基于子结构虚拟变形的损伤识别方法

针对大型土木结构损伤识别优化效率低的问题,提出了子结构虚拟变形方法。虚拟变形方法是一种结构快速重分析的方法,该方法利用单元的虚拟变形模拟结构的损伤,可以在不重新建立有限元模型的情况下,快速计算出结构参数改变后的结构响应。该文基于虚拟变形法的基本思想,对子结构的刚度矩阵进行分解和对损伤后结构运动方程进行整理,推导出利用子结构的虚拟变形刻画损伤的方法,扩展了虚拟变形方法的适用范围;并且给出了虚拟变形和结构响应的相关性计算公式,通过相关性分析提取主要的虚拟变形,减少参与计算的子结构虚拟变形的数目,提高计算效率;最后利用一个五十层框架的数值仿真验证方法的有效性。     

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。     

一种改进的基于频率测量的结构损伤识别方法

基于频率测量的结构损伤识别方法具有测试简便,精度较好的优点.在分析结构固有频率用于结构损伤识别的机理的基础上,对一种较好的基于频率的结构损伤识别方法进行了综合评述,并对针对其不足进行了改进.钢筋混凝土三跨连续梁的数值模拟计算表明,中低损伤度的识别结果令人满意.经过改进的方法计算简单,综合识别能力较强,便于工程应用.     

基于移动质量-频率曲线的结构损伤识别方法

针对土木工程中结构实测得到的模态信息少,结构损伤识别方法对噪声敏感且依赖高精确度有限元模型等问题,提出利用移动质量-频率曲线识别结构损伤的方法。首先,在结构上附加移动质量块,获得结构频率关于附加质量位置的关系曲线;然后,建立结构近似有限元模型,通过计算实际模型与近似有限元模型之间的质量-频率曲线的相关性,建立目标函数进行优化;最后,利用变截面简支梁结构进行数值模拟。结果表明:在模型存在较大误差情况下,该方法仍能够准确识别损伤位置,验证其具有较高的适用性。     

基于动力检测的损伤指标评价方法

摘 要：在基于振动测试的损伤识别方法中,如何选择合适的损伤指标是一个值得研究的问题,但目前损伤指标繁多,而且缺乏一个对损伤指标进行系统评价的方法。本文提出了采用损伤指标对刚度变化的灵敏度,对噪声的适应性,通过不完备信息进行正确识别的能力和对损伤位置精确指示的能力即局部性四个指标对损伤指标进行评价的方法,并给出了这四个指标量化公式,较全面地反应了损伤检测和监测在应用中遇到的各种问题,指导了损伤识别指标的选择。通过对简支梁的数值模拟研究显示这种损伤指标评价方法作为一种先验方法得出的结论与对每种损伤指标进行损伤识别得到的结论一致,说明这种评价方法的可靠性。本文提出的损伤指标的评价方法,考虑了实际中的各种主要因素,通过对损伤指标的四个特性的定量刻画使评价变得十分直观有效,对实际工程结构健康监测系统中损伤识别方法的选择具有重要的指导意义。     

基于加速度响应相关性的结构损伤识别方法

通过推导证明了加速度响应的相关特性包含结构的模态信息,与结构损伤的程度和位置相关,提出加速度响应相关特性作为结构损伤因子的损伤识别方法,以该指标作为BP神经网络神经元,判断结构的损伤程度和损伤位置。利用有限元分析证明了该方法的有效性,通过试验研究验证了该方法的适用性。     

基于单元残余能量灵敏度的结构损伤识别

在定义单元残余能量基础上进行单元残余能量的灵敏度分析,提出基于单元残余能量灵敏度分析的结构损伤识别方法,建立结构损伤识别方程组。通过解方程,求得损伤系数向量,便可同时识别结构的损伤位置及损伤程度。仿真结果表明,本文提出的方法对各损伤工况不但能准确定位,而且能有效识别出损伤程度,识别精度较高。     

基于子结构能量的点阵结构损伤诊断方法研究

提出了二阶子结构能量法以检测点阵结构损伤,称为SOSEM (second order substructure energy method)。对于复杂点阵板,在已知损伤位置条件下,该方法只需结构中存在“缺陷”构件的模态信息,就可以准确地得到构件损伤程度,而传统基于模态分析的方法必需整体结构完全的模态信息,新方法提高了检测效率,对实现快速（实时）点阵结构损伤检测具有重要的应用价值。为了验证本文方法的有效性,以点阵板为例,采用数值方法分析了几种典型损伤情况,结果表明,该方法能够准确地检测出点阵结构中一个或多个单元的损伤。     

基于人工神经网络的结构损伤识别方法

根据观测的结构频率变化,建立了基于人工神经网络的悬臂梁结构损伤识别方法.把结构固有频率的变化率作为BP神经网络的输入参数,对悬臂梁结构模型进行了损伤数值模拟计算.为了提高神经网络的泛化推广能力和收敛速度,将BFGS优化方法应用到神经网络的训练过程中.数值计算结果表明,所建立的结构损伤识别方法具有收敛速度快、识别精度高等特性.     

基于损伤因子的板架结构损伤识别方法研究

用基于损伤因子的损伤识别方法对板架结构损伤进行识别。先将板架结构简化为多个板的组合;基于损伤前后模态振型从应变能角度推导损伤因子的表达式,用通用有限元软件ANSYS计算两种损伤工况下损伤前后的模态振型,再用B样条法拟合损伤因子曲面从而对损伤的位置进行识别。最后用实验验证了该方法。该方法对于识别板架结构的单个损伤具有较好的可靠性,并可以为识别多损伤位置提供较好的方法指导。     

Structural damage identification using transfer matrix with lumped crack properties

A novel damage detection scheme is developed for detecting multiple cracks in beams, based on a transfer matrix (TM) approach. Lumped Crack TM of a beam element with multiple cracks is derived based on lumped crack properties. A cracked beam element is assumed as two intact beam elements connected with a hinge or torsional spring. The crack is modelled as an element of zero length and mass, but with elastic properties. Lumped crack approach is simpler for multiple cracks than the possible alternative methods. The state vector at a node includes displacements, forces and moments at that node; when it is multiplied with TM the state vector at the adjacent node can be obtained. The crack identification strategy used here, involves measuring the initial state vector at a node, in the zone of interest. The displacements at the adjacent nodes are measured and these are predicted using TM. Using an optimization algorithm the unknown crack parameters in the TM are solved by minimizing the deviation between measured and predicted displacements. The method is verified using several numerical models as well as experiments with cracked beams. The TM is shown to be suitable for local identification and also fast and accurate compare to other methods.     

输入未知条件下基于自适应广义卡尔曼滤波的结构损伤识别

发展一种输入未知条件下的自适应广义卡尔曼滤波(Adaptive Extended Kalman Filter with Unknown Inputs,AEKF-UI)方法,在线复合反演系统参数与未知输入,结合基于改进粒子群优化算法的自适应技术实现系统时变参数追踪,进而识别结构损伤,包括损伤发生的时间、位置和程度。建立基础隔震结构实验模型及理论模型,其中隔震层的非线性动力学特性通过Bouc-Wen模型描述。对基础隔震结构进行振动实验研究,采用刚度元件装置模拟时间、位置和程度不同的结构损伤,基于测得的加速度响应和AEKF-UI方法进行实时系统参数与未知输入的同步反演。研究结果表明:在两种典型地震波激励下,AEKF-UI方法得到的识别值与参考值相一致,验证了该方法在系统辨识中的有效性和准确性。     

Model-based identification of damage from sparse sensor measurements using Neumann series expansion

This paper presents a novel and effective modal data-based methodology for structural damage localization and quantification when the structure is equipped with a limited number of sensors. Damage detection problem is defined as an inverse model-based problem and a new damage-sensitive cost function is introduced using calculated Generalised Flexibility Matrix and Modal Assurance Criterion. The second-order approximation of Neumann Series Expansion-based Model Reduction approach is employed for numerically simulation of sparse sensor installation. Finally, a hybrid version of two different evolutionary optimization algorithms, named Particle Swarm Optimization–Colonial Competitive Algorithm (PSO–CCA), is suggested and utilized for solving optimization problem. This hybridization, not only can pick the positive points of the PSO and CCA for searching complex solution domain, but also can lead to achieving a powerful, fast speed optimization strategy. The efficiency of the presented method is demonstrated by studying three numerical examples under different damage patterns. Various challenges, such as the robustness of the method in the presence of random noises in the input data, are investigated. The obtained results introduce the presented method as a viable and practical strategy for structural damage identification, especially when a limited number of sensors are installed on the structure.     

Structural damage identification based on modified Artificial Bee Colony algorithm using modal data

A method for structural damage identification based on a modified Artificial Bee Colony algorithm is presented. A new formula is introduced to the onlooker bee phase to improve the convergence rate and the Tournament Selection Strategy is adopted instead of roulette to enhance global search ability of the algorithm. Test functions are introduced as benchmarks to verify the proposed algorithm. And then two numerical examples, including a supported beam and a plate, are conducted to investigate the efficiency and correctness of the proposed method. Final estimated results show that the present technique can produce more accurate damage identification results, comparing with other evolutionary algorithms, even with a few noise contaminated measurements.     

结构物理参数识别的贝叶斯估计马尔可夫蒙特卡罗方法

从结构动力特征方程出发,以结构主模态参数为观测量,推得结构物理参数线性回归模型。对该模型应用贝叶斯估计理论得到物理参数后验联合分布,再结合马尔可夫蒙特卡罗抽样方法给出各个物理参数的边缘概率分布和最优估计值,而提出了基于结构主模态参数的结构物理参数识别贝叶斯估计马尔可夫蒙特卡罗方法。对五层剪切型结构的数值研究表明,此方法能够利用少数主模态参数给出结构质量和刚度参数的概率分布和最优识别值,而且在主模态参数较准确时识别误差很小。     

An iterated IRS technique for cross-sectional damage modelling and identification in beams using limited sensors measurement

This paper presents an effective method for cross-sectional damage localization and quantification in beams. First, a new strategy is suggested for cross-sectional damage modelling by means of Iterated Improved Reduction System (IIRS) approach. Then, a novel damage localization index is proposed employing Grey System Theory (GST) as a geometrical criterion for quantifying the amount of correlation between vectors of the calculated curvatures for the diagonal members of the flexibility matrices in the damaged and undamaged states. Since the method employs only the modal data of the translational degrees of freedom, it can be interpreted as damage identification method by utilizing incomplete modal data or installing a limited number of sensors. After detecting the damage location, to estimate the exact parameters of the cross-sectional damage, the problem is defined as a finite element model-updating problem which is solved with a new evolutionary optimization approach named Imperialist Competitive Algorithm (ICA). The applicability of the method is demonstrated by studying different damage patterns on two numerical examples of beams. In addition, its robustness is investigated in the presence of random noises and modelling errors. Obtained results emphasize the high accuracy and promising performance of the method, especially when noisy incomplete modal data are used.     

基于虚拟变形法的移动质量识别方法研究

在车-桥耦合系统的移动质量(荷载)识别反问题中,识别移动质量会面临重构系统、优化缓慢的问题;而若直接识别移动荷载常常会遇到病态问题且对噪音敏感。针对这些缺陷,根据虚拟变形法(VDM)的结构快速重分析思想,提出移动动态影响矩阵,实现利用较少的传感器即可快速而准确地识别移动质量(荷载)。以移动质量为优化变量,避免了识别荷载常遇到的病态问题,对噪音鲁棒性强;且需要传感器信息少。每步优化中,利用移动动态影响矩阵,无需时时重构车-桥耦合系统的时变系统参数矩阵,优化效率高。VDM方法的思想是将实际结构的响应计算转化为初始结构模型在相同外荷载作用下的响应,与在结构模型发生改变的位置施加相关的虚拟变形或虚拟力引起的响应的线性叠加。通过简支梁模型和框架梁模型验证了该方法的可行性和有效性,即使在5%的噪声影响下,利用一个传感器就可以很好地识别多个移动质量。     

一种基于改进PSO算法的结构损伤识别方法

根据“结构损伤发生在局部”这一信息特征,在粒子群算法中引入零变异率系数,提出一种改进的粒子群结构损伤识别方法。两层刚架单损伤数值仿真研究结果表明：与普通粒子群算法相比,本文所提出的方法具有收敛速度快、识别结果准确、抗噪能力较强及受种群数影响小的特点,这些优点对于将PSO应用于复杂结构的损伤诊断有着重要意义     

分谱处理算法在基于超声导波损伤识别中的应用

利用分谱处理（SSP）算法,通过比较基准波信号和检测波信号的瞬时相位变化度（IPVD）来评估损伤散射的基础阶对称（S0）模式的飞行时间（ToF）,最终实现基于超声导波的损伤识别。首先在理想的工作环境中（无噪声）,对无缺陷的铝板进行检测,并把所采集的波信号作为基准信号。然后在不同的工作环境中（无噪声和有噪声）检测带有切缝缺陷的铝板,并把所采集的波信号作为检测信号。实验结果表明,当检测波信号的信噪比较低时,噪声能量严重干扰了检测波信号的能量分布。然而,SSP算法所提取的检测波信号的IPVD几乎不受噪声的干扰,因此利用SSP算法能够有效地抵抗强噪声的干扰,精确地评估损伤散射的S0模式的ToF,并进一步结合三角定位算法成功地定位出铝板中的切缝缺陷。     

Feasibility and limitations of damage identification in composite materials using acoustic emission

One of the current challenges in health monitoring of composite materials is the use of acoustic emission to identify damage modes. Many classification procedures have been reported in the literature but none of them clearly state limitations to their applicability, making it difficult to quantify them in different testing conditions. In the present paper, a method is described to characterize energy attenuation and how it affects AE signals features based solely on AE signals recorded during mechanical tests. Limitations to damage identification based on AE signals features can therefore be defined. The method is demonstrated on AE signals recorded during tensile tests on four different layups of carbon fiber reinforced polymer composites using signals frequency centroids to describe AE sources.