基于应变能等效指标的结构损伤识别技术研究

为了解决结构的多损伤识别问题,提出了一种基于应变能等效指标的损伤识别方法。首先给出了损伤前后模态应变能变化的表达式以及能量耗散公式,然后根据结构的能量耗散与应变能的变化值等价的原理,建立了一个四阶等效方程,最后求出了该方程的四个根,并通过对该方程四个根的分析得到了一个应变能等效指标,通过该指标可以方便的求解损伤的位置和程度。数值仿真结果表明,基于应变能等效指标的损伤识别方法不仅可以精确的识别出损伤的位置和程度,而且其识别精度明显好于应变能耗散率方法。     

考虑质量变化的刚架结构损伤识别方法研究

结构构件出现缺口损伤时,伴随着结构质量的减小。为了更精确地识别结构损伤,以折减损伤单元截面积来模拟结构损伤,提出考虑结构质量变化的刚架结构损伤识别方法。该方法首先通过相对模态应变能指标对结构损伤进行准确定位;之后考虑梁单元截面特性变化的特点将单元刚度矩阵进行分解,以多个指标描述单元损伤,从而建立结构损伤程度识别方程,并用最小二乘方法对损伤指标进行求解。数值算例结果表明,本文方法能实现对刚架结构损伤位置的准确定位和损伤程度的精确识别,而且具有良好的抗噪性。     

基于应变脉冲响应协方差的损伤识别方法研究

基于振动参数的结构损伤识别,是近年来土木工程的热点研究课题,振动参数包括频率、振型、频响函数、模态应变能、应变响应和加速度响应等,当结构损伤时,损伤位置附近将产生应力重分布,从而引起应变的变化,因此对比损伤前后的应变或者应变响应参数,可以用来识别结构损伤.提出了一种应变脉冲响应协方差参数,它是应变脉冲响应在时间区间上的能量积分;推导并证明了该参数是结构模态参数(频率,位移模态,应变模态,阻尼等)的函数,可用来表征结构状态.相比于传统的模态参数识别方法,可以保留更高阶的模态参数,而且避免了模态识别可能引起的误差;基于简支钢梁的多种损伤工况,研究和展示了该参数的特性,通过数值模拟发现,该参数能简单直观地判定损伤发生和识别损伤位置,无需建立结构分析模型,只需比较结构损伤前后的应变脉冲响应协方差参数即可;该参数简便易算,具有较好的抗噪性能,对结构损伤敏感,而且对结构刚度减少呈现一致变化特性,所以适合实际工程结构的健康监测和损伤识别.     

基于有限测点信息的结构损伤识别柔度法

利用有限测点获得结构的模态参数,提出了基于有限测点的结构损伤识别的柔度法。该方法是通过仅考虑结构柔度的灵敏度分析,以结构各自由度的损伤信息为条件,选择对结构柔度变化敏感的自由度为测点,并利用有限测点的信息提出了结构完备模态振型的重建技术。在此基础上,对柔度矩阵做关于结构物理参数变化量的一阶泰勒展开,来确定结构单元的损伤因子对结构进行损伤识别。从而实现利用结构有限测点的模态信息来识别结构的损伤,解决了测试结构的模态振型的不完整给结构的损伤诊断带来的困难。通过数值算例说明了该方法的有效性。     

基于Mindlin板理论的偏移损伤成像数值仿真研究

提出了一种应用散射Lamb波的偏移技术对板结构中多部位损伤进行实时识别. 基于Mindlin板理论,推导了板结构中弥散性弯曲波频率-波数域的快速偏移方法. 首先对由线性传感器阵列激励和接收到的入射和散射波场在波数-频率域分别进行延拓,然后根据Huygens原理,结合波场延拓的时间一致性原理施加成像条件,对损伤进行成像识别. 数值仿真研究采用基于Mindlin板理论的有限差分法模拟结构中含不同形状及尺寸损伤时的散射波场. 对模拟散射波场进行偏移成像的结果表明该方法不仅能够识别多部位损伤的位置,还具有识别损伤程度的能力,其快速计算的优点满足在线结构健康监测系统对实时性的要求.      

基于改进云推理算法的塔架结构损伤识别

为了解决塔架结构的损伤识别问题,提出了基于应变能和改进云推理算法的损伤识别方法。首先描述了云模型的基本理论和数字特征,并给出了模态应变能的基本公式;然后分析了X条件云发生器和Y条件云发生器的基本算法和运行步骤,借助灰云模型建立相应的前件云和后件云规则,考虑了测量噪声的影响,利用云发生器生成多组云滴,并利用多模式下云滴的确定度和生成值构建了基本云推理算法及其损伤识别指标。基本云推理算法中常会产生不均匀发散的云滴,从而使计算结果产生一定的偏差,为了降低云滴发散产生的偏差影响,提出了基于损伤模式数量加权的云推理改进策略。计算结果表明:云推理算法可以较好地应用于塔架结构的损伤识别,其识别结果明显优于传统的应变能耗散率指标方法;而改进云推理算法进一步提高了识别的精度,优于基本云推理算法。     

时变环境与损伤耦合下桥梁结构频率及阻尼比的统计分析

对时变环境与损伤耦合下我国某斜拉桥的结构频率及阻尼比进行统计分析,以提高结构损伤识别的精度.首先,利用该桥的长期监测数据,采用环境激励技术结合特征系统实现算法识别该桥梁结构的频率及阻尼比;其次,利用人工神经网络算法建立该桥梁结构的环境温度与结构频率及阻尼比的关系模型;然后,通过统计分析,建立完好状态下该桥梁结构模态参数误差因子的概率分布模型;最后,通过分析不同时段与完好结构状态下该桥梁结构模态参数误差因子的相交概率比识别结构损伤,并利用该桥的实测结果验证所提算法的有效性.     

自由度缩聚的柔度损伤诊断法

为了提高结构在模型自由度缩聚情况下的损伤识别结果的精度,本文推导了基于改进Guyan 缩聚法的结构振动方程式．通过求解振动特征方程,利用其特征值和特征向量构建结构缩聚后的柔度矩阵表达式,并引入结构缩聚后的柔度曲率矩阵差和柔度曲率矩阵变化率两个损伤指标,将引入的新损伤指标应用于平面桁架的损伤识别．研究表明：不管是单损伤还是多损伤,仅仅需要一阶模态参数,利用其引入的新损伤指标就可以精确地识别出损伤杆单元位置．即使在高强度噪音的影响下,也保证了其损伤识别结果的精确性．验证了本文基于改进的Guyan 缩聚法推导出的损伤指标具有较好的损伤定位性能和较高的抗噪性能．     

Assessment of structural damage using natural frequency changes

The present paper develops a new method for damage localization and severity estimation based on the employment of modal strain energy. This method is able to determine the damage locations and estimate their severities, requiring only the information about the changes of a few lower natural frequencies. First, a damage quantification method is formulated and iterative approach is adopted for determining the damage extent. Then a damage localization algorithm is proposed, in which a damage indicator is formulated where unity value corresponds to the true damage scenario. Finally, numerical studies and model tests are conducted to demonstrate the effectiveness of the developed algorithm.     

结构损伤诊断的改进灵敏度方法

提出一种改进的灵敏度方法用于工程结构损伤检测中.通过在迭代算法中引入一个“加速”公式来迅速获得足够精确的识别结果,避免了多次迭代,可以大大减少计算花费.用文献[8]和文献[10]中的两个数值算例对所提方法进行了验证,并把结果和原文中的计算结果进行比较.结果表明:采用改进的方法一般只需经过一次计算即可获得精度更高的识别结果,避免了多次迭代,显著减少了计算花费,显示了改进方法突出的优越性.     

Identification of Damping and Dynamic Young’s Modulus of a Structural Adhesive Using Radial Basis Function Neural Networks and Modal Data

In this paper, the radial basis function neural networks (RBFNN) was applied to the problem of identifying dynamic Young’s modulus and damping characteristic of a structural adhesive, using modal data. To identify Young’s modulus from undamped model, an appropriate RBFNN using modal data (mode shape and natural frequency) in each mode is developed. Based on a previous work, in order to identify loss factor, two approaches adopted in the identification process. In the first one, a two stage RBFNN is developed. In stage I, Young’s modulus is identified from undamped model and in stage II using the results of stage I an appropriate RBFNN is developed in each mode for identification of loss factor by implementing real parts of eigenvalues of damped model. In the second approach, a one stage RBFNN is developed using real and imaginary parts of eigenvalues of damped model to identify Young’s moduli and loss factors simultaneously. The repeatability and consistency of the method is proved by repeating the identification process for several times. The validity of results is proved by comparing the results with those identified in a previous work.     

A pseudo-elastic material damage model,part I: Strain energy density criterion

A failure criterion is presented which relates the strain energy density of the material to both yielding and fracture. Cumulative material damage throughout a structural component may be monitored and the relative influence of yielding and stable crack growth assessed. The criterion is demonstrated, using finite element analysis, for center cracked panel specimens differing by material toughness values. From crack growth increment predictions using the uniaxial stress-strain behavior of the material, the criterion predicts the critical value of the strain energy density factor S_c governing crack instability.     

Damage detection method in complicated beams with varying flexural stiffness

A damage detection method for complicated beam-like structures is proposed based on the subsection strain energy method (SSEM), and its applicability condition is introduced. For a beam with the continuously varying flexural stiffness and an edge crack, the SSEM is used to detect the crack location effectively by numerical modal shapes. As a complicated beam, the glass fiber-reinforced composite model of a wind turbine blade is studied based on an experimental modal analysis. The SSEM is used to calculate the damage index from the measured modal parameters and locate the damage position in the blade model successfully. The results indicate that the SSEM based on the modal shapes can be used to detect the damages in complicated beams or beam-like structures for engineering applications.     

Nondestructive evaluation of damage in composite structures using modal parameters

The problem of using measured modal parameters to detect and locate damage in structures made of fiberreinforced composites is investigated. Recent work in this area using modal sensitivity equations is used in conjunction with internal-state variable constitutive theory to derive a set of damage-detection equations which are used to predict, from changes in measured modal parameters, the current value of the internal-state variables in each finite element. The value of the internal-state variable determines the extent of damage at a given location. Numerical examples involving damaged composite beams are used to demonstrate the capability of the theory to predict the exact location and the severity of damage. To provide experimental evidence to support the theory, mechanical and modal tests are performed on a [0,90₃] _s laminated composite beam in the undamaged state and in three additional states of progressive damage. At each stage of damage, edge replications are taken to determine the crack density along the length of the beam. The predicted values of the internal-state variables, obtained from the modalsensitivity equations using measured modal information, are compared with the values of the internal-state variables obtained from crack-density measurements along the length of the beam. Good agreement between the predicted and the measured values is found.     

灰色相关性分析在结构静力损伤识别中的应用

基于灰色理论的相关性分析方法，首次提出了灰色曲率关联系数的概念并将其应用到结构的静力损伤识别中，提出了对局部损伤十分敏感的静态位移曲率置信因子SDCACi，通过该因子的大小对各节点所连接的单元是否会发生损伤进行精确的判断，然后运用最小二乘法对损伤区域的损伤程度进行识别．并将该方法应用于两端固支梁的损伤识别中，由识别结果可以证明：不论测量数据(用有限元仿真计算并考虑了测量误差)的多少，该方法对结构中的单损伤和多损伤都能进行准确的定位，因此该方法在大型结构及复杂结构的损伤识别中具有广阔的应用前景．     

Test analysis of detection of damage to a complicated spatial model structure

A two-stage damage detection approach is proposed and experimentally demonstrated on a complicated spatial model structure with a limited number of measurements. In the experiment,five known damage patterns,including 3 brace damage cases and 2 joint damage cases,were simulated by removing braces and weakening beam-column connections in the structure. The limited acceleration response data generated by hammer impact were used for system identification,and modal parameters were extracted by using the eigensystem realization algorithm. In the first stage,the possible damaged locations are determined by using the damage index and the characteristics of the analytical model itself,and the extent of damage for those substructures identified at stage I is estimated in the second stage by using a second-order eigen-sensitivity approximation method. The main contribution of this paper is to test the two-stage method by using the real dynamic data of a complicated spatial model structure with limited sensors. The analysis results indicate that the two-stage approach is ableto detect the location of both damage cases,only the severity of brace damage cases can be assessed,and the reasonable analytical model is critical for successful damage detection.     

基于振动测试与小波包分析的结构损伤预警

将结构振动测试技术与小波包分析相结合，提出对振动激励信号与响应信号分别 进行小波包分解并在此基础上计算结构的小波包脉冲响应函数及其小波包能量谱，用以表征 结构动力系统的损伤状态. 通过一钢筋混凝土板静力承载力的振动试验分析，计算该板在不 同受力阶段的小波包脉冲响应函数及其小波包能量谱，在此基础上对板不同受力阶段的损伤 状态进行了判别. 该方法克服了结构动力响应的小波包能量谱不能反映结构损伤状态的缺 点，试验表明所采用的方法是可行的.     

Numerical-experimental method for elastic parameters identification of a composite panel

A hybrid numerical-experimental approach to identify elastic modulus of a textile composite panel using vibration test data is proposed and investigated. Homogenization method is adopted to predict the initial values of elastic parameters of the composite, and parameter identification is transformed to an optimization problem in which the objective function is the minimization of the discrepancies between the experimental and numerical modal data. Case study is conducted employing a woven fabric reinforced composite panel. Three parameters (E₁₁, E₂₂, G₁₂) with higher sensitivities are selected to be identified. It is shown that the elastic parameters can be accurately identified from experimental modal data.     

THE DAMAGE LOCALIZATION FOR PLATE STRUCTURES BASED ON MODIFIED MODAL STRAIN ENERGY INDEX

为解决模态应变能方法识别中产生的"邻近效应"问题,提出基于修正模态应变能指标的板结构损伤定位方法.该方法首先利用邻近测点的应变能变化相对大小计算权重系数,再根据权重系数对测点相应区域的应变能进行重新分配.此外,通过定义的损伤辨识度指标研究噪声对损伤定位结果的影响.为验证本文所提方法的可行性和有效性,以一个四边简支板为数值算例.算例结果表明,本文方法对于点状、块状和带状损伤都可以实现准确定位,且具有良好的抗噪性.     

利用振动模态测量值和神经网络方法的结构损伤识别研究

提出了一种基于模态测量参数和神经网络的结构损伤检测方法,建造了两种输入方式的BP神经网络,即自振频率以及结合自振频率与振型,并讨论了不同数量的输入信息对结构损伤检测精度和计算效率的影响。证明了输入的参数越多,神经网络就越聪明,训练的收敛速度越快;以及在保证一定的测量精度的情况下,基于频率与振型的损伤识别结果要好于基于频率的检测结果。最后,通过对3层框架模型的4种损伤工况下的结构损伤检测结果的分析,认为利用模态测量参数和神经网络方法能够准确地识别结构损伤的位置,而且能较精确地识别结构损伤的大小。