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Structural Parameter Estimation Using Modal Responses and Utilizing Genetic Algorithm

Genetic Algorithms (GA) are powerful tools for solving large and complicated optimization problems. Objective functions used in parameter estimation (PE) are commonly nonlinear due to available measurements at a limited number of degrees of freedom for a structure. Sparse measurements create a fairly complicated objective function surface that requires a robust algorithm to find its global minimum without converging to a local minimum or diverging. This paper examines the potential of genetic algorithms to find the global minimum associated with modal stiffness and flexibility based objective functions used in PE. A finite element model of a six-story two-parameter shear building is used for this study. Using three-dimensional plots of the two objective functions, few cases with complex surface and several local minima are selected. FlexGA TM genetic algorithm software is then used to estimate parameters of the model. The overall performance of GA to find the global minimum for these cases is compared with gradient-based optimization methods (commonly referred to as hill climbing, HC). It is concluded that GA's performance in locating the basin of the global minimum is superior to HC. It is further recommended that when GA converges, switching to HC yields more accurate parameter estimates.