Finite element model updating using multiobjective optimization technique

Abstract

The analytical predictions from a finite element model often differ from the experimental results of a target structure. Finite element model updating is an inverse problem to identify and correct uncertain modeling parameters that leads to better predictions of the dynamic behavior of the structure. And it is usually posed as an optimization 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 used commonly.

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. Thus, a necessary approach is to solve the same problem repeatedly by varying the values of weighting factors until a satisfactory solution is obtained. But, due to the structural defects of the weighting method, it usually takes very much time to finally obtain satisfactory weights. As a detour, multiobjective optimization technique is introduced in this research. Among various multiobjective optimization methods, an interactive multiobjective optimization technique called satisficing trade-off method is used for its effectiveness.

Also the success of finite element model updating depends heavily on the selection of updating parameters. The updating parameter selection should be made with the aim of correcting uncertainties in the model. Moreover, the criteria or objective functions which designate differences between analytical and experimental re...