LORC Researchers introduce revolutionary Digital Twin technology for underground construction

How can Digital Twin technology revolutionise underground construction? Laing O'Rourke Centre’s researchers have published an innovative study that provides key insights into this emerging field. Led by PhD student Nandeesh Babanagar and Laing O'Rourke Associate Professor Dr Brian Sheil, the research, published in Tunnelling and Underground Space Technology, introduces a novel framework for applying Digital Twin (DT) technology to urban underground spaces. Co-authored with experts from the University of Birmingham, Monash University, and Arup, this work clarifies key DT concepts and presents new solutions to the unique challenges of underground construction.

Advancing the concept of digital twins for underground space

As underground construction becomes increasingly vital to accommodate growing infrastructure demands, the sector faces challenges such as uncertain ground conditions, complex site investigations, and prolonged project timelines. The study explores how DTs—virtual models dynamically linked to physical assets—can enhance efficiency, safety, and cost-effectiveness in underground projects.

The paper defines the maturity levels of DTs in underground construction, ranging from basic descriptive models to advanced prescriptive twins capable of automated decision-making. It also presents a layered DT architecture that integrates data-driven ground modelling, BIM integration, advanced sensing, and real-time monitoring. This research is a significant step toward bridging gaps between DTs and the well-established observational method in geotechnical engineering.

Key findings and implications

• Clarification of DT Terminologies: The study demystifies the evolving concept of DTs and outlines their potential applications in underground construction.
• Maturity Levels for DTs: The research categorises DTs into four developmental stages—descriptive, reflective, predictive, and prescriptive—based on their ability to analyse and optimise construction processes.
• Layered DT Architecture: A structured approach is proposed for developing DTs that integrate various technologies such as geospatial data, machine learning, and sensor networks.
• Bridging Observational Methods and DTs: The paper highlights how DTs can enhance the observational method by enabling real-time data acquisition, analysis, and automated decision-making in geotechnical engineering.

Real-world applications and future directions

The adoption of DTs could revolutionise underground construction by improving risk assessment, reducing project uncertainty, and enhancing decision-making efficiency. Potential applications include:

• Optimised Tunnel Boring Machine (TBM) Operations: Real-time DT integration can enhance TBM steering and ground response prediction.
• Foundation and Geotechnical Monitoring: Automated data-driven updates to ground models can improve safety and design efficiency.
• Predictive Maintenance for Underground Structures: Continuous monitoring and AI-driven insights can reduce failures and improve asset longevity.
• Sustainable Construction Practices: DTs can help minimise material use and carbon footprints through real-time optimisation of construction processes.

Looking ahead, further research will focus on refining DT applications in underground asset management, integrating DTs with city-scale digital infrastructure, and enhancing automated decision-making capabilities.

For more details, the full paper is available in Tunnelling and Underground Space Technology.