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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. 

The American Society of Civil Engineers (ASCE) Report Card for America’s Infrastructure gave bridges a C+ (mediocre) grade in 2017. Approximately, 1 in 5 rural bridges are in critical condition, which presents serious challenges to public safety and economic growth. Fortunately, during a series of workshops on this topic organized by the authors, it has become clear that Big Data could provide a timely solution to these critical problems. In this work in progress paper, we describe a conceptual framework for developing SMart big data pipelines for Aging Rural bridge Transportation Infrastructure (SMARTI). Our framework and associated research questions are organized around four ingredients: • Next-Generation Health Monitoring: Sensors; Unmanned Aerial Vehicle/System (UAV/UAS); wireless networks • Data Management: Data security and quality; intellectual property; standards and shared best practices; curation • Decision Support Systems: Analysis and modeling; data analytics; decision making; visualization, • Socio-Technological Impact: Policy; societal, economic and environmental impact; disaster and crisis management.