Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Structural inverse analysis by hybrid simplex artificial bee colony algorithms
Computers and Structures
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In the realm of finite element (FE) model updating and damage identification, an outstanding issue is with the limited amount of reliable response data that may be used to perform an inverse procedure. This problem can restrict the number or types of physical parameters that may be identified or updated, and it could also result in an erroneous identification of the parameters due to insufficient sensitivity of the data set. To tackle this problem, an effective enlargement of the data set is desired. This paper presents a genetic algorithm (GA)-based methodology to make effective use of the artificial boundary condition (ABC) frequencies for FE model updating. The ABC frequencies can be obtained through the measurement of the incomplete frequency response functions of the structural system with a limited number of sensors, and thus they can be of similar measurement accuracy as the natural frequencies. In the present methodology, a binary coding GA is proposed for the selection of the desired artificial boundary conditions; while for the actual updating of the FE model, a procedure based on a real coding GA is implemented. Numerical examples are provided to demonstrate the effectiveness of the proposed approach in the FE model updating.