## 3 Application of Drive-by Methodologies on the Bridge Subsystem
### Bridge Damage Identification
#### 3.2.3 Machine Learning Methodologies
Two approaches are usually mentioned in the literature and in the practice of feature normalization: (i) input-output, based on linear or nonlinear regression models or (ii) output-only, based on latent variable techniques such as Principal Component Analysis(PCA) [81]. The former removes the effects of EOVs, establishing relationships between measured structural responses and measured actions (e.g., traffic, speed, temperature). When monitoring systems do not include sensors to measure EOVs, latent variable methods can be applied. This approach can reduce the influence of independent actions using only structural measurements. Due to the need of suppressing nonlinear effects induced by EOVs, models such as auto-encoder have recently been implemented with high accuracy with regard to feature normalization [82; 83]. Data fusion aims to reduce the volume of data while maintaining their most important information. This procedure may merge features from a single sensor, features from spatially distributed sensors or even heterogeneous data types. In all approaches, the goal of data fusion is to achieve a new type of information with less volume and a similar or greater ability to define the measured phenomena when compared to that reached when using any of the original information sources alone. The Mahalanobis Distance has been widely used in this context due to its ability to represent the variability in multivariate data sets [84]. [PERSON] et al. [57] show that the Mahalanobis Distance is generic enough to be used to identify any damage while granting a weighting that is completely unsupervised and thus independent of the type of structure, the actions imposed on it, and the human intervention. It consists of a weighted damage indicator in which the weights are determined  
Figure 14: AR features obtained from the simulated accelerations responses in a mid-span section of a concrete slab: (**a**) undamaged condition, (**b**) damage scenarios [78]. by the covariance structure. In addition, and as an important advantage, the weighting proportional to the covariance structure grants an additional layer of feature normalization. While time-series analysis and distance measures can help perform data analysis and indicate the existence of different damages within a data set, employing online and automatic bridge health monitoring procedures must rely on machine learning approaches. These algorithms can autonomously decide whether one or more distinct damages are being observed from patterns in the features. Machine learning is one of the applications of artificial intelligence (AI) where machines are not explicitly programmed to perform certain tasks; rather, they learn and improve from experience automatically. Deep learning is a subset of machine learning based on artificial neural networks for predictive analysis. There are several machine learning algorithms, such as supervised learning or unsupervised learning. In this sense, feature classification as the last step of the methodology can be implemented to automatically discriminate the extracted features into healthy or damaged using supervised or unsupervised algorithms. If training data are available from undamaged and damaged structures, supervised learning algorithms can be implemented. Artificial neural networks (ANN) [85; 86], Support Vector Machines (SVM) [87], or deep Convolutional Neural Networks (CNN) are examples of promising algorithms for supervised strategies. As data collected from damaged civil engineering structures are scarce, unsupervised learning algorithms have been increasingly observed in the literature. This approach is generally based on training machine learning algorithms with baseline references of extracted features, where the structure is assumed to be undamaged and unchanged. New features are then analyzed by the pre-trained machine-learning algorithms to assess whether the structure's behavior remains unchanged. Most of unsupervised techniques are based on outlier detection [78].