## 5 Uncertainties Effect Minimization in SHM Systems
For example, high sensor sensitivity measurements without noise removal techniques may only be permissible when environmental and operational conditions remain unchanged and this scenario is very uncommon in the implementations of SHM systems. [PERSON] and [PERSON] [136] conducted a study to analyze the environmental effects (temperature and humidity) on the modal frequencies of the structure. Through one year of measurements in a 22-story building, [PERSON] identifies a correlation between the mentioned environmental conditions and the modal frequencies of the structure. The normalization of the sensing system data can reduce the effects of environmental noise and sensor failures. Some of these effects may be correlated with damage and should be eliminated to avoid a false-positive error in damage identification. [PERSON] et al. [137] employed cointegration and (PCA) techniques to remove operational and environmental effects and extract damage-sensitive features. [PERSON] et al. [138] proposed an identification method based on sensor measurements with noise to obtain the stiffness and damping matrices of a structure model. A final model including these parameters obtained from the noise measurements predicts building response to seismic excitation. Using SVM, relevant vector machines (RVM) and cointegration techniques, [PERSON] [139] implemented an SHM to monitor the structural condition of a historic building. This SHM system only considers the identified frequencies of the given structure and not the environmental effects of performing structural monitoring. Temperature variation is another environmental factor that can impact the performance of an SHM system and its ability to accurately detect damage, as the sensors can be affected by these variations. [PERSON] et al. [140] proposed a new damage identification system that employs a new algorithm, which includes the effect of temperature variations and their correlation with structural properties. A genetic algorithm (GA) is also used to solve the optimization of the process. Other approaches in the area of unsupervised learning that consider the effect of uncertainties in measured data have been proposed for anomaly detection systems that are resistant to uncertainty in sensor data. [PERSON] et al. [141] studied the use of symmetric Kullback-Leibler (SKL) distance and transmissibility function to perform structural anomaly detection by comparing a baseline state and potential damage scenarios. Bayesian inference and a Monte Carlo discordance test are used as a statistical screening scheme to deal with measurement uncertainties. To improve the probabilistic function of the anomaly detection model, [PERSON] et al. [142]. used the Bhattacharyya distance of TF. The performance of this proposal outperforms other proposals using Mahalanobis distance-based methods and have better resistance against the effects of uncertainties. [PERSON] and [PERSON] [143] presented a Mahalanobis-squared distance-based method that combines the one-class kNN rule and an adaptive distance measure. In this study, the clustering process eliminates the effects of varying environmental conditions on the anomaly detection process.