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The growing number of aging bridges and their associated risks call for smarter, more cost-effective maintenance strategies. Digital twins (DTs) offer significant potential by enabling real-time monitoring, predictive analytics, and integrated decision-making. Although DTs are widely explored, their integration into enterprise architectures for infrastructure asset management remains limited. This paper presents a structured approach to architecting predictive DTs for bridge maintenance using the ArchiMate modeling language. Following a Design Science Research Methodology (DSRM), we: (1) analyze limitations in traditional maintenance workflows; (2) design reference enterprise architectures for DT integration; (3) propose a phased migration strategy toward prescriptive DTs; and (4) demonstrate practical feasibility through a requirement-based comparison and an illustrative implementation applied to a Dutch steel bridge. The architecture integrates IoT sensing, AI-based defect detection, and XR-based visualization. This work provides a blueprint for digital transformation initiatives in civil infrastructure.

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Designing a Predictive Digital Twin Architecture for Smart Bridge Maintenance

  • 会议论文

  • 发表日期：

    2026年

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  • Ali Sabzi Khoshraftar,
  • Berend Jan van der Zwaag,
  • Duc V. Le,
  • Rob Bemthuis

摘要

The growing number of aging bridges and their associated risks call for smarter, more cost-effective maintenance strategies. Digital twins (DTs) offer significant potential by enabling real-time monitoring, predictive analytics, and integrated decision-making. Although DTs are widely explored, their integration into enterprise architectures for infrastructure asset management remains limited. This paper presents a structured approach to architecting predictive DTs for bridge maintenance using the ArchiMate modeling language. Following a Design Science Research Methodology (DSRM), we: (1) analyze limitations in traditional maintenance workflows; (2) design reference enterprise architectures for DT integration; (3) propose a phased migration strategy toward prescriptive DTs; and (4) demonstrate practical feasibility through a requirement-based comparison and an illustrative implementation applied to a Dutch steel bridge. The architecture integrates IoT sensing, AI-based defect detection, and XR-based visualization. This work provides a blueprint for digital transformation initiatives in civil infrastructure.