Journal articles: 'Model updating method'

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Yin, Hong, Jingjing Ma, Kangli Dong, Zhenrui Peng, Pan Cui, and Chenghao Yang. "Model Updating Method Based on Kriging Model for Structural Dynamics." Shock and Vibration 2019 (April 23, 2019): 1–12. http://dx.doi.org/10.1155/2019/8086024.

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Model updating in structural dynamics has attracted much attention in recent decades. And high computational cost is frequently encountered during model updating. Surrogate model has attracted considerable attention for saving computational cost in finite element model updating (FEMU). In this study, a model updating method using frequency response function (FRF) based on Kriging model is proposed. The optimal excitation point is selected by using modal participation criterion. Initial sample points are chosen via design of experiment (DOE), and Kriging model is built using the corresponding acceleration frequency response functions. Then, Kriging model is improved via new sample points using mean square error (MSE) criterion and is used to replace the finite element model to participate in optimization. Cuckoo algorithm is used to obtain the updating parameters, where the objective function with the minimum frequency response deviation is constructed. And the proposed method is applied to a plane truss model FEMU, and the results are compared with those by the second-order response surface model (RSM) and the radial basis function model (RBF). The analysis results showed that the proposed method has good accuracy and high computational efficiency; errors of updating parameters are less than 0.2%; damage identification is with high precision. After updating, the curves of real and imaginary parts of acceleration FRF are in good agreement with the real ones.

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Rui, Qiang, and Hong Yan Wang. "SRSM-Based Stochastic Model Updating Method." Advanced Materials Research 774-776 (September 2013): 12–16. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.12.

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With the combination of stochastic response surface model (SRSM) and Monte Carlo simulation (MCS)-based inverse error propagation method, a computational efficiency stochastic model updating approach has been proposed. The SRSM of original finite element model is determined using the Hermite polynomial chaos expansion and regression-based efficient collocation method. The efficiency of this method is demonstrated as a large number of computational demanding full model simulations are no longer essential, and instead, the updating of parameter mean values and variances is implemented on explicit SRSM. The effectiveness of this approach is validated through a bolt-jointed double-hat structure numerical example. The proposed method can be applied on stochastic uncertainty quantification of complex engineering structures.

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Hu, Sau-Lon James, Huajun Li, and Shuqing Wang. "Cross-model cross-mode method for model updating." Mechanical Systems and Signal Processing 21, no. 4 (2007): 1690–703. http://dx.doi.org/10.1016/j.ymssp.2006.07.012.

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Nobari, Ali Salehzadeh, and Mohammad Ali Farjoo. "Comparison Between Dedicated Model Updating Methods and Hybrid Method." Journal of Aircraft 41, no. 5 (2004): 999–1004. http://dx.doi.org/10.2514/1.260.

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Krasnorutskiy, D. A., P. A. Lakiza, V. A. Berns, and E. P. Zhukov. "Finite Element Model Updating Method of Dynamic Systems." PNRPU Mechanics Bulletin, no. 3 (December 15, 2021): 84–95. http://dx.doi.org/10.15593/perm.mech/2021.3.08.

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The finite element model updating method of dynamical systems based on results of modal tests is proposed. The purpose of updating is to change eigenspectrum. The method alters a stiffness matrix by adding an updating finite element model created on the nodes of the intial one with respect to the existing links between the linear degrees of freedom. The stiffnesses of the updating elements are utilized as the updating parameters to be defined. The objective function equals to the least square weighted sum of residuals between the target, which were determined experimentally, and current values of modal stiffnesses. The iterative solution process is carried out. At each iteration step the conjugate gradient method is applied to solve the unconstrained minimization problem. The modeshapes, which were calculated as the result of solving the generalized eigenvalue problem at the previous iteration step, are employed to calculate the current modal stiffnesses. The method does not have a limit to a size of matrices and keeps their sparsity and symmetry. It provides the model updating of selected regions of a structure and step-by-step model updating of predefined groups of eigenfrequencies. Moreover, geometrical features of a structure, such as the presence of the symmetry planes and structurally identical elements, may be taken into account. The method is implemented into a program and verified by the example of the free dynamically-scaled model of Tu-204. In order to perform the ground vibration testing, the model was suspended with a low-rigidity flexible support. The finite element model made of solid elements has been updated on the basis of the six experimentally determined sets of eigenfrequencies. The target frequencies from each set have been achieved with a high level of accuracy.

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Sanko, Nobuhiro. "Updating function model: Model updating method transferable in a wider range of data sizes." Asian Transport Studies 8 (2022): 100071. http://dx.doi.org/10.1016/j.eastsj.2022.100071.

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Yang, J., H. F. Lam, and J. Hu. "Ambient Vibration Test, Modal Identification and Structural Model Updating Following Bayesian Framework." International Journal of Structural Stability and Dynamics 15, no. 07 (2015): 1540024. http://dx.doi.org/10.1142/s0219455415400246.

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Structural health monitoring (SHM) of civil engineering structures based on vibration data includes three main components: ambient vibration test, modal identification and model updating. This paper discussed these three components in detail and proposes a general framework of SHM for practical application. First, a fast Bayesian modal identification method based on Fast Fourier Transform (FFT) is introduced for efficiently extracting modal parameters together with the corresponding uncertainties from ambient vibration data. A recently developed Bayesian model updating method using Markov chain Monte Carlo simulation (MCMCS) is then discussed. To illustrate the performance of the proposed modal identification and model updating methods, a scale-down transmission tower is investigated. Ambient vibration test is conducted on the target structure to obtain modal parameters. By using the measured modal parameters, model updating is carried out. The MCMC-based Bayesian model updating method can efficiently evaluate the posterior marginal PDFs of the uncertain parameters without calculating high-dimension numerical integration, which provides posterior uncertainties for the target systems.

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Li, Zhi Gang, Ying Chao Li, Shu Qing Wang, and Bin Yang. "Finite Element Model Updating of a Steel Jacket Scale Model." Applied Mechanics and Materials 166-169 (May 2012): 588–92. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.588.

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In this paper, the finite element model of a steel jacket scale model is updated using modal parameters identified by modal test. Updating parameters are selected based on sensitivity analysis by solving modal energies. And then, a two-steps updating process is carried out using different parameters and the Cross-Model Cross-Mode (CMCM) model updating method is applied in each step. Results indicate that with selection of updating parameters and sensitivity analysis, CMCM method can update the finite element model with physical meanings.

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Wei, Chunjie, and Jian Wang. "The Modified Increment Method for Eigenspace Model." Academic Journal of Applied Mathematical Sciences, no. 74 (August 13, 2021): 187–91. http://dx.doi.org/10.32861/ajams.74.187.191.

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Eigenspace is a convenient way to represent sets of observations with widespread applications, so it is necessary to accurately calculate the eigenspace of data. With the advent of the era of big data, the increasing and updating of data bring great challenges to the solution of eigenspace. Hall, et al. [1], proposed that the incremental method could update the eigenspace of data online, which reduces computational costs and storage space. In this paper, the updating coefficient of the sample covariance matrix in an incremental method is modified. Numerical analysis shows that the modified updating form has better performance.

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Zhang, De-Wen, and Lingmi Zhang. "Matrix transformation method for updating dynamic model." AIAA Journal 30, no. 5 (1992): 1440–43. http://dx.doi.org/10.2514/3.11083.

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Kim, Nam-Sik. "Numerical Model Updating Based on Univariate Search Method for High Speed Railway Bridges." Journal of the Korean Society of Civil Engineers 34, no. 1 (2014): 17. http://dx.doi.org/10.12652/ksce.2014.34.1.0017.

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Wang, De Jun, De Min Feng, and Bao Jin. "Proposed and Method Presentation of Bridge Model Updating." Key Engineering Materials 619 (July 2014): 11–17. http://dx.doi.org/10.4028/www.scientific.net/kem.619.11.

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This article briefly reviews the two methods of finite element model (FEM) updating, such as direct matrix methods and the sensitivity-based model updating methods. In addition, the problem in bridge structure model updating often needs to solve large-scale ill-posed linear systems. Therefore, two regularization methods of Tikhonov and TSVD were introduced. Meanwhile, for these systems, it is proposed that the application of the two kinds of regularization method to solve the problem which the test data contaminated by noise may rarely lead to a physically meaningful updated model.

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Davis, Sharon E., Robert A. Greevy, Christopher Fonnesbeck, Thomas A. Lasko, Colin G. Walsh, and Michael E. Matheny. "A nonparametric updating method to correct clinical prediction model drift." Journal of the American Medical Informatics Association 26, no. 12 (2019): 1448–57. http://dx.doi.org/10.1093/jamia/ocz127.

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Abstract Objective Clinical prediction models require updating as performance deteriorates over time. We developed a testing procedure to select updating methods that minimizes overfitting, incorporates uncertainty associated with updating sample sizes, and is applicable to both parametric and nonparametric models. Materials and Methods We describe a procedure to select an updating method for dichotomous outcome models by balancing simplicity against accuracy. We illustrate the test’s properties on simulated scenarios of population shift and 2 models based on Department of Veterans Affairs inpatient admissions. Results In simulations, the test generally recommended no update under no population shift, no update or modest recalibration under case mix shifts, intercept correction under changing outcome rates, and refitting under shifted predictor-outcome associations. The recommended updates provided superior or similar calibration to that achieved with more complex updating. In the case study, however, small update sets lead the test to recommend simpler updates than may have been ideal based on subsequent performance. Discussion Our test’s recommendations highlighted the benefits of simple updating as opposed to systematic refitting in response to performance drift. The complexity of recommended updating methods reflected sample size and magnitude of performance drift, as anticipated. The case study highlights the conservative nature of our test. Conclusions This new test supports data-driven updating of models developed with both biostatistical and machine learning approaches, promoting the transportability and maintenance of a wide array of clinical prediction models and, in turn, a variety of applications relying on modern prediction tools.

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Wu, Jie, Fan Cheng, Chao Zou, et al. "Swarm Intelligent Optimization Conjunction with Kriging Model for Bridge Structure Finite Element Model Updating." Buildings 12, no. 5 (2022): 504. http://dx.doi.org/10.3390/buildings12050504.

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For the simple bridge structure, the finite element model established by drawing and elastic mechanics method is accurate. However, when faced with large and complex long-span bridge structures, there are inevitable differences between the finite element model and the physical model, where the model has to be updated. It is problematic that the updating structural matrix cannot be fed back into the existing general finite element calculation software in the traditional structural matrix updating method. In this paper, a parameter-type updating method based on the “Kriging model + swarm intelligence” optimization is proposed. The Kriging model, based on Genetic Algorithm (GA), Bird Mating Optimizer (BMO), and Particle Swarm Optimization algorithm (PSO), is introduced into the finite element model, updating this to correct the design parameters of the finite element model. Firstly, a truss structure was used to verify the effectiveness of the proposed optimization method, and then a cable-stayed bridge was taken as an example. Three methods were used to update the finite element model of the bridge, and the results of the three optimization algorithms were compared and analyzed. The results show that, compared with the other two methods, the GA-based model updating method has the least time due to the small computation. The results of the BMO-based model were time consuming compared to the other two algorithms, and the parameter identification results were better than the GA algorithm. The PSO algorithm-based model updating method to solve the finite element model was repeated, which required a large amount of computation and was more time consuming; however, it had the highest parameter correction accuracy.

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Li, Wei-ming, and Jia-zhen Hong. "An improved model updating method of reduced-models for finite element model." Journal of Shanghai Jiaotong University (Science) 15, no. 3 (2010): 377–84. http://dx.doi.org/10.1007/s12204-010-1020-4.

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Wu, Xinhai, Huan He, Yang Liu, and Guoping Chen. "Model Updating for Systems with General Proportional Damping Using Complex FRFs." Shock and Vibration 2020 (November 19, 2020): 1–15. http://dx.doi.org/10.1155/2020/8886082.

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In this paper, we propose a model updating method for systems with nonviscous proportional damping. In comparison to the traditional viscous damping model, the introduction of nonviscous damping will not only reduce the vibration of the system but also change the resonance frequencies. Therefore, most of the existing updating methods cannot be directly applied to systems with nonviscous damping. In many works, for simplicity, the Rayleigh damping model has been applied in the model updating procedure. However, the assumption of Rayleigh damping may result in large errors of damping for higher modes. To capture the variation of modal damping ratio with frequency in a more general way, the diagonal elements of the modal damping matrix and relaxation parameter are updated to characterize the damping energy dissipation of the structure by the proposed method. Spatial and modal incompleteness are both discussed for the updating procedure. Numerical simulations and experimental examples are adopted to validate the effectiveness of the proposed method. The results show that the systems with general proportional damping can be predicted more accurately by the proposed updating method.

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Sun, Rui, Ricardo Perera, Enrique Sevillano, and Jintao Gu. "Parameter Identification of Composite Materials Based on Spectral Model by Using Model Updating Method." International Journal of Polymer Science 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/7310846.

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A model updating approach based on a spectral element model and solved with a particle swarm optimization (PSO) method is proposed to identify the vibration-damping properties of composite materials. In comparison with conventional finite element model updating, a composite beam is modeled in a unified way by using a spectral approach whose computational cost is significantly reduced due to its simplicity. In this way, the dynamic response can be captured accurately by using a very limited number of elements. To identify the material properties, experimental tests are carried out to get the initial parameters that are introduced to initialize the spectral model; then, a model updating process solved with a PSO algorithm is implemented to obtain the real material parameters. It has been demonstrated that the proposed spectral model is a potential tool for model updating and parameter identification.

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Liu, Chunqing, Fengliang Zhang, Yanchun Ni, et al. "Efficient Model Updating of a Prefabricated Tall Building by a DNN Method." Sensors 24, no. 17 (2024): 5557. http://dx.doi.org/10.3390/s24175557.

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The significance of model updating methods is becoming increasingly evident as the demand for greater precision in numerical models rises. In recent years, with the advancement of deep learning technology, model updating methods based on various deep learning algorithms have begun to emerge. These methods tend to be complicated in terms of methodological architectures and mathematical processes. This paper introduces an innovative model updating approach using a deep learning model: the deep neural network (DNN). This approach diverges from conventional methods by streamlining the process, directly utilizing the results of modal analysis and numerical model simulations as deep learning input, bypassing any additional complex mathematical calculations. Moreover, with a minimalist neural network architecture, a model updating method has been developed that achieves both accuracy and efficiency. This distinctive application of DNN has seldom been applied previously to model updating. Furthermore, this research investigates the impact of prefabricated partition walls on the overall stiffness of buildings, a field that has received limited attention in the previous studies. The main finding was that the deep neural network method achieved a Modal Assurance Criterion (MAC) value exceeding 0.99 for model updating in the minimally disturbed 1st and 2nd order modes when compared to actual measurements. Additionally, it was discovered that prefabricated partitions exhibited a stiffness ratio of about 0.2–0.3 compared to shear walls of the same material and thickness, emphasizing their role in structural behavior.

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Tu, Fei. "Application of Sliding Smoothing Method Denoising in Model Updating Damage Identification." Journal of Physics: Conference Series 2185, no. 1 (2022): 012014. http://dx.doi.org/10.1088/1742-6596/2185/1/012014.

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Abstract The accuracy of finite element model updating for structural damage identification is easily affected by the noise in the measured modal, so the sliding smoothing method is introduced to reduce the impact of noise. In each iteration step, the sliding smoothing method denoising is applied to the difference between measured modal and simulated modal from the finite element model because the time-frequency characteristic of actual modal is different from noise modal. The modal parameters after denoising are used to construct objective function for modal updating, and the optimal question is solved to determine the stiffness in finite element model. Finally, the stiffness change can indicate the damage position and magnitude. The numerical analysis shows the proposed method can improve the robustness of finite element model updating for the structural damage identification.

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Wu, Jie, Quansheng Yan, Shiping Huang, Chao Zou, Jintu Zhong, and Weifeng Wang. "Finite Element Model Updating in Bridge Structures Using Kriging Model and Latin Hypercube Sampling Method." Advances in Civil Engineering 2018 (December 11, 2018): 1–11. http://dx.doi.org/10.1155/2018/8980756.

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Computational cost reduction and best model updating method seeking are the key issues during model updating for different kinds of bridges. This paper presents a combined method, Kriging model and Latin hypercube sampling method, for finite element (FE) model updating. For FE model updating, the Kriging model is serving as a surrogate model, and it is a linear unbiased minimum variance estimation to the known data in a region which have similar features. To predict the relationship between the structural parameters and responses, samples are preselected, and then Latin hypercube sampling (LHS) method is applied. To verify the proposed algorithm, a truss bridge and an arch bridge are analyzed. Compared to the predicted results obtained by using a genetic algorithm, the proposed method can reduce the computational time without losing the accuracy.

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Wei, Sha, Yifeng Chen, Hu Ding, and Liqun Chen. "An improved interval model updating method via adaptive Kriging models." Applied Mathematics and Mechanics 45, no. 3 (2024): 497–514. http://dx.doi.org/10.1007/s10483-024-3093-7.

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Sun, Hang, and Yang Liu. "An Improved Taguchi Method and its Application in Finite Element Model Updating of Bridges." Key Engineering Materials 456 (December 2010): 51–65. http://dx.doi.org/10.4028/www.scientific.net/kem.456.51.

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In the model updating process, the objective function is usually set as the weighted sum of the difference between analytical and experimental dynamic characteristics. But it is difficult to select the weighting factors since the relative importance of each parameter to updated results is not obvious but specific for different problem. To overcome this problem, multi-objective genetic algorithm (GA) is introduced into model updating by Gyeong-Ho Kim since there is no need for selecting weighting values in multi-objective optimization technique. To complex structures, however, it is difficult to update the structural models by GA because of the relative low efficiency. While Taguchi updating method, deemed as an efficient and robust method, is a good choice to update the models of large structures. But Taguchi method is only applied to solve the single objective optimization problem of model updating. Therefore, this paper proposed improved Taguchi updating method to deal with the problem of model updating using multi-objective optimization technique. Then the proposed method is applied to update the model of a 14-bay beam with measured frequencies and modal shapes. The updated results show that the proposed method is promising to structural model updating.

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Wang, De Jun, and Yang Liu. "A Meta-Modeling Procedure for Updating the Finite Element Model of an Arch Bridge Model." Key Engineering Materials 540 (January 2013): 79–86. http://dx.doi.org/10.4028/www.scientific.net/kem.540.79.

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Finite element (FE) model updating of structures using vibration test data has received considerable attentions in recent years due to its crucial role in fields ranging from establishing a reality-consistent structural model for dynamic analysis and control, to providing baseline model for damage identification in structural health monitoring. Model updating is to correct the analytical finite element model using test data to produce a refined one that better predict the dynamic behavior of structure. However, for real complex structures, conventional updating methods is difficult to be utilized to update the FE model of structures due to the heavy computational burden for the dynamic analysis. Meta-model is an effective surrogate model for dynamic analysis of large-scale structures. An updating method based on the combination between meta-model and component mode synthesis (CMS) is proposed to improve the efficiency of model updating of large-scale structures. The effectiveness of the proposed method is then validated by updating a scaled suspender arch bridge model using the simulated data.

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Hao, Xiang Wei, and Yang Liu. "Updating the Finite Element Model of a Bridge Model Using a Hybrid Optimization Method." Key Engineering Materials 456 (December 2010): 37–50. http://dx.doi.org/10.4028/www.scientific.net/kem.456.37.

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Finite element model updating of structures usually ends up with a nonlinear optimization problem. An efficient optimization technique is proposed firstly, which draws together the global searching capability of chaos-based optimization technique and high searching efficiency of trust-region Newton method. This hybrid approach is demonstrated to be more efficient and prone to global minimum than conventional gradient search methods and random search methods by testifying with three test functions. The optimization problem for model updating using modal frequencies and modal shapes is formulated, and a procedure to update the boundary support parameters is presented. A modal test was conducted on a beam structure, and the identified mode frequencies are employed to formulate the optimization problem with the support parameters as the updating parameters. The discrepancy between the mode frequencies of the finite element models before and after updating is greatly reduced, and the updated support condition meet quite well with the insight to the devices that form the supports.

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Chen, Hui, Bin Huang, Kong Fah Tee, and Bo Lu. "A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique." Sensors 21, no. 9 (2021): 3290. http://dx.doi.org/10.3390/s21093290.

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This paper proposes a new stochastic model updating method to update structural models based on the improved cross-model cross-mode (ICMCM) technique. This new method combines the stochastic hybrid perturbation-Galerkin method with the ICMCM method to solve the model updating problems with limited measurement data and uncertain measurement errors. First, using the ICMCM technique, a new stochastic model updating equation with an updated coefficient vector is established by considering the uncertain measured modal data. Then, the stochastic model updating equation is solved by the stochastic hybrid perturbation-Galerkin method so as to obtain the random updated coefficient vector. Following that, the statistical characteristics of the updated coefficients can be determined. Numerical results of a continuous beam show that the proposed method can effectively cope with relatively large uncertainty in measured data, and the computational efficiency of this new method is several orders of magnitude higher than that of the Monte Carlo simulation method. When considering the rank deficiency, the proposed stochastic ICMCM method can achieve more accurate updating results compared with the cross-model cross-mode (CMCM) method. An experimental example shows that the new method can effectively update the structural stiffness and mass, and the statistics of the frequencies of the updated model are consistent with the measured results, which ensures that the updated coefficients are of practical significance.

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Ratcliffe, M. J., and N. A. J. Lieven. "An improved method for parameter selection in finite element model updating." Aeronautical Journal 102, no. 1016 (1998): 321–29. http://dx.doi.org/10.1017/s0001924000027548.

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AbstractConventional wisdom states that coordinate complete experimental response data are required to effect a successful model update. This paper examines the influence measured rotational degrees of freedom have on the behaviour of the frequency response function (FRF) sensitivity updating procedure. Three finite element (FE) model-based case studies are undertaken which suggest that very little improvement is afforded by the inclusion of measured rotations in the updating process.Error-location strategies in the past have yielded little qualitative information about the optimum selection of updating parameters in the model updating procedure. Two further cases are investigated; these suggest that no amount of coordinate completeness will rectify the debilitating effects of an inadequate p-value choice. A method of selecting the number and location of updating parameters — based on the FRF sensitivity method — is presented. This method is shown to yield promising results.

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Heo, Gwang Hee, Joon Ryong Jeon, Chin Ok Lee, Gui Lee, and Woo Sang Lee. "FE Model Updating for Health Monitoring of Structures and its Experimental Verification by Damage Detection." Key Engineering Materials 321-323 (October 2006): 268–72. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.268.

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This paper presents an effective method of FE model updating for health monitoring of structures by applying ambient vibration. And this method is experimented through damage detection and proved to be valid. Experiment about ambient vibration is performed on cantilever beam, and the dynamic characteristics are analyzed by NExT and ERA. The results of such experiments are compared to those of FE analysis, and this comparison enables us to overcome some errors in experiments and analysis. On the basis of improved results by the comparison, model updating is performed in order to construct a basic structure for health monitoring. For model updating, we employ direct matrix updating method (DMUM) and Error matrix method (EMM) in which ambient vibration is easily applied. The model updating by the methods are again evaluated in terms of error ratio of natural frequency, comparing each result before and after updating. Finally, we perform experiments on damage detection to verify the method of updating presented here, and evaluate its performance by eigen-parameter change method. The evaluation proves that the method of FE model updating using ambient vibration is effective for health monitoring of structure, and some further application of this method is suggested.

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Kumar Bagha, Ashok, Prashant Gupta, and Varun Panwar. "Finite element model updating of a composite material beam using direct updating method." Materials Today: Proceedings 27 (2020): 1947–50. http://dx.doi.org/10.1016/j.matpr.2019.09.024.

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Yuan, Ji. "Automatic update method of GIS platform drawing model based on machine learning." Journal of Computational Methods in Sciences and Engineering 22, no. 2 (2022): 425–35. http://dx.doi.org/10.3233/jcm-215735.

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Aiming at the problem that the number of data bytes in the traditional automatic update technology of GIS platform is small, a method of automatic update of GIS platform graph model based on machine learning is studied. Firstly, the data of the GIS platform model is convolved by the iso-linear feature detection operator in the automatic updating technology of the GIS platform model, and the calculated data of the GIS platform model is expressed as spatial data. A reasonable updating criterion is established, the spatial relationship of GSI data is reconstructed by the measure of updating criterion, the data vector of GIS platform model updated within the updating time range is calculated, and the regional data elements in the space are constantly changed to complete the data updating of GIS platform model. The experimental results show that compared with the automatic updating method of GIS platform model, the proposed method can update more data bytes with the same number of data bytes.

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Li, Wei-Ming, and Jia-Zhen Hong. "New iterative method for model updating based on model reduction." Mechanical Systems and Signal Processing 25, no. 1 (2011): 180–92. http://dx.doi.org/10.1016/j.ymssp.2010.07.009.

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Li, Kaiyang, Jie Fang, Bing Sun, Yi Li, and Guobiao Cai. "Structural Dynamic Model Updating with Automatic Mode Identification Using Particle Swarm Optimization." Applied Sciences 12, no. 18 (2022): 8958. http://dx.doi.org/10.3390/app12188958.

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Dynamic model-updating methods are a useful tool for obtaining high-precision finite element (FE) models. However, when using such methods to update a model, there will be problems with incompleteness and mode switching. To overcome these problems, this paper proposes a structural dynamic model-updating with an automatic mode-identification method. In this method, a mode-identification index is established based on image-similarity recognition to identify the consistency between FE and experimental mode shapes, and particle swarm optimization is introduced to update the model. In addition, to reduce the computational time, Latin hypercube sampling is employed to perform probability statistics of the switching range of the concerned mode orders, and the orders of mode identification are reduced according to the statistics results. In this paper, the proposed method was validated by model-updating of a square plate. The natural frequencies and mode shapes of the plate were obtained by experimental modal analysis and used as the updating objectives of the FE model. In addition, the boundary condition of the plate was simplified by a series of springs, which were used as updating parameters along with material properties and dimensions. Finally, the FE model of the plate was updated by the present method, and the results indicate that the objective function error of the updated FE model was successfully reduced from 14.31% to 1.05%, which proves that the proposed model-updating method is effective and feasible.

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Xu, He Long, Jun Xiao, and Yu Xin Zhang. "Dynamical Model Updating Based on Gradient Regularization Method." Applied Mechanics and Materials 351-352 (August 2013): 118–21. http://dx.doi.org/10.4028/www.scientific.net/amm.351-352.118.

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Modulus of elasticity is an important input parameter in all kinds of structural analyses. The mathematical model used to identify the structural elastic modulus with measured Frequencies and mode shapes at several points is thusly built up in this paper, and then Gradient-Regularization method, an inverse problem solution method, is employed to solve the problem. General finite element program is compiled, and numerical examples have proved that the method of this thesis is efficient. The issues such as the choice of model error and the choice of measuring points are discussed as well.

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Tian, Wei, Shun Weng, and Yong Xia. "Model updating of nonlinear structures using substructuring method." Journal of Sound and Vibration 521 (March 2022): 116719. http://dx.doi.org/10.1016/j.jsv.2021.116719.

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Gabriele, S. "The interval intersection method for FE model updating." Journal of Physics: Conference Series 305 (July 19, 2011): 012091. http://dx.doi.org/10.1088/1742-6596/305/1/012091.

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Weng, Shun, Yong Xia, Xiao-Qing Zhou, You-Lin Xu, and Hong-Ping Zhu. "Inverse substructure method for model updating of structures." Journal of Sound and Vibration 331, no. 25 (2012): 5449–68. http://dx.doi.org/10.1016/j.jsv.2012.07.011.

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Kwon, Kye-Si, and Rong-Ming Lin. "Frequency selection method for FRF-based model updating." Journal of Sound and Vibration 278, no. 1-2 (2004): 285–306. http://dx.doi.org/10.1016/j.jsv.2003.10.003.

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Kwon, Kye-Si, and Rong-Ming Lin. "Robust finite element model updating using Taguchi method." Journal of Sound and Vibration 280, no. 1-2 (2005): 77–99. http://dx.doi.org/10.1016/j.jsv.2003.12.013.

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Yuan, Yongxin. "A model updating method for undamped structural systems." Journal of Computational and Applied Mathematics 219, no. 1 (2008): 294–301. http://dx.doi.org/10.1016/j.cam.2007.07.025.

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Liu, Hao, and Yongxin Yuan. "New model updating method for damped structural systems." Computers & Mathematics with Applications 57, no. 5 (2009): 685–90. http://dx.doi.org/10.1016/j.camwa.2008.10.069.

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Halevi,, Yoram, Rachel Kenigsbuch,, and Izhak Bucher,. "Model Updating: A Combined Reference Basis - Sensitivity Method." Journal of the Mechanical Behavior of Materials 14, no. 6 (2003): 355–68. http://dx.doi.org/10.1515/jmbm.2003.14.6.355.

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Shaoqing, Mao. "Analysis of Structural System Dynamic Model Updating Method." International Journal of Advanced Pervasive and Ubiquitous Computing 5, no. 3 (2013): 66–74. http://dx.doi.org/10.4018/ijapuc.2013070107.

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Model updating methods are successful in use of simulated data without noise, but inevitably lead to damaged uncorrelated noise levels and real experimental data, and it is often unpredictable. In this paper, the use of the frequency response function associated with re-analysis and updating of dynamic systems are proposed. Transformation matrix complexity and the normal frequency response function relationship between the structures are obtained. The transformation matrix is used to calculate the modified damping matrix of the system. The modified mass and stiffness matrices by using the least squares method is used to determine the frequency response function from the normal. A numerical example is tested to illustrate the applicability of the proposed method. The results show that this method is effective.

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42

Dong, Bo, Yan Yu, and Dan Dan Tian. "Alternating projection method for sparse model updating problems." Journal of Engineering Mathematics 93, no. 1 (2015): 159–73. http://dx.doi.org/10.1007/s10665-014-9751-0.

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Xiao, Tian Yin, Jian Gang Han, and Hong Bo Gao. "Finite Element Model Updating of Space Grid Structures." Advanced Materials Research 243-249 (May 2011): 116–19. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.116.

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The aim of updating models is to generate improved numerical models which may be applied in order to predict actual dynamic behaviors of the structure. The approach of numerical predictions to the behavior of a physical system is limited by the assumptions used in the development of the mathematical model. Model updating is about correcting invalid assumptions by processing vibration test results. Updating by improving the physical meaning of the model requires the application of considerable physical insight in the choice of parameters to update and the arrangement of constraints, force inputs and response measurements in the vibration test. The choice of updating parameters is the most important and the numerical predictions should be sensitive to small changes in the parameters. So methods used in model updating places a demand that the mass, stiffness and damping terms should be based on physically meaningful parameters. Using the structure frequency and local modal shape acquired from structural time-history responses, a model updating method of space grid structures was established in this paper. A numerical example is provided to prove the accuracy of this method. The results show that the method can be effectively used to correct the finite element model of space grid structures.

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Ettefagh, Mir M., Hossein Akbari, Keivan Asadi, and Farshid Abbasi. "New structural damage-identification method using modal updating and model reduction." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, no. 6 (2014): 1041–59. http://dx.doi.org/10.1177/0954406214542966.

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Early prediction of damages using vibration signal is essential in avoiding the failure in structures. Among different damage-detection approaches, the finite-element model updating and modal analysis-based methods are of most importance due to their applicability and feasibility. Owing to some restrictions in nodal measurements in experimental cases, finite-element model reduction is an indispensable part of fault-detection methods. Even though model reduction of dynamic systems leads to the less complicated models, an improved convergence rate and acceptable accuracy are highly required for a successful structural health monitoring of the real complex systems. In this paper, the aim is to design a damage-detection algorithm based on a new model updating method, which has a faster rate of convergence and higher accuracy. Then the proposed method is applied on a simulated damaged beam considering different noise levels to see how capable the method is in dealing with noise-corrupted data. Finally, the experimentally extracted data from a cracked beam in a real noisy condition are used to evaluate the efficiency of the proposed method in identifying the damages in a beam-like structure. It is concluded that the identification of the damages by the proposed method is encouraging and robust to the noise compared with the traditional method. Also, the proposed method converges faster and is more accurate in identifying damage than the traditional method.

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Wu, Xueqian, and Yunfeng Dong. "Hierarchical Model Updating Method for Vector Electric-Propulsion Satellites." Applied Sciences 13, no. 8 (2023): 4980. http://dx.doi.org/10.3390/app13084980.

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Electric propulsion is of great significance to the development of high-efficiency and long-life satellites, and digital twins have gradually become a powerful tool for satellite engineering. Being affected by uncertainty factors such as the complexity and variability of the space environment and the satellite system, the digital twin model cannot accurately reflect the real physical properties. Therefore, it is crucial to update the satellite model to improve prediction accuracy. However, the complex structure and multi-physics process coupling of vector electric-propulsion satellites bring great challenges to model updating. According to the characteristics of the vector electric-propulsion satellite, this paper establishes mathematical models of the whole satellite. Additionally, a hierarchical model updating method is proposed and applied to the model updating case of a satellite with multiple subsystems. The simulation results show that the method is suitable for the model updating of the vector electric-propulsion satellite. Through multiple iterations of closed-loop cycles, the residual errors between the simulation values and the telemetry values can be decreased, and the errors between the estimated values and the true values of state variables can also be decreased by an order of magnitude.

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Xia, Zhiyuan, Aiqun Li, Jianhui Li, and Maojun Duan. "COMPARISON OF HYBRID METHODS WITH DIFFERENT META MODEL USED IN BRIDGE MODEL-UPDATING." Baltic Journal of Road and Bridge Engineering 12, no. 3 (2017): 193–202. http://dx.doi.org/10.3846/bjrbe.2017.24.

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Two hybrid model updating methods by integration of Gaussian mutation particle swarm optimization method, Latin Hypercube Sampling technique and meta models of Kriging and Back-Propagation Neural Network respectively were proposed, and the methods make the convergence speed of the model updating process faster and the Finite Element Model more adequate. Through the application of the hybrid methods to model updating process of a self-anchored suspension bridge in-service with extra-width, which showed great necessity considering the ambient vibration test results, the comparison of the two proposed methods was made. The results indicate that frequency differences between test and modified model were narrowed compared to results between test and original model after model updating using both methods as all the values are less than 6%, which is 25%−40% initially. Furthermore, the Model Assurance Criteria increase a little illustrating that more agreeable mode shapes are obtained as all of the Model Assurance Criteria are over 0.86. The particular advancements indicate that a relatively more adequate Finite Element Model is yielded with high efficiency without losing accuracy by both methods. However, the comparison among the two hybrid methods shows that the one with Back-Propagation Neural Network meta model is better than the one with Kriging meta model as the frequency differences of the former are mostly under 5%, but the latter ones are not. Furthermore, the former has higher efficiency than the other as the convergence speed of the former is faster. Thus, the hybrid method, within Gaussian mutation particle swarm optimization method and Back-Propagation Neural Network meta model, is more suitable for model updating of engineering applications with large-scale, multi-dimensional parameter structures involving implicit performance functions.

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Zhang, Bao Qiang, Guo Ping Chen, and Qin Tao Guo. "Finite Element Model Updating for Unsymmetrical Damping System with Genetic Algorithm." Applied Mechanics and Materials 166-169 (May 2012): 2999–3003. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.2999.

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Finite element model updating using incomplete complex modal data for unsymmetrical damping system with genetic algorithm is presented. The genetic algorithm method and finite element model updating based on optimization method using complex modal eigenvalue are introduced. The updating for simulation example about a flexible rotor system which is a typical unsymmetrical damping system is performed using bearing stiffness, bearing damping and diameter moment of inertia parameters. The results show that the maximum error of updated parameters is 0.15% and the objective function of genetic algorithm is 0.0081. The study demonstrates that the finite element model updating method using incomplete complex modal data with genetic algorithm is feasible and effective for unsymmetrical damping system.

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Yao, Dong Sheng, and Li Bin Zhao. "Scheme of Model Updating and Implement for Structural Dynamics Analysis." Applied Mechanics and Materials 252 (December 2012): 140–43. http://dx.doi.org/10.4028/www.scientific.net/amm.252.140.

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Model updating techniques are used to modify structural model for more accurate predictions of dynamics behavior. A simple survey on the model updating methods and correlation criteria is presented. Based on the inverse eigensensitivity method (IESM) and modal assurance criterion (MAC), a scheme of model updating for structures is presented and realized by user defined subroutine combined with APDL in commercial software ANSYS®. A four-DOF spring-mass system is assumed and updated, from which the predicted frequencies and MAC values are satisfied compared to the actual dynamics characteristics. This gives evidence that the presented model updating scheme is feasible and efficient. Furthermore, a cylindrical shell structure containing global and local modal information is established to research the updating ability of the scheme on some focused local modal information. The results due to the updated model of cylindrical shell structure show that not only the global modal data but also the local modal data have a good agreement with that of the actual structure.

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Cheng, Xiao-Xiang. "Model Updating for a Continuous Concrete Girder Bridge Using Data from Construction Monitoring." Applied Sciences 13, no. 6 (2023): 3422. http://dx.doi.org/10.3390/app13063422.

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Finite element (FE) model updating can guarantee the accuracy of the numerical analyses for civil structures. However, two deficiencies currently exist with the traditional FE model updating technique based on the measurements of modal parameters and/or the static structural responses of the built structure with respect to its reference information insufficiency and its non-unique solution generally obtained, hampering its extensive use. It becomes the goal of the whole engineering community to introduce new effective methods for the civil structural FE model update. To this end, an innovative FE model updating method using data from construction monitoring is proposed in this article. With regard to its theoretical novelty, the new method transforms the complicated multi-variable optimization mathematical problem with the traditional FE model updating technique into many simple single-variable parameter identification problems. Under the engineering background of Huangsha Harbor Bridge, a three-span concrete continuous box girder bridge constructed utilizing the symmetric cantilever casting method, the effectiveness and the efficiency of the new model updating practice were validated. It is demonstrated using quantitative data that the abundant data measured on Huangsha Harbor Bridge in construction stages can enhance the reference information for the more accurate FE model updating of the structure, and the uncertain parameters with the initial FE model of Huangsha Harbor Bridge can be progressively and easily identified for the proposed model updating method using many single-variable linear regression models, instead of one complicated multi-variable mathematical or numerical model employed by the traditional model updating approaches, which generally leads to non-unique solutions rendered by normal optimization algorithms.

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Chen, Zhe, and Qijun Zhao. "A Dynamic Model Updating Method with Thermal Effects Based on Improved Support Vector Regression." Applied Sciences 11, no. 17 (2021): 8025. http://dx.doi.org/10.3390/app11178025.

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The dynamic modeling of structures in a thermal environment has become a new research topic in structural dynamics. The amount of calculation caused by the complexity of the structure and the size of the FEM, which increase the difficulty in modeling the structural dynamic thermal effects are considered. In this study, model updating in thermal temperature environment is proposed based on the hierarchical method and improved SVR, and an iterative procedure is presented. First, the dynamic problem of structure under a thermal environment is classified into a thermal model and a structural dynamic model, and they are both constructed with the FE method. As a result, the model updating process is conducted for both the thermal model and structural dynamic model. Different from the variables in other model updating methods, the updating variables, which are composed of the mechanical characteristics and thermal parameters, in the proposed method are dominated by the temperature distribution of the structure. A surrogate model based on improved SVR is adopted in the hierarchical model updating approach to make the updating process more efficient. Finally, the improved SVR method is validated on a typical nonlinear function, and the proposed method is validated by updating the model of an elastic thin plate and a wing structure in a thermal environment.

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Journal articles on the topic 'Model updating method'

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