Correction Algorithm Based On Immune Particle Swarm Optimization For Dynamic Structural Model

In dynamic analysis of structure, the predictions from a dynamic structure model are often different to the measure data. In order to precisely predict the dynamic response of the structure, updating the structure model is necessary. Dynamic model updating came out in the fifties of last century. By several decades developing, now there are many methods about dynamic mode updating, but none of them is the recognized and effective in engineering.Base on the studying of dynamic model updating, this thesis put forward the method of using the immune particle swarm optimization algorithm in the dynamic model updating. The immune particle swarm optimization is combined by the particle swarm optimization algorithm and immune algorithm. It has many virtues such as rapidly convergence rate, and it is not easy to get into local optimum. Now, it is a new hot spot of intelligence algorithm researching.This thesis use the structural model updating based on immune particle swarm optimization in two structures. The first structure is a five floors steel frame, the other is Humen suspension bridge. And the thesis holds that the effect of the model updating is better by comparing the calculated modal data after model updating with before. Compare this thesis's result with other papers', this thesis's result is better.Moreover from discussing the algorithm's characteristic by the analysis of updating process, find that the function of algorithm, such as the diversity of immune system, self-regulation and immune memory are all playing effectively. Finally, a suit size of particle swarm will exert the immune characteristic easily.