Structural damage identification based on Bayesian theory and improved immune genetic algorithm

摘要

In order to solve structural multi-damage identification problem, a two-stage damage detection method based on Bayesian theory and immune genetic algorithm (IGA) is presented. First, structural modal strain energy and frequency are considered as two kinds of information sources, and Bayesian theory is utilized to integrate the two information sources and preliminarily identify structural damage locations. After the damaged locations are determined, immune genetic algorithm is used to identify structural damage extent. Considering the search efficiency of the simple IGA is still not very good, some improved strategies are presented, such as culture vaccine, concentration control of the best antibody, and two termination conditions etc. Simulation results show that the two-stage method can precisely identify structural damage locations and extent, and the calculated results of the proposed improved IGA are obviously better than those of both the simple IGA and the genetic algorithm with elitist strategy.