Structural Damage Detection Based On Particle Swarm Optimization

Under changeable loads in bad environmental working conditions and some accident effects, structures suffer various damages during their lifetime. The damages are not easily to be found first, accumulated stage by stage and even cause the whole structure to be destroyed at last. Thus the research on structural damage detection has become a hot topic nowadays and become an active branch of civil engineering. Being funded jointly by the Innovative Funding of the Ministry of Water Resources of China (No: SCX2003-18) and the National Natural Science Foundation of China (No. 50378041), several key points are proposed in this thesis. First, some disadvantages of the conventional particle swarm optimization (PSO) algorithm are determined, and then an improved particle swarm optimization (IPSO) algorithm is proposed, finally a comparative study on both PSO and IPSO algorithm has been conducted for the structural damage detection. First, in this thesis the methods of structural damage detection which have been developed during last decades are summarized. It can be seen that the structural damage detection often can be converted into the constrained optimization problem. The background, the basic theory and the improvement of the PSO algorithm are discussed, especially for the premature convergence problem. And then an IPSO algorithm is proposed, aiming to increase the diversity of the particles during the last phase of PSO, thereby to avoid local minimum to some extend. A suit of five benchmark functions are employed to evaluate the IPSO algorithm in comparison with the conventional PSO. The experimental results show that the IPSO algorithm is superior to the conventional PSO algorithm. Both the conventional PSO and the IPSO algorithm are applied to structural damage detection. The numerical simulations, which are classified into single and multi-damage with several cases respectively of a 2-rigid frame structure, show that the PSO algorithm is valid for structural damage detection and the IPSO algorithm is more effective than the conventional PSO algorithm. Three different objective functions which comprising of frequency and incomplete model shapes are carried out for evaluation of the incompletion of model shapes. The illustrated results show that the objective function with coordinate modal assurance criterion is effective. This method doesn't need the reduction of the finite elemental model and the model shape expansion. A 3-story experimental building model is carried out for evaluation of the PSO and IPSO algorithm, and the experimental results show that the PSO algorithm is promising. Finally, several key points are pointed out in this thesis.