基于神经网络响应面法的随机结构动力可靠度分析

在对神经网络响应面法的原理和算法进行研究的基础上，建立了基于神经网络响应面法的随机结构动力可靠度分析方法。首先，基于首次超越破坏准则，参照静力可靠度的功能函数模式，建立了随机结构的动力可靠度功能函数；然后引入响应面法，以三层BP神经网络作为拟合函数，推导了功能函数的拟合表达式；最后结合一次二阶矩方法求解可靠指标。算例分析表明了本文方法有较好的计算精度和计算效率，在复杂结构的动力可靠度分析中有较强的实用价值。     

基于响应面法的复式钢箱提篮拱地震可靠度分析

地震可靠度是桥梁抗震研究中的重要问题。基于随机分析的响应面理论和规范反应谱法,提出了一种分析具有随机结构参数的桥梁地震可靠度的方法,研究了复式钢箱提篮拱桥在地震激励下,结构设计基准期内的可靠度。分析时考虑了结构参数和场地土的随机性,分别计算了钢箱提篮拱桥主、副拱肋在多遇地震、设防地震和罕遇地震作用下的失效概率,得到了主、副拱肋在设计基准期内,按规范“三水准设防标准”条件下的地震可靠度。计算结果表明,该桥设计满足抗震规范要求。     

二次序列响应面法分析重力坝的动力可靠度

提出了用二次序列响应面法分析重力坝可靠度的方法。该方法用界面元法对重力坝进行确定性计算,在适当的区间内用统计的方法确定各变量的样本点,经过若干次重复计算,由界面元法的计算结果可以得到该结构的响应面函数,然后由这一函数算出重力坝可靠度的近似值。用同样方法可得第二、第三个响应面函数,并使可靠度计算结果逐渐达到良好的精度要求。这一方法与统计可靠度分析方法相比可以减少大量的计算工作量。对于包含混凝土特性和坝上游水位等随机变量的重力坝,本文用上述方法计算了一些关键时刻重力坝抗裂的动力可靠度,并据此分析了在整个地震过程中该坝抗裂可靠指标β（t）随时间t的变化规律。     

随机结构在随机载荷下的动力可靠度分析

提出了综合考虑动载荷和结构参数双重随机性的动力可靠度分析方法。首先基于随机振动的首次超越破坏准则,参照随机变量功能函数模式,建立了随机结构动力可靠度功能函数;然后引入序列响应面法,对功能函数进行拟合;最后用JC法求解可靠指标。算例分析验证了本文方法具有较好的计算精度和计算效率,尤其在复杂工程结构的动力可靠度分析中有广阔的应用前景。     

Kriging响应面代理模型在有限元模型确认中的应用

结合Kriging理论,实现Kriging响应面代理模型在有限元模型确认过程中响应预测的应用。讨论模型验证与确认的基本思想,初步提出有限元模型确认流程;以Garteur benchmark飞机结构瞬态响应仿真为例,建立加速度响应最大值Kriging响应面,通过蒙特卡洛方法,实现有限元模型参数不确定性正向传递;采用核密度估计建立加速度响应最大值概率分布,计算响应量置信区间上下限。结果表明,Kriging响应面能准确对有限元模型响应进行预测,可为有限元模型确认过程提供很大便利。     

MTMD控制下随机结构的动力可靠度分析

考虑到实际工程结构的不确定性,基于遗传优化的神经网络响应面法,进行了MTMD控制下随机结构的动力可靠度分析,并对TMD及MTMD控制下结构的动力可靠度进行对比。该方法不仅具有传统神经网络响应面法的特性,而且引进了遗传算法的全局随机搜索的优点,可以精确地逼近随机结构的功能函数表达式,有效地减少用JC法求解可靠度指标的迭代次数,节省时间。算例分析表明了本文方法的有效性和准确性,对于随机结构,MTMD比TMD能更好地提高结构的动力可靠度。     

带限位TMD的抗风动力可靠度研究

摘要：针对实际高层高耸结构在设置TMD控制装置时,存在空间位置有限、TMD行程受限的问题,进行了带限位TMD的抗风动力可靠度研究。首先,采用虚拟激励法计算复杂高耸结构—TMD体系在脉动风荷载下的随机风振响应;然后,基于随机振动的首次超越破坏准则,研究TMD装置在容许行程范围内不同重现期风荷载下的动力可靠度。并以国内在建的第一高塔-广州新电视塔为工程算例,验证了该研究在实际设计中所具有的重大工程意义。     

改进响应面法计算钢筋混凝土框架体系可靠度

钢筋混凝土框架结构体系可靠度是评定结构抗震性能的重要指标,也是对既有结构进行加固的评估基础。以框架底层柱端极限弯矩承载力作为参考变量可有效评估框架结构体系可靠度,该方法采用Pushover静力推覆分析,通过研究框架结构底层柱端极限弯矩承载能力对Pushover曲线的影响规律,由Pushover曲线上的基准点推广得到结构失效响应面,从而计算出结构体系可靠度。应用该方法对一榀钢筋混凝土框架进行了体系可靠度计算,计算结果与直接响应面法和蒙特卡洛法分析结果进行对比,结果表明,该方法计算快捷,且能够很好地评估出框架体系抗震可靠度。     

响应面法在结构体系可靠度分析中的应用

一个失效模式由许多的失效单元构成,它是一个并联系统;而所有的失效模式构成一个串联系统.整个结构体系可看成是许多并联系统(失效模式)组成的一个串联系统.首先,利用基于响应面的随机有限元法来获得失效模式中各个单元的极限状态方程,这些方程都是二次多项式;第二步,利用结构可靠度分析中的几何法得到这些方程的等效线性化方程从而可逐步得到该失效模式的等效线性化方程;第三步,计算各失效模式间的相关系数;最后,由Ditlevsen界限法来计算结构的体系可靠度.算例表明,利用该方法来获得大型、复杂结构的体系可靠度具有高效、实用的特点.     

基于响应面法的结构动力学模型修正

为了获得精确的结构动力学模型,提出了响应面和优化相结合的方法。利用参数化模型和优化拉丁方试验设计获取样本点构造多项式响应面模型,最小二乘法确定多项式系数并检验响应面的拟合精度。用响应面计算结果与实验结果的误差构造目标函数,自适应模拟退火算法来优化修正响应面参数,将修正后的参数值带入有限元模型得到修正模型。以欧洲航空科技组织的基准模型GARTEUR飞机模型为算例,对比修正前后模态频率,结果表明修正后的模型在测试频段和预测频段具有良好的复现和预测能力,进而验证了基于响应面法与优化方法相结合的结构动力学有限元模型修正的有效性。     

A response surface method based on weighted regression for structural reliability analysis

Approximation methods such as the response surface method (RSM) are widely used to alleviate the computational burden of engineering analyses. For reliability analysis, the common approach in the RSM is to use regression methods based on least square methods. However, for structural reliability problems, RSMs should approximate the performance function around the design point where its value is close to zero. Therefore, in this study, a new response surface called ADAPRES is proposed, in which a weighted regression method is applied in place of normal regression. The experimental points are also selected from the region where the design point is most likely to exist. Examples are given to demonstrate the benefit of the proposed method for both numerical and implicit performance functions.     

基于Kriging模型的充液管道共振非概率可靠性分析

为解决传统概率可靠性在解决流固耦合问题方面的不足,研究了结构不确定参量用超椭球凸集模型和区间变量共同描述下的非概率共振可靠性问题。针对隐式极限状态函数难以求解的问题,引入Kriging模型和超立方抽样技术应用于非概率可靠性分析。该方法用Kriging模型作为近似模型描述原结构,并在计算过程中不断更新近似模型。考虑管道与液体之间的耦合作用,利用有限元软件对所建立的简单管道系统进行模态计算并且结合防共振理论进行充液管路系统的流固耦合振动非概率可靠性分析,用优化的方法计算可靠性指标。工程算例分析表明该方法的合理性,能完善流固耦合管道系统的防共振可靠性分析方法与理论。     

Unified reliability analysis by active learning Kriging model combining with random‐set based Monte Carlo simulation method

Reliability analysis with both aleatory and epistemic uncertainties is investigated in this paper. The aleatory uncertainties are described with random variables, and epistemic uncertainties are tackled with evidence theory. To estimate the bounds of failure probability, several methods have been proposed. However, the existing methods suffer the dimensionality challenge of epistemic variables. To get rid of this challenge, a so‐called random‐set based Monte Carlo simulation (RS‐MCS) method derived from the theory of random sets is offered. Nevertheless, RS‐MCS is also computational expensive. So an active learning Kriging (ALK) model that only rightly predicts the sign of performance function is introduced and closely integrated with RS‐MCS. The proposed method is termed as ALK‐RS‐MCS. ALK‐RS‐MCS accurately predicts the bounds of failure probability using as few function calls as possible. Moreover, in ALK‐RS‐MCS, an optimization method based on Karush–Kuhn–Tucker conditions is proposed to make the estimation of failure probability interval more efficient based on the Kriging model. The efficiency and accuracy of the proposed approach are demonstrated with four examples. Copyright © 2016 John Wiley & Sons, Ltd.     

Response surface method based on uniform design and weighted least squares for non-probabilistic reliability analysis

The non-probabilistic reliability theory is a promising methodology for implementing structural reliability analysis in case of scarce statistical data. One of the main obstacles to implement non-probabilistic reliability analysis is the implication of the limit state function (LSF) for complex structures. This paper aims to establish a surrogate model of the LSF with higher simulation precision, and whereby proposes a response surface method based on the combination of uniform design (UD) and weighted least squares (WLS). At first, the UD method is selected as the sampling method of interval variables to realize the uniform space-filling of the initial samples, and the sample set is updated by gradually adding the approximate optimal points to increase the sampling density of critical domain. Then, the WLS method is applied to improve the precision of the response surface by adjusting the importance of samples to the function fitting. Finally, a method of constructing sample weights is developed. Two examples are applied to validate the feasibility and efficiency of the proposed method. Results show that the proposed method is effective for non-probabilistic reliability analysis of complex structures owning to high computational precision and low computational cost in both numerical and case study.     

Bounds approximation of limit-state surface based on active learning Kriging model with truncated candidate region for random-interval hybrid reliability analysis

This article reports a brand-new methodology based on active learning Kriging model for hybrid reliability analysis (HRA) with both random and interval variables. Unlike probabilistic reliability analysis, the limit state surface (LSS) of HRA is projected into a banded region in the domain of random variables. Only approximating the bounds of the banded region is able to meet the accuracy requirement of HRA. In the proposed methodology, the HRA problem is innovatively transformed into a traditional system reliability analysis (SRA) problem with numerous failure modes. And then a basic idea from the field of SRA is borrowed into HRA, and the so-called truncated candidate region (TCR) for HRA is proposed. In each iteration, the negligible region which probably does not influence the bounds estimation of failure probability is truncated from the original candidate region, and the optimal training point is chosen from the TCR. After several iterations, the TCR will converge to the true ideal candidate region, that is, the candidate region without the inner part of LSS, and the added training points will be driven to the region around the bounds of LSS. The performance of the proposed method is compared with relevant methods by five case studies.     

An active learning Kriging model combined with directional importance sampling method for efficient reliability analysis

In this paper, the active learning Kriging model (ALK), which has been studied extensively in recent years, has been expanded by combining with the directional importance sampling (DIS) method. The directional sampling method can reduce the dimensionality of the variable space by random sampling or interpolation in the direction of vector diameter, which can improve the efficiency of reliability analysis. It is especially suitable for the surfaces whose limit state is spherical or near-spherical. By introducing the control coefficient and constructing the directional importance sampling density function, the sampling efficiency can be further improved in the design point domain. A novel reliability analysis method called ALK-DIS method is proposed. The greatest advantage of the proposed method is its ability on great computational efficiency and dealing with small failure probability problem In addition, due to the excellent performance of directional sampling method in dealing with multi-failure model reliability problems, the ALK-DIS method has the advantage of being applied to system reliability analysis in this paper successfully. The applicability, feasibility and efficiency of the proposed method are proved on examples which contain linearity equation, non-linear numerical example, non-linear oscillator and system reliability engineering problems.     

飞机内襟翼机构动态响应可靠性分析

基于虚拟样机技术和可靠性分析理论,研究了某飞机内襟翼机构动态响应的可靠性.首先,采用有限元分析软件Patran和动力学分析软件ADAMS建立了刚柔耦合的内襟翼机构虚拟样机,并进行了动态仿真,得到了给定外载荷下襟翼机构保持规定运动规律所需的驱动力随时间变化的规律.其次,分析了滑轨、滚轮尺寸及气动载荷具有随机不确定性且丝杆最大驱动力为定值情况下的襟翼动态卡滞可靠性,通过对运动时域中功能随机过程的极小化变换,用矩方法和随机响应面法对极小化的功能随机函数统计分布规律进行了近似,编程实现了襟翼机构卡滞失效概率的求解,为襟翼运动机构的可靠性设计提供了一种可行方法.     

Application of the response surface methods to solve inverse reliability problems with implicit response functions

The inverse first-order reliability method (FORM) is considered to be one of the most widely used methods in inverse reliability analysis. It has been recognized that there are shortcomings of the inverse FORM in solving inverse reliability problems with implicit response functions, primarily inefficiency and difficulties involved in evaluating derivatives of the implicit response functions with respect to random variables. In order to apply the inverse FORM to structural inverse reliability analysis, response surface methods can be used to overcome the shortcomings. In the present paper, two different response surface methods, namely the polynomial-based response surface method and the artificial neural network-based response surface method, are developed to solve the inverse reliability problems with implicit response functions, and the accuracy and efficiency of the two response surface methods are demonstrated through two numerical examples of steel structures. It is found that the polynomial-based response surface method is more efficient and accurate than the artificial neural network-based response surface method. Recommendations are made regarding the suitability of the two response surface methods to solve the inverse reliability problems with implicit response functions.     

基于改进云图法的高土石坝抗震可靠度分析

高土石坝抗震可靠度研究对大坝抗震防灾和震害风险研究具有重要意义。通过考虑地震动和筑坝料参数双重随机性,建立基于地震易损性和地震峰值加速度概率密度函数的高土石坝抗震可靠度模型,为研究不同设计使用年限的高土石坝抗震可靠度提供依据。通过拉丁抽样方法选取筑坝料参数样本并与选择地震动组合成样本对,选取坝顶相对震陷率作为性能参数,提出考虑抗震设防标准的高土石坝性能水平了;采用SWANDYNE Ⅱ程序进行动力计算,并根据改进云图法得到不同地震峰值加速度下坝顶相对震陷率的地震易损性三维曲面;结合糯扎渡高土石坝不同设计年限的概率分布函数与地震易损性曲面,确定不同设计年限失效概率和抗震可靠度。分析结果表明:随着设计使用年限增加,大坝各个性能水平可靠度不断减小,对于严重破坏状态下不同设计年限可靠度均能满足《水利水电工程结构可靠性设计统一标准》规范要求,说明糯扎渡高土石坝在变形方面抗震设计是合理的。     

Assessment of the efficiency of Kriging surrogate models for structural reliability analysis

This paper presents an assessment of the efficiency of the Kriging interpolation models as surrogate models for structural reliability problems involving time-consuming numerical models such as nonlinear finite element analysis structural models. The efficiency assessment is performed through a systematic comparison of the accuracy of the failure probability predictions based on the first-order reliability method using the most common first- and second-order polynomial regression models and the Kriging interpolation models as surrogates for the true limit state function. An application problem of practical importance in the field of marine structures that requires the evaluation of a nonlinear finite element structural model is adopted as numerical example. The accuracy of the failure probability predictions is characterised as a function of the number of support points, dispersion of the support points in relation to the so-called design point and order of the Kriging basis functions. It is shown with the application problem considered that the Kriging interpolation models are efficient surrogate models for structural reliability problems and can provide significantly more accurate failure probability predictions as compared with the most common polynomial regression models.