# Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
###### Abstract  
This study focuses on developing and examining the effectiveness of Transfer Learning (TL) for structural health monitoring (SHM) systems that transfer knowledge about damage states from one structure (i.e., the source domain) to another structure (i.e., the target domain). Transfer Learning (TL) is an efficient method for knowledge transfer and mapping from source to target domains. In addition, Proper Orthogonal Modes (POMs), which help classify behavior and health, provide a promising tool for damage identification in structural systems. Previous investigations show that damage intensity and location are highly correlated with POM variations for structures under unknown loads. To train damage identification algorithms based on POMs and ML, one generally needs to use multiple simulations to generate damage scenarios. The developed process is applied to a simply supported truss span in a multi-span railway bridge. TL is first used to obtain relationships between POMs for two modeled bridges: one being a source model (i.e., labeled) and the other being the target modeled bridge (i.e., unlabeled). This technique is then implemented to develop POMs for a damaged, unknown target using TL that links source and target POMs. It is shown that the trained knowledge from one bridge was effectively generalized to other, somewhat similar, bridges in the population. structural health monitoring; Transfer Learning; domain adaptation; damage detection; Proper Orthogonal Decomposition; classification; bridge 2023  
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wide variety of bridges with some degree of similarity in behavior and damage states by transferring, a priori, knowledge from one structure to similar structures in the population. Data-driven methodologies for damage identification in structures are often developed using ML and, more specifically, pattern recognition algorithms [4]. The most common approach for implementing data-driven damage identification is to find a robust damage feature from measured data. One of the main challenges in feature selection is finding features that are sensitive to damage and insensitive to other operational variables. Additionally, the correlation of feature behavior with damage level and potential low dimensionality of the feature vector are crucial factors affecting successful damage identification [5]. Although modal parameters are common features for most vibration-based damage identification methods, they cannot be directly used for damage identification in highly nonlinear structural systems [6]. In addition, modal parameters need to be decoupled from sensor noise to achieve improved damage detection accuracy [7]. [PERSON] et al. [8] investigated the fault detection of an in-service railway track using measured acceleration. They demonstrated that, for a certain range of train forward speed, the extracted amplitude of acceleration after applying Peak-Based Decomposition (PBD) corresponded to the data observed from the Track Recording Vehicle (TRV) and could potentially be considered a damage indicator. [PERSON] et al. [9] proposed ensemble empirical mode decomposition (EEMD) to eliminate unnecessary information from the original signal for analyzing the pantograph-catenary system. Principal Component Analysis (PCA), a POD method, is a powerful technique for capturing dominant features in a multi-dimensional system using a few modes [10]. To eliminate environmental effects, [PERSON] et al.