COMPARISON AND EVALUATION OF ANALYTICAL APPROACHES TO STRUCTURAL DYNAMIC MODEL CORRECTION

Considerable discrepancies exist between predictions from a structural dynamic model and experimental results of a laboratory model or actual structure when the two are compared. Updating the existing dynamic model based on modal test data is very important in order to precisely predict actual behaviors of the structure via the structural dynamic model. This paper reviews the procedures for refining a structural dynamic model from modal test data by different approaches which are used effectively, including the modal sensitivity method, neural networks method and Genetic algorithm, together with the recent research advances in this field. Some problems of dynamic model correction encountered in the actual applications such as incomplete modal test data and robustness of correction, as well as the factors affecting the computational efficiency and solution convergence, are discussed. Merits and defects of these proposed methods are also discussed and some current problems needed to be solved in the future are pointed out by comparison of numerical results of a real 5-story-steel-frame model updating from limited modal test data.