A Bayesian model updating method using subset simulation optimization given ambient vibration data is proposed to improve the efficiency in sampling and avoid local optimums, which is applied to a full-scale high-rise structure. In the proposed method, the Bayesian fast Fourier transform method is used to extract the most probable values (MPVs) and coefficients of variation (COVs) of modal parameters of the structure, which are considered as the weighting factors in the likelihood function. The posterior probability density function (PDF) of the updating parameters is then derived considering the prior PDF as the regularization term. The subset simulation optimization algorithm is extended to find the global optimal solution of the posterior PDF. The MPVs and COVs of the updating parameters are finally presented. The proposed method is first verified by updating the 15 story stiffness scaling factors of a shear building model. Then, the proposed method is investigated through a numerical application of a four-story frame structure, in which accurate parameter estimations are observed independent of the prior PDF. Finally, the proposed method is applied to a real-world 13-story twin-tower masonry structure which is a modern heritage building. Ambient vibrations were measured through a series of accelerometers instrumented in the structure. The updating parameters are the moduli of elasticity of selected substructures with two model classes studied: three parameters and six parameters. The model-predicted modal parameters from the updated model are in good agreement with their identified counterparts. It is also shown that the case using three parameters provides larger evidence based on the Bayesian model class selection method. The updated refined finite element model provides a basis for the long-term structural health monitoring.

提出了一种基于子集模拟优化的贝叶斯模型更新方法，该方法在给定环境振动数据的情况下提高了采样效率，避免了局部最优，将其应用于大型高层建筑。在该方法中，使用贝叶斯快速傅立叶变换方法来提取结构的模态参数的最可能值（MPV）和变异系数（COV），它们被视为似然函数中的加权因子。然后，将先前的PDF作为正则项，得出更新参数的后验概率密度函数（PDF）。扩展了子集仿真优化算法，以找到后PDF的全局最优解。最后给出了更新参数的MPV和COV。首先通过更新剪切建筑模型的15个层的刚度比例因子来验证所提出的方法。然后，通过四层框架结构的数值应用研究了所提出的方法，其中独立于先前的PDF观察到准确的参数估计。最后，将所提出的方法应用于现实世界中的13层双塔砌体结构，这是一栋现代文物建筑。通过一系列安装在结构中的加速度计测量环境振动。更新参数是所选子结构的弹性模量，其中研究了两个模型类别：三个参数和六个参数。来自更新模型的模型预测模态参数与其确定的对应物非常吻合。还表明，基于贝叶斯模型类别选择方法，使用三个参数的情况提供了更大的证据。更新后的改进的有限元模型为长期结构健康监测提供了基础。