The importance of Structural Health Monitoring (SHM) in the industry is increasing due to various loads, such as earthquakes and wind, having a significant impact on the performance of structures and equipment. Estimating responses is crucial for the effective health management of these assets. However, using numerous sensors in facilities and equipment for response estimation causes economic challenges. Additionally, it could require a response from locations where sensors cannot be attached. Digital twin technology has garnered significant attention in the industry to address these challenges. This paper constructs a digital twin system utilizing the Long Short-Term Memory (LSTM) model to estimate responses in a pipe system under simultaneous seismic load and arbitrary loads. The performance of the data-driven digital twin system was verified through a comparative analysis of experimental data, demonstrating that the constructed digital twin system successfully estimated the responses.

1. 서 론
2. 배관의 데이터 기반 디지털 트윈 시스템
    2.1 디지털 트윈 시스템 개발
    2.2 디지털 트윈 시스템에 활용된 LSTM 기법
    2.3 디지털 트윈 시스템에 활용된 하중추정 기법
3. 배관의 디지털 트윈 시스템 구축
    3.1 대상 배관 및 수치모델
    3.2 배관의 응답을 추정하는 딥러닝 모델 개발
4. 디지털 트윈 시스템 적용
5. 결 론
감사의 글

• 김동창(경북대학교 융복합시스템공학과 박사과정) | Kim Dongchang (Ph.D. Student, Department of Convergence & Fusion System Engineering, Kyungpook National University)
• 김건규(경북대학교 융복합시스템공학과 석사과정) | Kim Gungyu (Graduate Student, Department of Convergence & Fusion System Engineering, Kyungpook National University)
• 곽신영(한밭대학교 건설환경공학과 부교수) | Kwag Shinyoung (Associate Professor, Department of Civil & Environmental Engineering, Hanbat National University)
• 임승현(경북대학교 융복합시스템공학과 부교수) | Eem Seunghyun (Associate Professor, Department of Convergence & Fusion System Engineering, Kyungpook National University / A B S T R A C T / The) Corresponding author