A multivocal literature review of digital twins, architectures, and elements in civil engineering

Recent structural health monitoring (SHM) strategies in civil engineering increasingly leverage digital twins, which digitally represent the structures being monitored as well as the SHM systems installed on the structures. Despite the widespread adoption of digital twin applications in recent years in SHM, there is a lack of agreement on a common definition of digital twins. Furthermore, there is no consensus on digital twin architectures and on the internal elements that constitute digital twins. A common digital twin definition would advance digital twin implementations and operability, and insights into digital twin architectures and digital twin elements would be vital to enhance the reliability and performance of digital-twin-based SHM systems. A significant number of digital twin definitions have been proposed and a plethora of reviews have been published; however, little emphasis has been given to digital twin architectures and internal elements. This paper presents a multivocal review of digital twins in civil engineering, aiming to provide a panorama of the digital twin landscape in civil engineering with explicit insights into the architectures and internal elements used in digital twin applications. From a methodological standpoint, the review follows a twofold approach that encompasses (i) peer-reviewed, indexed literature (“white literature”) as well as (ii) non-indexed sources (“gray literature”) that include industrial digital twin applications. Besides the multivocal review, a generic digital twin reference architecture is drawn from the review results and a digital twin definition is formulated both in an informal and formal (i.e., mathematical) manner. It is expected that the generic reference architecture and the definitions proposed in this study may serve as a blueprint for digital-twin-based SHM applications, with significant implications for researchers, practitioners, and policymakers in structural health monitoring.