Abstract：At first, the linear structural identification model is obtained based on a series of conversions of the dynamic characteristic equation, then the posterior distribution of the model is achieved by using the Bayesian updating theory. Utilize the structural modal parameters, and take their randomness into consideration, the samples of the parameters from the conditional posterior distribution of the linear structural identification model is achieved. During the process, the Gibbs sampling based on the Markov Chain Monte Carlo method is taken. The numerical examples show that the Gibbs sampling can not just identify the damage degree and locations in different ways with little error, but can make a probability measure of the identified values.
刘书奎;吴子燕;张玉兵. 基于Gibbs抽样的马尔科夫蒙特卡罗方法在结构物理参数识别及损伤定位中的研究[J]. , 2011, 30(10): 203-207.
Liu Shu-kui;Wu Zi-yan; Zhang Yu-bing. The identification of physical parameters and damage locations using the Gibbs sampling based on the Markov Chain Monte Carlo method. , 2011, 30(10): 203-207.
2011, Vol. 30 
