SHM-driven digital twins for civil engineering
Publication date
2025-10-01
Document type
Konferenzbeitrag
Organisational unit
Conference
International Symposium on Non-Destructive Testing in Civil Engineering (NDT-CE 2025) ; Izmir, Turkey ; September 24–26, 2025
Publisher
NDT.net
Series or journal
E-Journal of Nondestructive Testing (eJNDT)
ISSN
Periodical volume
30
Periodical issue
10
Part of the university bibliography
✅
Language
English
Keyword
dtec.bw
Abstract
As infrastructure ages, the demand for intelligent, proactive maintenance solutions has grown. This study explores
the integration of Structural Health Monitoring (SHM) with Digital Twin technology for civil engineering,
focusing on an existing bridge. By deploying a network of sensors and data acquisition systems, real-time structural
data is continuously fed into a high-fidelity Digital Twin model of the bridge. This SHM-driven Digital Twin is
designed not only to reflect the bridge’s current condition but also to anticipate potential issues before they evolve
into critical failures. The approach enables predictive maintenance by analysing sensor data to identify early signs
of wear, fatigue, and structural anomalies, significantly extending the bridge’s service life and ensuring safety.
The results demonstrate how combining SHM with Digital Twin technology can transform traditional maintenance
practices into a data-informed, predictive system, marking a step forward for intelligent infrastructure. This paper
also discusses the challenges of sensor integration, data management, and real-time analytics essential for
achieving effective predictive maintenance in civil engineering.
the integration of Structural Health Monitoring (SHM) with Digital Twin technology for civil engineering,
focusing on an existing bridge. By deploying a network of sensors and data acquisition systems, real-time structural
data is continuously fed into a high-fidelity Digital Twin model of the bridge. This SHM-driven Digital Twin is
designed not only to reflect the bridge’s current condition but also to anticipate potential issues before they evolve
into critical failures. The approach enables predictive maintenance by analysing sensor data to identify early signs
of wear, fatigue, and structural anomalies, significantly extending the bridge’s service life and ensuring safety.
The results demonstrate how combining SHM with Digital Twin technology can transform traditional maintenance
practices into a data-informed, predictive system, marking a step forward for intelligent infrastructure. This paper
also discusses the challenges of sensor integration, data management, and real-time analytics essential for
achieving effective predictive maintenance in civil engineering.
Description
Published by NDT.net under License CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/)
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Published version
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