TY - GEN
T1 - Data Delivery for Digital Twin Models of Prestressed Concrete Bridges
AU - Shim, Changsu
AU - Roh, Gitae
AU - Youn, Seok Goo
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - To ensure the optimal performance of prestressed concrete bridges, a seamless flow of data from the design phase through to operation is crucial. This study proposes the adoption of digital engineering models for both individual prefabricated bridge members and the fully assembled structure, facilitating comprehensive information delivery throughout the bridge’s lifecycle. It underscores the importance of documenting variations in material properties and construction stages, culminating in the establishment of a baseline model upon bridge completion, grounded in actual rather than assumed design data. The ongoing collection of inspection data during the bridge’s operational phase enables continual update of this model, enhancing the reliability of performance assessments. A pivotal advancement is the development of a conceptual digital twin model, enriched by aggregated data from analogous structures, which heralds a new era in predictive analytics for bridge performance. The application of this model to an expressway bridge illustrates its potential to revolutionize bridge engineering by enabling proactive maintenance and management strategies. This paper not only details the framework of this innovative digital twin model but also highlights its practical implications, marking a significant leap forward in the field of civil engineering.
AB - To ensure the optimal performance of prestressed concrete bridges, a seamless flow of data from the design phase through to operation is crucial. This study proposes the adoption of digital engineering models for both individual prefabricated bridge members and the fully assembled structure, facilitating comprehensive information delivery throughout the bridge’s lifecycle. It underscores the importance of documenting variations in material properties and construction stages, culminating in the establishment of a baseline model upon bridge completion, grounded in actual rather than assumed design data. The ongoing collection of inspection data during the bridge’s operational phase enables continual update of this model, enhancing the reliability of performance assessments. A pivotal advancement is the development of a conceptual digital twin model, enriched by aggregated data from analogous structures, which heralds a new era in predictive analytics for bridge performance. The application of this model to an expressway bridge illustrates its potential to revolutionize bridge engineering by enabling proactive maintenance and management strategies. This paper not only details the framework of this innovative digital twin model but also highlights its practical implications, marking a significant leap forward in the field of civil engineering.
KW - baseline model
KW - Data delivery
KW - digital twin model
KW - key performance
KW - prestressed concrete bridge
UR - https://www.scopus.com/pages/publications/105016159300
U2 - 10.1007/978-981-96-8464-9_36
DO - 10.1007/978-981-96-8464-9_36
M3 - Conference contribution
AN - SCOPUS:105016159300
SN - 9789819684632
T3 - Lecture Notes in Civil Engineering
SP - 282
EP - 288
BT - Proceedings of the 18th East Asia-Pacific Conference on Structural Engineering and Construction - Volume 1 - EASEC-18 2024
A2 - Tangtermsirikul, Somnuk
A2 - Panuwatwanich, Kriengsak
A2 - Tanapornraweekit, Ganchai
A2 - Warnitchai, Pennung
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th East Asia-Pacific Conference on Structural Engineering and Construction, EASEC 2024
Y2 - 13 November 2024 through 15 November 2024
ER -