标题： 使用贝叶斯方法的有限元模型修正 显示英文标题

标题： Finite Element Model Updating Using Bayesian Approach

摘要： 本文比较了有限元模型更新中的最大似然法和贝叶斯方法。 最大似然法采用遗传算法实现，而贝叶斯方法则采用马尔可夫链蒙特卡罗方法实现。 这些方法在一个简支梁和一个非对称H形结构上进行了测试。 结果显示，与通过最大似然法获得的有限元模型相比，贝叶斯方法得到的更新后的有限元模型预测的模态属性更加准确。 此外，发现这两种方法所需的计算负载相同。 显示英文摘要

摘要： This paper compares the Maximum-likelihood method and Bayesian method for finite element model updating. The Maximum-likelihood method was implemented using genetic algorithm while the Bayesian method was implemented using the Markov Chain Monte Carlo. These methods were tested on a simple beam and an unsymmetrical H-shaped structure. The results show that the Bayesian method gave updated finite element models that predicted more accurate modal properties than the updated finite element models obtained through the use of the Maximum-likelihood method. Furthermore, both these methods were found to require the same levels of computational loads.