Bayesian model updating facilitates the calibration of analytical models based on observations and the quantification of uncertainties in model parameters such as stiffness and mass. This process significantly enhances damage assessment and response predictions in existing civil structures. Predominantly, current methods employ modal properties identified from acceleration measurements to evaluate the likelihood of the model parameters. This modal analysis-based likelihood generally involves a prior assumption regarding the mass parameters. In civil structures, accurate determination of mass parameters proves challenging owing to the significant uncertainty and time-varying nature of imposed loads. The resulting inaccuracy potentially introduces biases while estimating the stiffness parameters, which affects the assessment of structural response and associated damage. Addressing this issue, the present study introduces a stress-resultant-based approach for Bayesian model updating independent of mass assumptions. This approach utilizes system identification on strain and acceleration measurements to establish the relationship between nodal displacements and elemental stress resultants. Employing static analysis to depict this relationship aids in assessing the likelihood of stiffness parameters. Integrating this static-analysis-based likelihood with a modal-analysis-based likelihood facilitates the simultaneous estimation of mass and stiffness parameters. The proposed approach was validated using numerical examples on a planar frame and experimental studies on a full-scale moment-resisting steel frame structure.

翻译：贝叶斯模型更新基于观测数据校准分析模型，并量化刚度、质量等模型参数的不确定性。该过程显著提升了既有土木结构的损伤评估与响应预测能力。目前主流方法利用加速度测量识别的模态特性来评估模型参数的似然度。这种基于模态分析的似然度评估通常需要预先对质量参数进行假设。在土木结构中，由于外荷载具有显著的不确定性和时变性，质量参数的精确测定极具挑战。由此产生的误差可能在估计刚度参数时引入偏差，进而影响结构响应及相关损伤的评估。针对此问题，本研究提出一种基于应力合力的贝叶斯模型更新方法，该方法无需质量假设。该途径通过对应变与加速度测量值进行系统辨识，建立节点位移与单元应力合力之间的关系。利用静力分析描述该关系有助于评估刚度参数的似然度。将此基于静力分析的似然度与基于模态分析的似然度相结合，可实现质量与刚度参数的同时估计。通过平面框架数值算例及足尺抗弯钢框架结构试验研究，验证了所提方法的有效性。