Online selection of the appropriate model and identifying its parameters based on measured vibrational data are among the challenging issues in dynamic system identification. After a severe earthquake, quick monitoring and assessment of structural health status play a crucial role in effective critical risk management for the building owners and decision-makers. The Bayesian multiple modeling approach is a suitable tool for optimal model class selection, which is used in this article mainly for improving data fitting precision, decreasing dimensions of structural unknown vector through removing unnecessary parameters, detecting the occurrence and type of predominant phenomenon related to degradation and pinching, and finally increasing stability and convergence rate of the used identification algorithm. In this study, a reliable and effective time-domain method is proposed for online probabilistic model class selection and joint estimation of structures using the central difference Kalman filter and Bayesian multiple modeling approach. Also, in contrast to existing studies, the performance and efficiency of the proposed algorithm are investigated in nonlinear system identification with a large number of unknowns. The proposed algorithm is implemented on three moment-frame buildings with 8, 54, and 60 unknowns. In addition, a numerical example is provided to analyze the case in which the exact model is not among the plausible models. To verify the performance of the hybrid identification method, robust simulations with synthetic measurement noises and modeling errors were generated using the Monte Carlo random simulation method. The result shows the method can be used to simultaneously select the optimal model class and identify its unknown states and parameters in an online manner.

在线选择合适的模型并基于测得的振动数据识别其参数是动态系统识别中的难题之一。发生严重地震后，对建筑物健康状况的快速监控和评估对于建筑物所有者和决策者的有效重大风险管理至关重要。贝叶斯多重建模方法是用于选择最佳模型类别的合适工具，在本文中主要用于提高数据拟合精度，通过去除不必要的参数来减少结构未知向量的维数，检测与退化相关的主要现象的发生和类型和捏合，最终提高了所用识别算法的稳定性和收敛速度。在这个研究中，提出了一种可靠有效的时域方法，采用中心差分卡尔曼滤波和贝叶斯多重建模方法进行结构在线概率模型选择和联合估计。此外，与现有研究相反，在大量未知数的非线性系统识别中，研究了该算法的性能和效率。该算法在具有8、54和60个未知数的三个矩框架建筑物上实现。另外，提供了一个数值示例来分析确切模型不在合理模型中的情况。为了验证混合识别方法的性能，使用蒙特卡洛随机模拟方法生成了具有综合测量噪声和建模误差的鲁棒模拟。