【摘要】 通过对结构动力特征方程进行的一系列变化,得到了线性结构识别模型,并由贝叶斯更新理论得到其后验分布形式。利用结构的模态参数,并考虑其随机性,应用基于Gibbs抽样的马尔科夫蒙特卡罗方法对线性结构识别模型中各参数的条件后验分布进行了抽样,成功地实现了结构物理参数识别及损伤定位。数值算例表明:Gibbs抽样结果可以以不同的方式标识结构的损伤程度及位置且识别的误差较小。更多还原

【Abstract】 At first,A linear structural identification model was obtained based on a series of conversion of dynamic characteristic equations.Then,the posterior distribution of the model was achieved by using Bayesian updating theory.Utilizing the structural modal parameters,and taking their randomness into consideration,the samples of the parameters from the conditional posterior distribution of the linear structural identification model were achieved.During the process,Markov chain Monte Carlo method based on Gibbs sampling was used.The numerical examples showed that Gibbs sampling can not only identify damage level and locations in different ways with less error,but also can make a probability measurement of the identified values.更多还原