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Sensorintegrierte Digitale Zwillinge für das automatisierte Monitoring von Infrastrukturbauwerken
Publikationstyp
Journal Article
Date Issued
2022
Sprache
English
Author(s)
Institut
Journal
Volume
99
Issue
6
Start Page
471
End Page
476
Citation
Bautechnik 99 (6): 471-476 (2022-06)
Publisher DOI
Scopus ID
Peer Reviewed
true
Sensor-integrated digital twins for wireless structural health monitoring of civil infrastructure. Digital twins constitute a promising technology that lies at the core of emerging trends within Industry 4.0, such as cyber-physical systems and the Internet-of-Everything. The civil engineering community has been showing increasing interest in adopting digital twins for operation and management purposes, e. g. within the framework of structural health monitoring (SHM). In particular, SHM stands to benefit from the enhanced predictive capabilities of digital twins, which yield richer information on structural conditions than conventional models. However, adapting state-of-the-art digital twins to modern SHM strategies, which rely on wireless technologies, is hardly straightforward. Specifically, digital twins that model a physical process are usually centralized and updated using large amounts of data collected from the physical process. By contrast, wireless SHM systems are distributed, and wireless SHM strategies typically focus on minimizing wireless communication, which is unreliable and power-consuming. In this context, this paper presents the results of a feasibility study that discusses the practical considerations of embedding digital twins in wireless SHM systems. Through field tests on a pedestrian bridge, instrumented with a wireless SHM system, the decentralized embedment of a digital twin – in the form of partial digital twins – into the wireless sensor nodes is reported. The field test results indicate that decentralized digital twins can advantageously be used for advancing wireless SHM.
Subjects
Buildings
civil infrastructure
digital twins
digitalization
IT/Automatical/CAD
structural health monitoring
wireless sensor networks
DDC Class
620: Ingenieurwissenschaften