Since the end of the 20th century, the large-scale, complex, and intelligent development of civil structures has made structural health monitoring (SHM) increasingly important, expanding from single load stress monitoring to structural damage detection, rapid localization, and life prediction. SHM is the process of identifying and warning structural damage using data obtained from sensors. However, a single measurement data or digital model is insufficient to support reliable diagnosis and prediction. Digital twins (DT) emphasize the use of all data, the diversity of data provided by sensors, and the use of data to drive knowledge acquisition and increase cognition. With the help of big data provided by health monitoring systems, digital twins use data analysis to diagnose the current state of civil structures, predict their future performance, and make real-time decisions on future operations based on the information provided by the data.

This Research Topic aims to provide a convergence research forum for academics and practitioners around the world to present the latest advances on the digital twin for structural health monitoring, including the advances of professional knowledge and technical solutions in digital twin for SHM.

The scope of this Research Topic covers all issues relating to digital twin for SHM subject field. Issues within this Research Topic could be connected to, but are not limited to, the following listed areas:

 Smart sensor development (hardware or software) in SHM

 Application of wireless sensors and IoT to SHM

 Analysis method for SHM data

 Big data management and mining

 Digital twin concepts, architecture, and frameworks

 Digital twin theory and method for SHM

 Digital twin key technologies and tools for SHM

 Structural damage identification based on the DT model

 Structural performance evaluation based on the DT model

 Case studies of digital twin for SHM (e.g., bridges, buildings, and tunnels), etc.

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Frontiers in Built Environment

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