Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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In this paper, a new method is proposed for the identification of inelastic shear frame structures with hysteresis, using data collected on their dynamic response. It uses the Prandtl-Ishlinskii rate independent model for hysteresis. The proposed identification method is capable of identifying the mass, damping and restoring force of a frame structure, which can be used in forming the equations of motion of the frame. By solving the equations of motion, the dynamic response is predicted. The method is based on the combined using of the Quadratic Programming (QP) and Genetic Algorithms (GA). First, assuming a set of Prandtl-Ishlinskii constants, the QP is used to find the best frame parameters which can be used in its equations of motion to predict its dynamic response with the least error compared to the real data collected on its dynamic response, while the GA is used to find the best Prandtl-Ishlinskii constants for more reduction in the error. To show the capability of the proposed method in this paper, a single degree of freedom (SDOF) frame with bilinear nonlinearity is utilized for identification, and the results show the high capability of the method.