Year: 2025
Type: article
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.
DOI: 10.58286/31669
Authors: Sylvia Kessler, Martin Köhncke
Institution: Helmut Schmidt University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Funder: European Commission
OpenAlex ID: W4414335723