Digital Twin for Civil Engineering Systems: An Exploratory Review for Distributed Sensing Updating

Area of Science:

• Civil Engineering
• Computer Science
• Data Science

Background:

• Growing demand for transport networks strains ageing infrastructure.
• Replacing all aging assets is often infeasible and costly.
• Structural Health Monitoring (SHM) offers a viable alternative for extending asset service life.

Purpose of the Study:

• To review the utility and operational principles of Digital Twin models in infrastructure management.
• To explore the application of Digital Twins for enhancing the durability and serviceability of transport networks.
• To assess the suitability of Distributed Sensing for Digital Twin sensor networks.

Main Methods:

• Exploratory review of Digital Twin technology and its components.
• Analysis of data integration from sensor networks to Digital Twin models.
• Examination of structural reliability indices and predictive capabilities.

Main Results:

• Digital Twins, frequently updated with sensor data, provide a dynamic digital reconstruction of physical assets.
• This technology enables infrastructure managers to monitor, optimize, and make data-driven decisions.
• Distributed Sensing is identified as a suitable sensor network component for Digital Twins.

Conclusions:

• Digital Twin models offer a powerful tool for proactive maintenance and informed decision-making in infrastructure management.
• The integration of real-time sensor data and structural reliability indices enhances asset serviceability.
• Digital Twins, supported by Distributed Sensing, are crucial for the future of resilient and sustainable transport networks.