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学术动态：A Digital Twin Framework for Structural Health Monitoring of Existing Large-Span Bridges

MDPI Sensors 论文摘要：Large-span bridges are critical components of transportation networks. Environmental variability, material degradation, and cumulative fatigue continuously affect their long-term performance. Digital Tw

创建时间2026-05-22

论文标题：A Digital Twin Framework for Structural Health Monitoring of Existing Large-Span Bridges

发布日期：2026-05-22

作者：Minh Quang Tran, Hélder S. Sousa, José C. Matos, Son N. Dang, Huan X. Nguyen

DOI：10.3390/s26113293

论文摘要：Large-span bridges are critical components of transportation networks. Environmental variability, material degradation, and cumulative fatigue continuously affect their long-term performance. Digital Twin (DT) technology has emerged as a promising paradigm for integrating sensing, modeling, and data analytics. Most existing DT implementations in civil infrastructure rely on dense sensor networks, assume near-complete observability, and primarily serve as passive visualization or diagnostic tools, limiting their scalability and practical applicability. This paper proposes a DT framework specifically designed for the monitoring and management of existing large-span bridges under sparse sensing conditions. The framework adopts an information-centric perspective in which limited physical measurements are complemented by full-field state reconstruction through the integration of physics-based modeling, data-driven learning, and uncertainty-aware inference. A synchronized reference configuration, termed State 0, is introduced as the initial basis for tracking structural changes over time, while allowing conditional re-baselining through a Dynamic State 0 (DS0) when verified reassessment justifies it. On this basis, the proposed DT is formulated as an adaptive and decision-oriented cyber–physical system that supports optimization-based recommendations for sensing, inspection, and maintenance planning.

元数据：Crossref 收录的 MDPI Sensors 论文。 DOI: 10.3390/s26113293. Vol. 26, Issue 11. Authors: Minh Quang Tran, Hélder S. Sousa, José C. Matos, Son N. Dang, Huan X. Nguyen.

开放许可： https://creativecommons.org/licenses/by/4.0/

原文链接： https://doi.org/10.3390/s26113293

PDF 链接： https://www.mdpi.com/1424-8220/26/11/3293/pdf

来源： MDPI Sensors via Crossref

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