BILPUBLISHERJOURNAL TITLEYEARSUMMARY

ITERATIVE METHOD-iterative methods provide wide choices of updating parameters, structural connectivity can be easily maintained and corrections suggested in the selected parameters can be physically interpreted. -Iterative methods either use eigendata or FRF data.-Iterative methods are based on minimizing an objective function that is generally a nonlinear function of selected updating parameters. - Quite often eigenvalues,eigenvectors or response data are used to construct the objective function

1(Collins et al., 1974)Statistical Identification of Structures1974Objective:-used the eigendata sensitivity for analytical model updating in an iterative framework.

2(Lin & Ewins, 1994)Analytical model improvement using frequency response functions1994Objective:-used measured FRF data to update an analytical model

3(Modak et al., 2002)Comparative study of model updating methods using simulated experimental data2002Objective-Comparison of response function method (RFM) and inverse eigensensitivity method neglecting damping was done with an objective to study the accuracy with which they predicted the corrections required in an FE model

4(Modak et al., 2000)Model updating using constrained optimizationObjective-proposed an updating method in which updating of undamped FE model was done by imposing constraint on natural frequencies and mode shapesNoted:Most of the updating methods neglect the damping. So these methods can be used up to the point of predicting the natural frequencies and real modes. But these cannot be used for complex frequency response functions (FRFs) and complex modes.

5(Arora et al., 2009)Finite element model updating with damping identification2009Objective:-to propose a new procedure for damped model updating(considered on damping) for better FRF matching based on application on damping identification procedure with 2 steps :- Mass and stiffness updated using Response Function Method Damping matrix identified using updated mass and matrices method-Result the damped FE model updating procedure predict accurately the measured FRFs

6(Mehrpouya et al., 2013)FRF based joint dynamics modeling and identification2013Objective:-using two methods for joint identification which are inverse receptance coupling (IRC) method & the point-mass model.Results both model yielded close results in simulation & experiment-But, IRC method have several advantages over point mass model Its explicit closed-form solution (eliminates the necessity of numerical solution in the point-mass model, which suffers from repeatability.) only requires two sets of measurements(pint mass model 3) time taken (faster only take in seconds than hours)

7(Mares et al., 2006)Model Updating Using Bayesian Estimation2006Objective:-to discuss about Markov-Chain Monte Carlo theory (MCMC) and implementing it into model updating in the case of multiple sets of experimental results by using frequency responses functions.-using simulated 3 DOF to illustrate some aspect of the method - assessment of the consequences of wrongly chosen updating parameters, model structure errors, noise effects, observability of the model errors through measurements locations and optimal frequency measurement points for the minimisation of model errors is carried out.-

8(Baaa et al., 2011)A model updating approach based on design points for unknown structural parameters2011Objective:-addressed an updating algorithm to modify the numerical models by using design points for unknown structural properties-to minimize the error by updating uncertain parameters for each mode and combine them to get an optimum solution-formulate objective function as the difference between EMA and analytical frequency and using optimisation method to identify parameters that minimize the function-using the algorithm that used by Zhang & Der Kiureghian to calculate design point and being developed using three steps:1. applying the calculation of the design point algorithm for each mode2. determining the modal participation factors (MPFs)3. the calculation of the uncertain parameters by using design pointand MPFs-the reliability analysis program, FERUM is updated for implicit limit state function using ANSYS finite element program.-the proposed algorithm gives better solutions for model updating compared to the initial values.

9(Jensen et al., 2014)Model reduction techniques for Bayesian finite element model updating using dynamic response data2014Objective-presented a strategy for integrating a class of model reduction into FE model updating formulation in order to reduce computational cost involved in the dynamic re-analyses of large scale linear models including localized nonlinearities-the implementation of the transitional Markov Chain Monte Carlo method in the framework of FE model updating using dynamic response data

NON ITERATIVE (DIRECT)Although these methods are computationally cheaper and reproduce the measured modal data exactly, they violate structural connectivity and updated structural matrices are difficult to interpret

Arora, V., Singh, S., & Kundra, T. (2009). Finite element model updating with damping identification. Journal of sound and vibration, 324(3), 1111-1123. Baaa, H. B., Trker, T., & Bayraktar, A. (2011). A model updating approach based on design points for unknown structural parameters. Applied Mathematical Modelling, 35(12), 5872-5883. Collins, J. D., Hart, G. C., Haselman, T. K., & Kennedy, B. (1974). Statistical Identification of Structures. AIAA Journal, 12(2), 185-190. doi: 10.2514/3.49190Jensen, H., Millas, E., Kusanovic, D., & Papadimitriou, C. (2014). Model-reduction techniques for Bayesian finite element model updating using dynamic response data. Computer Methods in Applied Mechanics and Engineering, 279, 301-324. Lin, R. M., & Ewins, D. J. (1994). Analytical model improvement using frequency response functions. Mechanical Systems and Signal Processing, 8(4), 437-458. doi: http://dx.doi.org/10.1006/mssp.1994.1032Mares, C., Dratz, B., Mottershead, J., & Friswell, M. (2006). Model updating using Bayesian estimation. Paper presented at the International Conference on Noise and Vibration Engineering, ISMA2006, Katholieke Universiteit Leuven.Mehrpouya, M., Graham, E., & Park, S. S. (2013). FRF based joint dynamics modeling and identification. Mechanical Systems and Signal Processing, 39(1), 265-279. Modak, S. V., Kundra, T. K., & Nakra, B. C. (2000). Model updating using constrained optimization. Mechanics Research Communications, 27(5), 543-551. doi: http://dx.doi.org/10.1016/S0093-6413(00)00128-2Modak, S. V., Kundra, T. K., & Nakra, B. C. (2002). Comparative study of model updating methods using simulated experimental data. Computers & Structures, 80(56), 437-447. doi: http://dx.doi.org/10.1016/S0045-7949(02)00017-2