# Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
###### Abstract
[11] proposed a PCA-based damage identification approach that chose the first natural frequency as the damage feature and assessed its performance by examining an experimental time varying system under controlled temperature. [PERSON] and [PERSON] [12] numerically studied structural damage detection using Singular Value Decomposition (SVD) by computing Proper Orthogonal Modes (POMs) and examined differences between POMs in healthy and damaged structure models. [PERSON] and [PERSON] [13] developed a POD-based damage identification algorithm for a composite beam using vibration data. [PERSON] et al. [14] developed an automated damage detection framework utilizing POD and Artificial Neural Networks (ANNs) to detect the location of simulated fatigue cracks in a steel railway bridge. More recently, [PERSON] et al. [15] examined the effectiveness of a POD framework for damage identification in three simply supported bridges by imposing actual damage scenarios. The damage identification framework for complex structural systems is developed by training the damage features to be mapped to the corresponding damage labels. ANN is utilized to model this relationship between input (damage features) and output (damage labels) data. This algorithm has been widely used for pattern recognition and classification, and, in structural engineering, for the identification of deficiencies. [PERSON] and [PERSON] [16] proposed an ANN-focused, two-step algorithm that implemented modal energies for simulated damage identification using an FE girder bridge model. [PERSON] et al. [17] demonstrated the efficacy of ANNs in identifying damage location and severity in truss bridge connections using modal shape parameters. [PERSON] et al. [18] proposed using a multilayer ANN that focused on changes in natural frequencies. Novelty indices that quantified damage severity were determined to distinguish changes in natural frequencies caused by damage from those caused by temperature variations. Despite the large amount of research that successfully utilized ANNs as a learning method for applications of structural damage identification and SHM to various engineering problems, training time and output accuracy depend heavily on network structure. In many cases, the amount of computational effort required to retrain a developed network for a structure in a population with some degree of similarity to a structure within a trained network is excessive. Finite Element (FE) modeling uncertainties (MUs) can lead to unreliable ANN results [19]. MU can be influential with respect to damage detection algorithm accuracy even if there is a good match between FE model predictions and experimental data [20]. [PERSON] et al. [21] used differences between mode shape ratios for damage identification on a simple beam and a multi-girder bridge using ML to examine the effect of MU on damage detection. The proposed method was applied to an in-service, multi-girder bridge, and minor estimated damage intensity false positives were observed. [PERSON] et al. [19] utilized statistical ANNs involving modal parameters from an FE model of a single span steel portal frame to investigate the effects of MU on vibration-based damage detection by considering random errors. They concluded that the statistical ANN detected damages with higher accuracy compared to a normal ANN. [PERSON] et al. [22] investigated the effects of MU on an operational railway bridge using a hybrid damage detection algorithm based on POD and ANNs. A series of numerical investigations were completed that involved different MUs and a robust damage feature was developed that was less sensitive to MUs. Result accuracy varied based on examined MU, with less accurate results being obtained at higher MU levels.