考虑质量变化的空间拱结构小损伤识别方法研究

该研究针对空间拱结构损伤识别精度问题,提出了一种空间拱结构小损伤的动态识别方法。该方法在识别过程中考虑损伤区的质量变化的因素来提高了结构损伤识别的精度。在建立的空间拱结构损伤模型中,应用频率和振型的质量因子变化来表示损伤区质量的变化。应用Wittrick-Williams算法对损伤参数进行计算,根据计算结果来判定结构的损伤位置和程度。为了实现应用最少的测量模态和结构振动特性数据对结构损伤精确识别,建立了一个应用遗传算法的优化计算程序对损伤参数优化计算。在该优化程序中,应用初始的随机染色体群代表沿空间拱不同的损伤分布状况。通过对单一损伤、多个损伤区域和不同边界条件下空间拱进行损伤识别案例对所述方法进行验证,并分析解决了对称结构损伤定位唯一性的问题。结果表明,所提出的识别方法对空间拱结构的损伤具有较好的识别效果,所建立的损伤识别程序具有较高的识别精度。     

基于模型修正的钢管焊接结构焊缝损伤识别

摘要：本文提出了一种基于模型修正的钢管焊接结构焊缝损伤识别方法。利用从发射台骨架试验模型获取的模态参数,选择识别结果中精度较好的模态频率作为模型修正的基准频率。通过对待修正参数的灵敏度分析,运用ANSYS和MATLAB软件对有限元模型进行了修正。以实测模态和计算模态之间的误差建立一个带约束边界非线性最小二乘目标函数,将损伤识别问题转化为优化问题,并采用信赖域方法求解该优化问题。以有限元模型焊接结点单元组弹性模量的降低模拟焊缝损伤,并假定了两种损伤工况,通过对发射台骨架模型的数值仿真及试验研究,结果表明本文提出的损伤识别方法识别效果较为理想,为解决这种复杂焊接结构焊缝损伤识别问题提供了新的思路。     

结构损伤与荷载的快速同步识别方法研究

本文仅以损伤因子为优化变量,提出一种结构损伤和荷载同步识别的方法。首先通过时域荷载识别的方法将未知荷载转化为损伤因子的函数,将近似荷载作用下的结构响应和实测响应的平方距离作为目标函数,从而降低了需要识别未知参数的数目;然后在目标函数的计算过程中,利用虚拟变形法（VDM）可进行结构快速重分析的思想,快速构造给定损伤因子下系统的脉冲响应,避免每步迭代重新集装系统矩阵,并通过荷载形函数方法进一步提高荷载识别的效率;最后利用二次多项式插值近似结构每个时刻的响应方法和推导对应目标函数的梯度表达式来提高优化搜索的速度。本文利用刚架模型进行数值模拟,准确识别了结构中柱子单元刚度损伤、附加质量以及梁上的未知移动荷载,并通过一个悬臂梁试验进一步验证所提出方法的准确性和可行性。     

基于动响应约束的结构材料优化设计

针对于随机荷载作用下动响应为约束的结构材料优化问题,基于结构拓扑优化思想,提出了一种变动响应约束的结构材料优化方法。采用分式有理式和幂函数识别结构材料单元特性参数,以微观单元拓扑变量倒数为设计变量,导出了频率及振型对微观单元设计变量的一阶导数,进而得到了随机荷载作用下结构均方响应的一阶近似展开式。结合变约束限的思想,建立了以结构质量作为目标函数,均方响应作为约束条件的连续体微结构拓扑优化近似模型,并采用对偶方法进行求解。对典型结构进行了考虑单个和多个动响应约束的结构材料优化设计,优化所得结果验证了该方法的有效性和可行性。     

基于不完备实测模态数据的结构损伤识别方法研究

在传统的基于模型修正的损伤识别中,由于实测模态信息有限而待识别参数过多,往往导致损伤识别方程出现较大误差,从而限制了该方法在复杂结构中的应用。为解决这一问题,对结构的自由度进行了分解,将损伤结构中模态振型的未测量部分表达为已测量到的模态振型、模态频率以及结构其它参数的函数。将损伤视为结构单元刚度的减小,利用完好结构的计算模态数据以及损伤结构扩充后的实测模态数据,建立了结构的损伤识别方程。运用信赖域优化算法对具有双重约束条件的目标函数进行最小化,识别出了结构各单元的刚度损伤参数。通过两个损伤识别数值仿真算例及实验验证,结果表明,在测点数量有限及测试噪声等不利因素影响下,所提方法只需运用少量的实测模态信息,即可实现结构损伤位置及程度的准确识别,同时算法具有较好的鲁棒性。     

基于EMD和VARMA模型的结构损伤识别

提出了基于EMD和VARMA模型的结构损伤识别方法。该方法首先将结构反应信号用EMD方法分解成一系列固有模态函数,然后将固有模态函数表示为时变VARMA模型并用Kalman滤波方法估计时变VARMA参数,最后根据时变VARMA参数定义一个新的损伤指标用于结构损伤识别。为检验该指标的实际性能,算例中选用ImperialCounty Services Building和Van Nuys Hotel作为基准结构。通过其实测地震反应记录的分析表明:该指标在实际的量测环境和噪声条件下具有较好的敏感性和抗噪能力,可有效地识别结构多处损伤的发生过程和严重程度;由于该指标定义在反应信号特征提取的基础上,无需其他额外的信息,它可同时用于结构整体和局部两个层次损伤的识别;同时,该指标还适于实时(在线)的结构损伤识别或健康监测,因其直接由时变VARMA参数推导得出。最后,对后续研究工作进行了展望。     

基于同伦迭代算法的弱耦合梁的损伤识别

本文应用有限元方法将多跨连续梁结构简化为弱耦合的欧拉－伯努利梁模型,利用Newmark积分法直接积分法求出系统在外激励作用下的动态响应。在结构的损伤识别过程中以结构抗弯刚度的减少作为损伤识别参数进行损伤识别,通过最小二乘法来构造同伦方程。该方法的收敛不依赖于初始值的选择,并且可以对具有重频的多跨结构进行准确识别。讨论了模拟人工测量噪声对损伤识别结果的影响。数值模拟算例表明本方法能够快速有效地检测出弱耦合梁的局部损伤,并具有精度高,对测量噪声不敏感等特点。     

基于遗传算法的磁流变阻尼器Bouc_Wen模型参数辨识

在除试验数据外无任何先验知识的条件下如何识别Bouc_Wen模型的参数是一个亟待解决的问题。本文在对磁流变阻尼器进行力学特性试验的基础上,采用遗传算法对磁流变阻尼器Bouc_Wen模型进行参数辨识,并采用缩小参数取值范围的方法逐渐提高遗传算法的识别精度。通过分析参数值与电流间的特征曲线,采用曲线拟合的方法确定它们之间的函数关系,再利用遗传算法得到具体的函数表达式。最后用不同幅值和频率的激励试验数据对识别结果和拟合结果进行了验证。结果表明：利用缩小取值范围的方法得到的Bouc_Wen模型参数识别结果在全局最优解的附近,拟合结果和辨识出的模型均能满足要求     

动载荷时域半解析识别方法

基于模态空间转换、离散数据最小二乘拟合与模态叠加原理,提出一种动载荷时域半解析识别方法。通过模态空间转化将动载荷识别问题转化为模态坐标函数识别问题,以结构特性参数与已知自由度上的动响应为输入,利用最小二乘拟合得到模态坐标的拟合函数,根据叠加原理与结构动力学控制方程,识别出结构时域动载荷。分别用计算仿真与试验测试得到的时域加速度响应作为输入,实现时域动载荷的识别。识别结果与真实动载荷对比表明,本文的识别方法能较准确地识别结构动载荷值及载荷作用部位。     

有限观测绝对加速度响应下剪切框架在未知地震作用下损伤诊断

针对框架结构的绝对加速度响应部分观测、地震作用未观测的情况,提出一种多层剪切框架结构诊断的方法。该方法先依次采用扩展卡尔曼估计和递推最小二乘对一层以上结构的扩展状态向量和未知作用力进行递推;然后利用结构频率特征方程,对第一层的结构参数进行估计;最后基于数值求解一阶微分方程,识别未观测的地震荷载。算例表明, 该方法能够很好识别出结构参数和地震输入,通过跟踪结构刚度参数的退化,对未知地震作用下结构损伤进行诊断。     

基于频响函数和遗传算法的结构损伤识别研究

提出一种基于频响函数和遗传算法的结构损伤识别方法.以单元刚度折减因子为遗传算法的优化变量,以测试频响函数和计算频响函数的形状相关系数来构造遗传算法的优化目标函数和适应度函数;为克服二进制编码的缺点,采用浮点数编码方案;最后通过一个桁架结构模型进行数值模拟,计算结果表明,即使在考虑一定测量噪声水平的情况下,仍然能够准确识别出结构的多处损伤,验证了该方法的有效性和可行性.     

遗传演化结构优化算法

ESO算法是近年来提出的一种结构优化算法,它可以应用于多个领域。在土木工程方面,利用ESO算法可以寻找结构最优的拓扑形状,指导结构的设计。但这种算法存在先天的局限性,即无法保证它所得到的解是最优解。为此,将遗传算法与ESO算法揉合在一起,形成了一种新的遗传ESO算法,简称为GESO算法。GESO算法把群体的概念借鉴到ESO中,巧妙地解决了遗传算法费时和ESO易陷入局部最优解的两个问题。实例证明它得到的结果大多优于ESO算法。     

An engineering method for complex structural optimization involving both size and topology design variables

This work presents an engineering method for optimizing structures made of bars, beams, plates, or a combination of those components. Corresponding problems involve both continuous (size) and discrete (topology) variables. Using a branched multipoint approximate function, which involves such mixed variables, a series of sequential approximate problems are constructed to make the primal problem explicit. To solve the approximate problems, genetic algorithm (GA) is utilized to optimize discrete variables, and when calculating individual fitness values in GA, a second-level approximate problem only involving retained continuous variables is built to optimize continuous variables. The solution to the second-level approximate problem can be easily obtained with dual methods. Structural analyses are only needed before improving the branched approximate functions in the iteration cycles. The method aims at optimal design of discrete structures consisting of bars, beams, plates, or other components. Numerical examples are given to illustrate its effectiveness, including frame topology optimization, layout optimization of stiffeners modeled with beams or shells, concurrent layout optimization of beam and shell components, and an application in a microsatellite structure. Optimization results show that the number of structural analyses is dramatically decreased when compared with pure GA while even comparable to pure sizing optimization.     

基于MATLAB的一种混合遗传算法的研究

在简要分析简单遗传算法的基础上,介绍了一种改进的混合遗传算法.使用MATLAB语言编制了GA及其改进算法的实现程序,改进算法可以大幅度提高GA用于求解复杂问题的鲁棒性.多峰值函数优化结果表明,该算法能更有效地达到全局最优解.     

Simultaneous distribution and sizing optimization for stiffeners with an improved genetic algorithm with two-level approximation

This article presents an improved genetic algorithm with two-level approximation (GATA) to optimize the distribution and size of stiffeners simultaneously. A novel optimization model of stiffeners, including two kinds of design variables, is established. The first level approximation problem transforms the original implicit problem to an explicit problem which involves the topology and size variables. Then, a genetic algorithm (GA) addresses the mixed variables. The individuals in the GA are coded by topology variables, and when calculating an individual’s fitness, the second level approximation problem is embedded to optimize the size variables. Considering the stiffeners’ optimization, several aspects of the initial GATA are updated, including the relationship between two kinds of variables, the weight and its sensitivity calculation and the GA strategy, to optimize the stiffeners’ size and distribution simultaneously. Numerical examples show that the improved GATA is effective in optimizing the stiffened shells’ topology and size variables simultaneously.     

Topology optimization of trusses using genetic algorithm,force method and graph theory

In this article size/topology optimization of trusses is performed using a genetic algorithm (GA), the force method and some concepts of graph theory. One of the main difficulties with optimization with a GA is that the parameters involved are not completely known and the number of operations needed is often quite high. Application of some concepts of the force method, together with theory of graphs, make the generation of a suitable initial population well‐matched with critical paths for the transformation of internal forces feasible. In the process of optimization generated topologically unstable trusses are identified without any matrix manipulation and highly penalized. Identifying a suitable range for the cross‐section of each member for the ground structure in the list of profiles, the length of the substrings representing the cross‐sectional design variables are reduced. Using a contraction algorithm, the length of the strings is further reduced and a GA is performed in a smaller domain of design space. The above process is accompanied by efficient methods for selection, and by using a suitable penalty function in order to reduce the number of numerical operations and to increase the speed of the optimization toward a global optimum. The efficiency of the present method is illustrated using some examples, and compared to those of previous studies. Copyright © 2003 John Wiley & Sons, Ltd.     

Optimization of Construction of Tire Reinforcement by Genetic Algorithm

A new tire design procedure capable of determining the optimum tire construction was developed by combining a finite element method approach with mathematical programming and a genetic algorithm (GA). Both procedures successfully generated optimized belt structures. The design variables in the mathematical programming were belt angle and belt width. Using the merits of a GA which enabled the use of discrete variables, the design variables in the GA were not only the topology of the belt and belt angle but also the belt material. Furthermore, a discrete objective function such as the number of parts could be optimized in the GA. The optimized structure obtained by the GA was verified to increase the cornering stiffness more than 15 percent as compared with the control structure in an indoor drum test.     

包装物回收物流中的车辆路径优化问题

目的提高遗传算法(GA)求解包装物回收车辆路径优化问题的性能。方法通过对传统GA算法的改进,提出混合蜂群遗传算法(HBGA)。首先改进传统GA算法的初始种群生成方式,设计初始种群混合生成算子;其次,提出最大保留交叉算子,对优秀子路径进行保护;然后,在上述改进的基础上引入蜜蜂进化机制,用以保证种群多样性和优秀个体特征信息的利用程度;最后,对标准算例集进行仿真测试。结果与传统GA算法相比,HBGA算法在全局寻优能力、算法稳定性和运行速度方面均有所改善。HBGA算法的全局寻优能力和算法稳定性均优于粒子群算法(PSO)、蚁群算法(ACO)和禁忌搜索算法(TS),但运行速度稍慢于TS算法。结论对传统GA算法的改进是合理的,且HBGA算法整体求解性能优于PSO算法、ACO算法和TS算法。     

求解约束优化问题的退火遗传算法

针对基于罚函数遗传算法求解实际约束优化问题的困难与缺点,提出了求解约束优化问题的退火遗传算法。对种群中的个体定义了不可行度,并设计退火遗传选择操作。算法分三阶段进行,首先用退火算法搜索产生初始种群体,随后利用遗传算法使搜索逐渐收敛于可行的全局最优解或较优解,最后用退火优化算法对解进行局部优化。两个典型的仿真例子计算结果证明该算法能极大地提高计算稳定性和精度。     

Topology optimization of fluid problems using genetic algorithm assisted by the Kriging model

A non‐gradient‐based approach for topology optimization using a genetic algorithm is proposed in this paper. The genetic algorithm used in this paper is assisted by the Kriging surrogate model to reduce computational cost required for function evaluation. To validate the non‐gradient‐based topology optimization method in flow problems, this research focuses on two single‐objective optimization problems, where the objective functions are to minimize pressure loss and to maximize heat transfer of flow channels, and one multi‐objective optimization problem, which combines earlier two single‐objective optimization problems. The shape of flow channels is represented by the level set function. The pressure loss and the heat transfer performance of the channels are evaluated by the Building‐Cube Method code, which is a Cartesian‐mesh CFD solver. The proposed method resulted in an agreement with previous study in the single‐objective problems in its topology and achieved global exploration of non‐dominated solutions in the multi‐objective problems. © 2016 The Authors International Journal for Numerical Methods in Engineering Published by John Wiley & Sons Ltd