This paper proposes a new stochastic model updating method based on the homotopy surrogate model (HSM) and Bayesian sampling. As a novel intrusive surrogate model, the HSM is established by the homotopy stochastic finite element (FE) method. Then combining the advanced delayed‐rejection adaptive Metropolis–Hastings sampling technology with HSM, the structural FE model can be updated by uncertain measurement modal data. The numerical results show that the updating effectiveness of the proposed method is better than that of the Bayesian methods with the non‐intrusive surrogate models, such as stochastic response surface model and Kriging model. Compared to the Bayesian method with the intrusive second‐order perturbation model, the updating results of the proposed method are more accurate, especially when the fluctuation of the uncertain measured data is large and the stiffness of the structure significantly changes. The model updating results of a cable‐stayed bridge show that the statistic modal properties of the updated bridge model have a very good agreement with the uncertain measurement modal data.

中文翻译：

---

一种用于随机模型更新的侵入式同伦代理模型的高效贝叶斯方法

本文提出了一种基于同伦代理模型（HSM）和贝叶斯采样的随机模型更新方法。 HSM作为一种新颖的侵入式代理模型，是通过同伦随机有限元（FE）方法建立的。然后将先进的延迟拒绝自适应Metropolis-Hastings采样技术与HSM相结合，可以通过不确定的测量模态数据更新结构有限元模型。数值结果表明，该方法的更新效果优于随机响应面模型和克里金模型等非侵入性代理模型的贝叶斯方法。与侵入式二阶扰动模型的贝叶斯方法相比，该方法的更新结果更加准确，特别是当不确定的测量数据波动较大且结构刚度发生显着变化时。斜拉桥的模型更新结果表明，更新后的桥梁模型的统计模态特性与不确定测量模态数据具有很好的一致性。