Finite element model updating based on eigenvalue and strain energy residuals using multiobjective optimisation technique

The numerical results from a finite element (FE) model often differ from the experimental results of real structures. FE model updating is often required to identify and correct the uncertain parameters of FE model and is usually posed as an optimisation problem. Setting up of an objective function, selecting updating parameters and using robust optimisation algorithm are three crucial steps in FE model updating. In this paper, a multiobjective optimisation technique is used to extremise two objective functions simultaneously which overcomes the difficulty of weighing the individual objective function of more objectives in conventional FE model updating procedure. Eigenfrequency residual and modal strain energy residual are used as two objective functions of the multiobjective optimisation. Only few updating parameters are selected on the basis of the prior knowledge of the dynamic behaviours of the structure and eigenfrequency sensitivity study. The proposed FE model updating procedure is first applied to the simulated simply supported beam. This case study shows that the methodology is robust with an effective detection of assumed damaged elements. The procedure is then successfully applied to the updating of a precast continuous box girder bridge that was tested on field under operational conditions.     

Finite element model updating and structural damage identification using OMAX data

The main limitations in the finite element (FE) model updating technique lie in the ability of the FE model to represent the true behavior of the structure (modelling problem), and in the ability to identify enough modal parameters with sufficient accuracy, especially for large structures that are tested in operational conditions (identification problem). In this paper, the identification problem is solved with an OMAX approach, where an artificial force is used in operational conditions and a structural model is identified that takes both the forced and the ambient excitation into account. From an extensive case study on a real three-span bridge, it is observed that, while updating the FE model using the experimental output-only data yields a good fit, discrepancies show up when the more extensive set of OMAX data is used for validation, or even for updating. It can be concluded that an OMAX approach not only increases the well-posedness of the updating problem, it also allows to detect potential inaccuracies in the FE model.     

基于模态综合技术的结构有限元模型修正

由于结构的动力分析需要大量的计算时间和占用大量的计算机内存,常规的数值迭代计算方法难以实现,提出了基于模态综合技术的模型修正方法。该方法首先得到缩减后结构模型的频率与振型,并将该振型转换为缩减前模型物理坐标下的振型。然后,用缩减后模型的频率和转换后的振型,共同构成模型修正的优化目标函数,进而通过优化求解实现结构的模型修正。该方法既保证了计算精度又提高了模型修正的计算效率,使大型复杂结构的模型修正成为可能。最后,对某吊杆拱桥模型进行了动态测试和模型修正,验证了该算法的有效性。     

Comparative study of damped FE model updating methods

Most of finite element (FE) model updating techniques do not employ damping matrices and hence, cannot be used for accurate prediction of complex frequency response functions (FRFs) and complex mode shapes. In this paper, a detailed comparison of two approaches of obtaining damped FE model updating methods are evaluated with the objective that the FRFs obtained from damped updated FE models is able to predict the measured FRFs accurately. In the first method, damped updating FE model is obtained by complex parameter-based updating procedure, which is a single-step procedure. In the second method, damped updated model is obtained by the FE model updating with damping identification, which is a two-step procedure. In the first step, mass and stiffness matrices are updated and in the second step, damping matrix is identified using updated mass and stiffness matrices, which are obtained in the previous step. The effectiveness of both methods is evaluated by numerical examples as well as by actual experimental data. Firstly, a study is performed using a numerical simulation based on fixed–fixed beam structure with non-proportional viscous damping model. The numerical study is followed by a case involving actual measured data for the case of F-shaped test structure. The updated results have shown that the complex parameter-based FE model updating procedure gives better matching of complex FRFs with the experimental data.     

使用有效模态质量和遗传算法的有限元模型修正

提出将模态频率和有效模态质量构造的残差作为遗传算法的目标函数进行结构动力学有限元模型修正的方法。有效模态质量不但可以为结构动力学响应分析提供一种判断模态贡献程度的方法,而且能够为有限元模型修正提供更多的信息量。介绍了有效模态质量的概念和基于遗传算法的结构动力学模型修正理论,在此基础上采用仿真算例验证了所提出方法的正确性和有效性。仿真结果显示,模型修正后参数最大误差为-0.062%,不管是在修正频段内还是修正频段外,频率和有效模态质量的均方误差都小于0.025%。研究表明,使用有效模态质量和遗传算法的结构动力学有限元模型修正是有效可行的。     

步兵战车车体结构有限元模型修正

针对某型步兵战车整车刚柔耦合发射动力学中柔性车体有限元模型精度低的问题,基于模态试验数据,应用支持向量机响应面模型修正理论对车体结构有限元模型进行了修正。应用ANSYS有限元分析软件对车体结构进行模态分析,提取前6阶模态的固有频率和振型。为验证模型,设计了模态试验方案,实测了车体结构的模态信息。基于有限元模型数据与实测数据的相对误差,采用支持向量机响应面模型修正方法对车体结构弹性模量和密度进行修正。模型确认结果和动力学模型应用结果表明,修正后的车体有限元模型精度有了大幅度提高,能更加真实地反映车体的结构特征,为射击精度分析提供了准确的模型基础。     

Model error correction from truncated modal flexibility sensitivity and generic parameters: Part I—simulation

A great advantage of the flexibility based method is the availability of the large amount of measured information from a few lower modes which can be practically measured. But the modal truncated errors would be significant for a structure with high modal density, and this poses a limitation on the usage of existing flexibility methods. This paper presents the truncated modal flexibility sensitivity with respect to the generic parameters, and a model updating method is proposed based on this sensitivity and incomplete measurement. The loss of contribution from the higher modes to the modal flexibility will not be a source of error in the proposed method. The effect of spatial incompleteness and measurement noise is investigated with numerical studies. The proposed approach is found capable of updating both the systematic model error and local stiffness error separately in a single or two stages under noisy environment.     

基于近似似然的频响函数不确定性模型修正

贝叶斯模型修正框架下，以频响函数作为目标，提出了一种使用近似似然函数的不确定性模型修正方法。相比于模态参数，频响函数包含了结构更加充分的信息，用于结构动力学模型修正时有诸多优点，但现有的不确定性模型修正方法并不能很好地实现将频响函数作为目标进行修正。针对此问题，介绍了频响函数和贝叶斯框架下的不确定性模型修正理论，基于近似贝叶斯计算提出了一种近似似然函数，可适用于频响函数作为目标进行不确定性修正。将提出的似然函数应用到三自由度数值和H型非对称梁的有限元模型修正算例中，并结合DREAM算法对不确定性参数进行识别。研究结果表明：修正后参数的上、下限与目标值相差无几，修正后模型的频响函数与目标值几乎重合，在一定噪声水平下仍具有较好的修正效果，验证了所提方法的有效性。     

DAMAGE IDENTIFICATION USING CHANGES OF EIGENFREQUENCIES AND MODE SHAPES

In the present paper, we describe an approach to identify the location and the extent of the damage introduced into the steel frame, using a two-step procedure. In the first step, the measured dynamic response of the original undamaged structure was used to generate a reference finite element (FE) model of the structure. The selected parameters were identified by means of a mathematical optimisation algorithm (‘updating procedure'), minimising an objective function containing the test/analyses differences of eigenfrequencies and mode shapes. The uncertain model parameters had to be chosen with care in order to retain the physical significance of the updated model. In the next step, the experimental modal data of the damaged structure were used to identify the extent of the damages. This was based on comparing the changes of stiffness parameters identified from the undamaged and the damaged structure. With the identified parameters, the FE model was able to reproduce the experimental data as close as possible and allowed the identification of the extent of the damage.     

An improved updating parameter selection method and finite element model update using multiobjective optimisation technique

Finite element model updating is a procedure to minimise the differences between analytical and experimental results and is usually posed as an optimisation problem. In model updating process, one requires not only satisfactory correlations between analytical and experimental results, but also maintaining physical significance of updated parameters. For this purpose, setting up of an objective function and selecting updating parameters are crucial steps in model updating. These require considerable physical insight and usually trial-and-error approaches are common to use. In conventional model updating procedures, an objective function is set as the weighted sum of the differences between analytical and experimental results. But the selection of the weighting factors is not clear since the relative importance among them is not obvious but specific for each problem. In this work, multiobjective optimisation technique is introduced to extremise several objective terms simultaneously. Also the success of finite element model updating depends heavily on the selection of updating parameters. In order to avoid an ill-conditioned numerical problem, the number of updating parameters should be kept as small as possible. Such parameters should be selected with the aim of correcting modelling errors and modal properties of interest should be sensitive to them. When the selected parameters are inadequate, then the updated model becomes unsatisfactory or unrealistic. An improved method to guide the parameter selection is suggested.     

Studies in dynamic design of drilling machine using updated finite element models

The aim of the present work is to develop updated FE models of a drilling machine using analytical and experimental results. These updated FE models have been used to predict the effect of structural dynamic modifications on vibration characteristics of the drilling machine. Two studies have been carried out on the machine. In the first study, modal tests have been carried out on a drilling machine using instrumented impact hammer. Modal identification has been done using global method of modal identification. For analytical FE modeling of the machine, a computer program has been developed. The results obtained using FEM, have been correlated with the experimental ones using mode shape comparison and MAC values. Analytical FE model has been updated, with the help of a program, which has been developed using direct methods of model updating. In the second study, modal testing has been carried out using random noise generator and modal exciter. Global method has been used for modal identification. Analytical FE modeling has been done using I-DEAS software. Correlation of FE results with the experimental ones has been carried out using FEMtools software. Updating of the analytical FE model has also been done using the above software, based on an indirect technique viz. sensitivity based parameter estimation technique. The updated FE models, obtained from both the studies have been used for structural dynamic modifications (SDM), for the purpose of dynamic design and the results of SDM predictions are seen to be reasonably satisfactory.     

使用应变模态和遗传算法的有限元模型修正方法

提出了采用应变模态置信度为待修正响应特征的有限元模型修正方法。应变模态置信度是评价有限元仿真与试验测试结果相关性的方法,可以为模型修正提供全局的频率误差信息和局部的应变相关性信息。首先,介绍了应变模态和有限元模型修正的相关理论方法;然后,以某航空加筋壁板结构为对象,通过仿真分析和"仿真试验"获得结构的应变模态频率以及对应的应变振型,进一步计算频率误差和应变模态置信度误差;最后,基于两种误差构造模型修正的目标函数,采用遗传算法对目标函数进行优化,修正结构中的待修正参数,并将修正后参数代入模型,验证所提方法的正确性和有效性。结果表明:所采用的方法获得的修正后有限元模型具有复现修正响应特征的能力,并且对于未修正频段内的响应也具有较好的预测能力。     

考虑模型偏差的结构动力学模型修正

针对结构有限元模型修正后仍可能存在模型偏差的问题,提出用待修正参数的不确定性来表征模型偏差的有限元模型修正方法。首先,基于响应面方法识别得到待修正参数的最优值,并通过计算结果与试验结果比较获得模型偏差;然后,基于响应面模型并结合灵敏度分析计算得到模型偏差对待修正参数的影响,从而得到考虑模型偏差后待修正参数的区间;最后,通过一个悬臂梁工程实例的模型修正,验证了笔者所提出方法的可行性。结果表明,考虑模型偏差的修正可以提高模型可靠性。     

Adaptive regularization parameter optimization in output-error-based finite element model updating

In finite element (FE) model updating, regularization methods are required to alter the ill-conditioned system of equations towards a well-conditioned one. The present study addresses the regularization parameter determination when implementing the Tikhonov regularization technique in output-error-based FE model updating. As the output-error-based FE model updating results in a nonlinear least-squares problem which requires iteration for solution, an adaptive strategy that allows varying value of the regularization parameter at different iteration steps is formulated, where the optimal regularization parameter at each iteration step is determined based on the computationally efficient minimum product criterion (MPC). The performance of MPC in output-error-based FE model updating is examined and compared with the commonly used L-curve method (LCM) and the generalized cross validation (GCV) through numerical studies of a truss bridge using noise-free and noise-corrupted modal data. It is shown that MPC is effective and robust in determining the regularization parameter compared with the other two methods, especially when noise-corrupted data are used. The adaptive strategy is more efficient than the fixed strategy that uses a constant value of the regularization parameter throughout the iteration process.     

基于灵敏度分析的刀形天线有限元模型修正

准确可靠的有限元模型是结构动态特性分析、设计改进的基础,文中利用模态试验得到的模态参数对某刀形天线进行有限元模型修正.首先建立天线结构的参数化模型,然后通过灵敏度分析选择合适的设计参数作为后续优化对象,利用计算与试验的模态频率之间的相对误差构造加权的优化目标函数,最后应用1阶优化方法修正结构的有限元模型.修正后有限元模型的模态频率最大相对误差降低至10%以内,模态置信度(MAC)均大于0.8.该修正模型可用于后续的动力学分析.     

Model selection in finite element model updating using the Bayesian evidence statistic

This paper considers the problem of finite element model (FEM) updating in the context of model selection. The FEM updating problem arises from the need to update the initial FE model that does not match the measured real system outputs. This inverse system identification-problem is made even more complex by the uncertainties in modeling some of the structural parameters. Such uncertainty often results in a number of competing forms of FE models being proposed which leads to lack of consensus in the field. A model can be formulated in a number of ways; by the number, the location and the form of the updating parameters. We propose the use of a Bayesian evidence statistic to help decide on the best model from any given set of models. This statistic uses the recently developed stochastic nested sampling algorithm whose by-product is the posterior samples of the updated model parameters. Two examples of real structures are each modeled by a number of competing finite element models. The individual model evidences are compared using the Bayes factor, which is the ratio of evidences. Jeffrey's scale is then used to determine the significance of the model differences obtained through the Bayes factor.     

建立斜拉桥基准有限元模型的新方法与实现

在进行斜拉桥监测与检测研究时,为了建立准确而可靠的基准有限元模型,使其索力、位移等趋于监测或检测结果,需进行模型修正工作,但各种模型修正方法多数需要进行迭代运算,不仅计算工作量巨大,而且有时难以实现预期目标。针对这一问题,提出了一种使用Ansys与Matlab软件,利用影响矩阵和优化算法相结合进行模型修正的新方法。该方法不需要迭代,可获得索力、位移等参数,且与实测值相吻合。通过实例对该方法进行了验证,证明了该方法可行并易于实现。     

基于矩阵逼近的模型修正方法的研究

提出一种新的以试验振动和参数辨识的数据为参数,进行有限元分析模型修正的方法。该方法基于矩阵最佳逼近理论,运用Ｂａｙｅｓ估计原理来处理试验结果误差带来的试验模态可信度问题,求取分析模对试验获得的不完备模态的谱点的最佳逼近结果,最后获得质量阵的最小修正模型。     

Updating non-linear components

The objective of the present investigations was updating of finite element (FE) models with local non-linearities, such as Coulomb friction, gaps, local plasticity. Parameters of non-linear elements in the input file of a FE code are updated by fitting simulated time history functions and the corresponding measurement data. The problem of estimating the initial values as well as the problem of increasing error between simulated and measured time history functions have been overcome by using the method of 'modal state observers'. State observers are known in control theory but are a new approach for FE analysis.The presented methods use least square algorithms with analytically and numerically calculated sensitivity matrices for the updating process. A program for updating on principle any parameter of the input file of a standard FE code is described. The only requirement is, that the parameters should have a significant influence on the measured time history function. All of the presented methods have been validated against test results.     

Model error correction from truncated modal flexibility sensitivity and generic parameters. II: experimental verification

This paper reports on the experimental verification of the truncated modal flexibility sensitivity based model updating method proposed in the companion paper (Wu and Law, Mechanical System and Signal Processing, doi: 10.1016/S0888-3270 (03) 00094-3) with real measurements on a three-dimensional cantilever frame structure in the laboratory. Two schemes of model error correction are presented to improve the initial inaccurate finite-element model of the structure. The first procedure updates the initial model in two stages. The systematic model errors are firstly updated using macrogeneric parameters. The local errors are then improved using elemental eigenparameters. The second procedure improves both the systematic and local errors without a measured baseline reference simultaneously. The first procedure can differentiate the types of errors in the structure while the latter procedure removes the usual requirement in most existing model based updating methods that an accurate finite -element model should be used as reference.