this study. By virtue of the construction of a membership function, a finite number of sample data on uncertain structural parameters are processed, and the effective interval estimation on uncertain parameters can be obtained. Moreover, uncertainty propagation based on interval analysis is performed to obtain the structural responses interval according to the quantified results of the uncertain structural parameters. The proposed method can decrease the demanding on the sample number of measurement data in comparison with the classical probabilistic method. For instance, the former only need several to tens of sample data, whereas the latter usually need several tens to several hundreds of them. The numerical examples illustrate the feasibility and validity of the proposed method for non-probabilistic quantification of limited uncertain information as well as propagation analysis.     

Interval modal superposition method for impulsive response of structures with uncertain-but-bounded external loads

This paper is concerned with the problem of the dynamic response of structures with uncertain-but-bounded external loads. Based on the theory of complex modal analysis, and interval mathematics, a new non-probabilistic method-interval modal superposition method is proposed to find the least favorable impulsive response and the most favorable impulsive response of structures. Through mathematical analysis and numerical calculation, comparisons between interval modal superposition method and probabilistic approach are made. Instead of probabilistic density distribution or statistical quantities, in the presented method, only the bounds on uncertain parameters are needed, Numerical examples indicate that the width of the region of the dynamic response yielded by the interval modal superposition method is larger than those produced by probabilistic approach while the interval modal superposition method will required less computation effort.     

Dynamic load identification for interval structures under a presupposition of ‘being included prior to being measured’

Influences of structural uncertainties in the dynamic load identification are always significant and need to be quantified. In case of insufficient information available, intervals are favorable for modelling uncertainties. To perform the interval propagation in an inverse problem, this paper develops a sequential dual-stage interval identification method under a presupposition that each noisy response, which is an accomplished measurement for reconstructing unknown loads, should be included in the corresponding interval response of the structure exerted by interval loads to be identified. The proposed method transforms the interval identification problem into a classical one at the midpoint of interval parameters and an optimization model for minimizing the radius of each interval load. The effectiveness of the proposed method is validated by a spatial truss subjected to multiple forces due to the inclusion of each unknown load in the corresponding load. Besides, regularized solutions without exact knowledge of the accuracy loss are recommended to be used as few as possible in the interval identification of unknown loads.     

Correlation propagation for uncertainty analysis of structures based on a non-probabilistic ellipsoidal model

Traditional non-probabilistic methods for uncertainty propagation problems evaluate only the lower and upper bounds of structural responses, lacking any analysis of the correlations among the structural multi-responses. In this paper, a new non-probabilistic correlation propagation method is proposed to effectively evaluate the intervals and non-probabilistic correlation matrix of the structural responses. The uncertainty propagation process with correlated parameters is first decomposed into an interval propagation problem and a correlation propagation problem. The ellipsoidal model is then utilized to describe the uncertainty domain of the correlated parameters. For the interval propagation problem, a subinterval decomposition analysis method is developed based on the ellipsoidal model to efficiently evaluate the intervals of responses with a low computational cost. More importantly, the non-probabilistic correlation propagation equations are newly derived for theoretically predicting the correlations among the uncertain responses. Finally, the multi-dimensional ellipsoidal model is adopted again to represent both uncertainties and correlations of multi-responses. Three examples are presented to examine the accuracy and effectiveness of the proposed method both numerically and experimentally.     

A Chebyshev interval method for nonlinear dynamic systems under uncertainty

This paper proposes a new interval analysis method for the dynamic response of nonlinear systems with uncertain-but-bounded parameters using Chebyshev polynomial series. Interval model can be used to describe nonlinear dynamic systems under uncertainty with low-order Taylor series expansions. However, the Taylor series-based interval method can only suit problems with small uncertain levels. To account for larger uncertain levels, this study introduces Chebyshev series expansions into interval model to develop a new uncertain method for dynamic nonlinear systems. In contrast to the Taylor series, the Chebyshev series can offer a higher numerical accuracy in the approximation of solutions. The Chebyshev inclusion function is developed to control the overestimation in interval computations, based on the truncated Chevbyshev series expansion. The Mehler integral is used to calculate the coefficients of Chebyshev polynomials. With the proposed Chebyshev approximation, the set of ordinary differential equations (ODEs) with interval parameters can be transformed to a new set of ODEs with deterministic parameters, to which many numerical solvers for ODEs can be directly applied. Two numerical examples are applied to demonstrate the effectiveness of the proposed method, in particular its ability to effectively control the overestimation as a non-intrusive method.     

Interval uncertainty analysis for static response of structures using radial basis functions

This paper proposes a new interval uncertainty analysis method for static response of structures with unknown-but-bounded parameters by using radial basis functions (RBFs). Recently, collocation methods (CM) which apply orthogonal polynomials are proposed to solve interval uncertainty quantification problems with high accuracy. These methods overcome the drawback of Taylor expansion based methods, which are prone to overestimate the response bounds. However, the form of orthogonal basis functions is very complicated in higher dimensions, which may restrict their application when there exist relatively more interval parameters. In contrast to orthogonal basis function, the form of radial basis function (RBF) is simple and stays the same in whatever dimension. This study introduces RBFs into interval analysis of structures and provides a relatively simple approach to solve structural response bounds accurately. A surrogate model of real structural response with respect to interval parameters is constructed with the RBFs. The extrema of the surrogate model can be calculated by some auxiliary methods. The static response bounds can be obtained accordingly. Two numerical examples are used to verify the proposed method. The engineering application of the proposed method is performed by a center wing-box. The results prove the effectiveness of the proposed method.     

An enhanced subinterval analysis method for uncertain structural problems

This paper proposes an enhanced subinterval analysis method to predict the bounds of structural response with interval parameters, which could deal with problems with relatively large uncertainties of the parameters. The intervals are first divided into several subintervals, and two expansion routes are then constructed based on the sensitivity analysis. Two subinterval sets are selected according to the expansion points on the routes, and the first order Taylor expansion method is then adopted to complete the subinterval analysis. Based on the selected subinterval sets, the upper and lower bounds of the structural response are further obtained by employing the interval union operation. An adaptive convergence approach is presented to determine the appropriate number of subintervals. Four numerical examples are investigated to demonstrate the validity of the proposed method.     

Subset selection for the least probable multinomial cell

Summary An inverse sampling procedureR is proposed for selecting a randomsize subset which contains the least probable cell (i.e., the cell with the smallest cell probabilities) from a multinomial distribution withk cells. Type 2-Dirichlet integrals are used (i) to express the probability of a correct selection in terms of integrals with parameters only in the limits of integration, (ii) to prove that the least favorable configuration underR is the so-called slippage configuration withk equal cell probabilities, and (iii) to express exactly the expectation of the total number of observations required and the expectation of the subset size under the procedureR.     

Sparse regression Chebyshev polynomial interval method for nonlinear dynamic systems under uncertainty

This paper proposes a new higher-efficiency interval method for the response bound estimation of nonlinear dynamic systems, whose uncertain parameters are bounded. This proposed method uses sparse regression and Chebyshev polynomials to help the interval analysis applied on the estimation. It is also a non-intrusive method which needs much fewer evaluations of original nonlinear dynamic systems than the other Chebyshev polynomials based interval methods. By using the proposed method, the response bound estimation of nonlinear dynamic systems can be performed more easily, even if the numerical simulation in nonlinear dynamic systems is costly or the number of uncertain parameters is higher than usual. In our approach, the sparse regression method “elastic net” is adopted to improve the sampling efficiency, but with sufficient accuracy. It alleviates the sample size required in coefficient calculation of the Chebyshev inclusion function in the sampling based methods. Moreover, some mature technologies are adopted to further reduce the sample size and to guarantee the accuracy of the estimation. So that the number of sampling, which solves the certain ordinary differential equations (ODEs), can be reduced significantly in the Chebyshev interval method. Three numerical examples are presented to illustrate the efficiency of proposed interval method. In particular, the last two examples are high dimension uncertain problems, which can further exhibit the ability to reduce the computational cost.