## 5 Uncertainties Effect Minimization in SHM Systems  
Within the training and operation process of SHM systems, model uncertainties and monitoring system anomalies adversely affect the performance and the damage detection system capability. These uncertainties can be found in the data that are used for training and in the data that are processed during system operation. In the training phase of the machine-learning model, it is common to use synthetic training data generated from FEM models of the structure of interest. The simulation of the structural response facilitates the task of generating data under different operating and structural damage conditions. The initial model of the structure of interest must accurately reflect the structural behavior and response, as it is the starting point for comparison with future states to determine the presence of structural damage. To minimize the uncertainties of the structural FEM model, several solutions have been proposed in recent years to obtain a model with reliable parameters and several solutions have been proposed for the optimal way to update FEM models in the SHM area. [PERSON] and [PERSON] [131] proposed a direct FEM update strategy based on the identification of modal parameters and their comparison with the predicted structural response from measurements on a bridge structure. [PERSON] [132] presented an evolutionary strategy in combination with a preliminary damage detection update triggering process and an ARMA model. An iterative FEM model updating process is used in [PERSON]'s approach [133], where a sensitivity analysis determines the most significant structural parameters that have influence on the overall behavior of the structure. A survey of different FEM model updating methods in the area of SHM can be found in [134]. In the case of bridge-type \begin{table}
\begin{tabular}{c c c} \hline
**Model Approach** & **Advantages** & **Disadvantages** \\ \hline \multirow{8}{*}{Physics-based SHM} & \(\bullet\) & The model parameters have a \\  & straightforward physical interpretation. & \(\bullet\) & The calculations of the solution of the model equations may not be feasible for real-time SHM applications, where the complexity of the structure requires long processing times. \\  & \(\bullet\) & It can reach all the levels of damage \\  & identification if the parameters are defined & \(\bullet\) & The uncertainties and changing parameters may reduce the accuracy of the output model. Therefore, an estimation using other methods, such as model-based techniques, is encouraged. \\ \hline \multirow{8}{*}{Data-based SHM} & \(\bullet\) & The solution process is hidden from the \\  & \(\bullet\) & user, so the rationale for successful damage \\  & \(\bullet\) & classification is not explicit. \\  & \(\bullet\) & The training data set must be large enough \\  & \(\bullet\) & Data collected by the sensors can be \\  & minimized for the classification performed & \(\bullet\) & algorithms such as CNN. In addition, \\  & \(\bullet\) & by the ML model. & obtaining the training samples of damage \\  & \(\bullet\) & Damage identification can be performed \\  & \(\bullet\) & even if the parameters of the structure are \\  & unknown or cannot be estimated. & \(\bullet\) & Computational training times can be costly \\  & & for some ML algorithms (SVM, for \\  & & example). Real-time SHM monitoring \\  & & systems require faster methods such as \\  & & NN solutions. \\ \hline \end{tabular}
\end{table} 
Table 11: Advantages and disadvantages of a physics-based model and data-based models in SHM building applications. structures, an analysis of the different model updating techniques can be found in [PERSON]'s work in [135]. On the other hand, during SHM systems operation it is necessary to identify the impact of environmental and operational effects on structural parameters to minimize their effects on damage-sensitive features, especially in these structures under harsh or extreme conditions.