基于粒子群优化Kriging模型的边坡可靠度分析

采用传统极限平衡法进行边坡可靠度分析时,不可避免会遇到一个问题,即边坡功能函数形式的高度非线性以及隐含性.对于隐式功能函数,传统的求解方法是通过对功能函数进行多次迭代,从而得到安全系数值.但是由于功能函数的形式较为复杂,导致迭代计算的过程变得尤为繁琐且效率低下.鉴于传统边坡可靠度分析中存在的安全系数计算繁琐耗时的问题,提出一种基于粒子群优化(PSO)算法的自动采样Kriging代理模型方法,该方法可以代替功能函数的作用进行安全系数的求解.首先用拉丁超立方抽样方法(LHS)选取少量土体参数组,并通过极限平衡法求出对应的安全系数,将土体参数组和安全系数作为初始样本建立Kriging模型;其次由粒子群优化算法将最有期望改善模型拟合精度的样本点添加到样本集合中,以逐步迭代提升Kriging模型的计算精度;最后集合经典蒙特卡洛模拟(MCS)获得边坡的破坏概率.通过一个双层的土质边坡算例分析,证明了该方法可以实现准确高效的安全系数计算,尤其是在安全系数计算量十分庞大的情况下可以大大节省计算时间,是一种有效的边坡工程稳定可靠度分析方法.     

几种结构非概率可靠性模型的比较研究

相对概率可靠性模型和模糊可靠性模型,基于区间分析的结构非概率可靠性模型对数据的要求低,因此在实际工程中对非概率可靠性模型的研究越来越重要.近年来,非概率可靠性理论得到了很好的发展和完善.文中综述了已有的4种主要的非概率可靠性模型,针对线性结构功能函数,分别从度量原理、可靠性指标物理意义、适用范围和结果精度等方面对各可靠性模型进行比较与总结;针对非线性结构功能函数,对各可靠性模型的适用性进行了初步的讨论,从而对非概率可靠性模型有更加全面和深刻的理解,为实际工程中非概率可靠性模型的选取提供重要的理论依据.     

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。     

结构可靠性分析的支持向量机方法

针对结构可靠性分析中功能函数不能显式表达的问题,将支持向量机方法引入到结构可靠性分析中．支持向量机是一种实现了结构风险最小化原则的分类技术,它具有出色的小样本学习性能和良好的泛化性能,因此提出了两种基于支持向量机的结构可靠性分析方法．与传统的响应面法和神经网络法相比,支持向量机可靠性分析方法的显著特点是在小样本下高精度地逼近函数,并且可以避免维数灾难．算例结果也充分表明支持向量机方法可以在抽样范围内很好地逼近真实的功能函数,减少隐式功能函数分析（通常是有限元分析）的次数,具有一定的工程实用价值．     

基于改进粒子群算法的模糊聚类-概率神经网络模型的企业财务危机预警模型研究

为了充分发挥概率神经网络在企业财务危机预警中的作用,克服概率神经网络平滑参数难以确定和空间复杂度高的不足,本文提出一类新的参数动态调整的粒子群算法优化概率神经网络的平滑参数,进而采用改进粒子群算法优化初始隶属度矩阵的模糊聚类方法实现对样本的选择,解决了概率神经网络平滑参数的确定及空间结构复杂的问题。提出了基于改进粒子群算法的模糊聚类-概率神经网络企业财务危机预警模型,并以我国上市公司作为研究对象进行了实证研究。结果表明,经过模糊聚类和改进粒子群算法优化的概率神经网络具有更优的预测性能,并在企业财务危机长期预警方面具有一定效用。     

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.     

基于粒子群算法的非等距GOM（1，1）模型

等间距灰色GOM（1,1）模型是一种基于反向累加生成的灰色预测模型．为了拓广适用范围,提高GOM（1,1）模型的拟合和预测精度,给出了非等间距灰色GOM（1,1）模型的建模方法,并利用粒子群优化算法对非等间距灰色GOM（1,1）模型的参数进行优化．最后,利用一个仿真实例,表明基于粒子群优化算法的灰色GOM（1,1）模型...     

基于粒子进化的SD多功效流率函数及其应用北大核心CSCD

系统动力学(SD)模型的流率是动态仿真决策控制要素,但在SD方法中,仅给出定义式,解析范式缺失,实际应用常采用模糊确定的表函数.由于SD模型的流位演进流率与粒子群算法(PSO)的粒子进化速度属性类同,则在析解流率决策控制机制与粒子进化速度方程的基础上,依据粒子进化的结构原理,讨论流率函数结构,建立多功效流率函数解析式,提出系数估计策略,构造动态仿真算法.以区域生态经济系统动态仿真为例,将流率函数及其系数估计策略和动态仿真算法加以实际应用,以检验多功效流率函数的有效性和适用性.多功效流率函数物理意义明确,具有可比可控性、结构优化性和减少主观性等多重功效,可作为SD方法中流率函数的范式.     

基于粒子进化的SD多功效流率函数及其应用

系统动力学(SD)模型的流率是动态仿真决策控制要素,但在SD方法中,仅给出定义式,解析范式缺失,实际应用常采用模糊确定的表函数.由于SD模型的流位演进流率与粒子群算法(PSO)的粒子进化速度属性类同,则在析解流率决策控制机制与粒子进化速度方程的基础上,依据粒子进化的结构原理,讨论流率函数结构,建立多功效流率函数解析式,提出系数估计策略,构造动态仿真算法.以区域生态经济系统动态仿真为例,将流率函数及其系数估计策略和动态仿真算法加以实际应用,以检验多功效流率函数的有效性和适用性.多功效流率函数物理意义明确,具有可比可控性、结构优化性和减少主观性等多重功效,可作为SD方法中流率函数的范式.     

基于Kriging模型和提升小波变换的随机模型修正北大核心CSCD

为提高随机模型修正效率,减小计算量,提出了一种基于Kriging模型和提升小波变换的随机模型修正方法.首先,对加速度频响函数进行提升小波变换,提取第5层近似系数代替原频响函数.其次,采用拉丁超立方抽样抽取待修正样本,将其作为Kriging模型的输入,对应的近似系数作为输出,构建Kriging模型.提出了一种引入莱维飞行(Lévy flight)的蝴蝶优化算法(LBOA),并将其应用于Kriging模型相关参数的寻优中,提高Kriging模型的精度.最后,以最小化Wasserstein距离为目标,通过鲸鱼优化算法求解待修正参数的均值.测试函数结果表明,LBOA在寻优能力、收敛精度和稳定性等方面有了很大的提升.数值算例的修正误差均低于0.4%,验证了所提模型修正方法具有较高的修正精度和效率.     

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.     

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.     

An adaptive multiple-Kriging-surrogate method for time-dependent reliability analysis

For accurately and efficiently estimating the time-dependent failure probability (TDFP) of the structure, a novel adaptive multiple-Kriging-surrogate method is proposed. In the proposed method, the multiple Kriging models with different regression trends (i.e., constant, linear and quadratic) are simultaneously constructed with the highest accuracy, on which the TDFP can be obtained. The multiple regression trends are adaptively selected based on the size of sample base, the maximum differences of multiple models and the global accuracy of multiple models. After that, the most suitable multiple regression trends are identified. The proposed method can avoid man-made subjectivity for regression trend in general Kriging surrogate method. Furthermore, better accuracy and efficiency will be obtained by the proposed multiple surrogates than just using a fixed regression model for some engineering applications. Five examples involving four applications with explicit performance function and one tone arch bridge under hurricane load example with implicit performance function are introduced to illustrate the effectiveness of the proposed method for estimating TDFP.     

Diseño óptimo robusto utilizando modelos Kriging: aplicación al diseño óptimo robusto de estructuras articuladas

Conventional methods addressing the robust design optimization problem of structures usually require high computational requirements due to the nesting of uncertainty quantification within the optimization process. In order to address such a problem, this work proposes a methodology, based on Kriging models, to efficiently assess the uncertainty quantification in the optimization process. The Kriging model approximates the structural performance both in the design domain and in the stochastic domain, which allows to decouple the uncertainty quantification process and the optimization process. In addition, an infill criterion based on the variance of the Kriging prediction is included to update the Kriging model towards the global Pareto front. Three numerical examples show the applicability and the accuracy of the proposed methodology. The results show that the proposed method is appropriate to solve the robust design optimization problem with reasonable accuracy and a considerably lower number of function calls than required by conventional methods.     

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.     

结构模糊可靠性分析方法的泛灰求解

针对现有的基于区间求解结构模糊可靠度方法的缺陷,提出了一种新的求解结构模糊可靠度方法.该方法利用泛灰数描述与结构基本变量概率分布相关的不确定参数,并将这些泛灰数引入到结构模糊可靠度计算中,得出了较为精确的结构可靠度计算结果.数值算例表明,该方法得到的结构可靠度区间更窄,实现了利用较少的信息量得到较精确的可靠度计算结果,相比传统的结构模糊可靠度计算方法能提供更多、更精确的关于结构安全程度的有用信息.