Research Institute for Infrastructure Performance, Korea Infrastructure Safety & Technology Corporation, Jinju 52856, Korea.
Department of Civil and Environmental Engineering, Sejong University, Seoul 05006, Korea.
Sensors (Basel). 2019 Nov 21;19(23):5099. doi: 10.3390/s19235099.
By virtue of the advances in sensing techniques, finite element (FE) model updating (FEMU) using static and dynamic data has been recently employed to improve identification on updating parameters. Using heterogeneous data can provide useful information to improve parameter identifiability in FEMU. It is worth noting that the useful information from the heterogeneous data may be diluted in the conventional FEM framework. The conventional FEMU framework in previous studies have used heterogeneous data at once to compute residuals in the objective function, and they are condensed to be a scalar. In this implementation, it should be careful to formulate the objective function with proper weighting factors to consider the scale of measurement and relative significances. Otherwise, the information from heterogeneous data cannot be efficiently utilized. For FEMU of the bridge, parameter compensation may exist due to mutual dependence among updating parameters. This aggravates the parameter identifiability to make the results of the FEMU worse. To address the limitation of the conventional FEMU method, this study proposes a sequential framework for the FEMU of existing bridges. The proposed FEMU method uses two steps to utilize static and dynamic data in a sequential manner. By using them separately, the influence of the parameter compensation can be suppressed. The proposed FEMU method is verified through numerical and experimental study. Through these verifications, the limitation of the conventional FEMU method is investigated in terms of parameter identifiability and predictive performance. The proposed FEMU method shows much smaller variabilities in the updating parameters than the conventional one by providing the better predictions than those of the conventional one in calibration and validation data. Based on numerical and experimental study, the proposed FEMU method can improve the parameter identifiability using the heterogeneous data and it seems to be promising and efficient framework for FEMU of the existing bridge.
凭借传感技术的进步，最近已经使用静态和动态数据来进行有限元（FE）模型更新（FEMU），以提高更新参数的识别能力。使用异构数据可以提供有用信息，以提高 FEMU 中的参数可识别性。值得注意的是，异构数据中的有用信息可能会在传统的 FEM 框架中被稀释。在之前的研究中，传统的 FEMU 框架一次使用异构数据来计算目标函数中的残差，并将其压缩为一个标量。在这种实现方式中，应该小心地用适当的权重因子来构建目标函数，以考虑测量的规模和相对重要性。否则，异构数据的信息将无法得到有效利用。对于桥梁的 FEMU，由于更新参数之间的相互依赖性，可能存在参数补偿。这会加剧参数可识别性，从而使 FEMU 的结果变得更糟。为了解决传统 FEMU 方法的局限性，本研究提出了一种用于现有桥梁的 FEMU 的顺序框架。所提出的 FEMU 方法使用两个步骤，以顺序方式使用静态和动态数据。通过分别使用它们，可以抑制参数补偿的影响。通过数值和实验研究验证了所提出的 FEMU 方法。通过这些验证，从参数可识别性和预测性能方面研究了传统 FEMU 方法的局限性。与传统方法相比，所提出的 FEMU 方法通过提供比传统方法更好的校准和验证数据的预测结果，在更新参数方面显示出更小的可变性。基于数值和实验研究，所提出的 FEMU 方法可以利用异构数据提高参数可识别性，并且似乎是现有桥梁 FEMU 的一种有前途且有效的框架。
