## 2 Materials and Methods
### Finite Element Model Updating  
In order to predict the structural response accurately, the available information should be effectively used such that FEM parameters can be determined to the best extent. In this modeling example, the design drawings and material properties are unavailable, therefore, the initial FEM is based on site observations and estimations. The observations include the length and the outer diameter of structural members by scaling the pixel values with respect to the known dimensions (i.e., window size). Although the outer diameter can be determined using bridge photographs, the cross-sectional thickness or the inner diameter is unknown. Likewise, support restraints are set as uncertain parameters with possible realizations such as fixed, pinned, or roller. Other than these, contribution of the nonstructural components is difficult to estimate, therefore, mass sources are assigned based on crude assumptions. The analysis primarily considers displacement demand in the vertical direction which is influential on glass facade safety, and the paper presents time history analysis results in z-direction. The model consists of 48 nodes and 98 beam-column elements. Figure 5 shows the node and element tags for the baseline model. Figure 6 shows the modeling uncertainties taking place without the necessary documentation. To summarize, tubular structural member section dimensions, distributed mass due to non-structural components, and support restraints all contribute to the modeling uncertainties and will be determined throughout the FEM updating process. The proposed FEM updating method consists of generating a large number of models changing in uncertain parameters, comparing the modal analysis results of each FEM with the experimental data, and selecting the model which minimizes the error between the simulation (model) and the reality  
Figure 4: Exemplary crowdsourced submission time histories and Fourier spectra. Figure 3: Conceptual CPS scheme for smartphone-based SHM. (identification). In order to establish an autonomous parameter study and FEM updating procedure, an OpenSees-Matlab integration loop is pursued. Specifically, OpenSees scripts are simultaneously generated, run, and evaluated by a controller Matlab code. As mentioned previously, three different parameters are selected to create different FEM batches. These are the boundary conditions, element stiffness values, and nodal masses, respectively. For each boundary condition combination changing in fixity definitions, a set of models with varying stiffness and mass values are generated. Each of the model batches are evaluated according to the difference between the first, second, and the third FEM and identification results. This is conducted by developing an objective function quantifying the error between a model and the reference modal parameter values. In the former studies, the authors adopted Least Square Method (LSM) to formulate the objective function [46; 47] whereas different approaches are present in the literature [55; 56], in this study, the objective function is structured in terms of error between the simulation and the experimental results and multiplication of multiple modal parameter errors. To specify, the objective function, which is a function of the support restraints, stiffness and mass distributions, is formulated as  
\[\text{Obj}(\text{BC},\text{K},\text{M})=\prod_{\text{mode}=1}^{3}(\text{f}_{ \text{mode FEM}}-\text{f}_{\text{mode EXP}}|/\text{f}_{\text{mode EXP}})\]  
where BC, K, M represent changing FEM parameters such as boundary condition (BC), member stiffness, and mass values, respectively. Each boundary condition, stiffness, and mass combination corresponds to a different set of first, second, and third modal frequencies represented with \(\text{f}_{\text{mode FEM}}\) term, and the model accuracy is determined based on the deviation from experimental values represented with \(\text{f}_{\text{mode EXP}}\).