Underground construction projects often run over budget and behind schedule because engineers cannot accurately predict how soil, water, and buried structures will interact. This fellowship aims to fix that by building a digital twin—a virtual replica of an underground construction site that updates in real time with sensor data. The core problem is that soil-fluid-structure interactions are notoriously complex; traditional modelling methods produce uncertainties that force conservative designs and costly delays. The researcher will combine Building Information Modeling, digital twins, and advanced data analytics to create a system that can forecast what-if scenarios, optimise construction methods, and enable predictive maintenance. If successful, the work could make underground infrastructure—such as metro lines, utility tunnels, and storage caverns—cheaper, faster, and more resilient to unexpected ground conditions. The project is applied engineering research with a clear practical goal: to give civil engineers a reliable digital tool for managing the hidden complexity beneath our cities.
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CONTEXT In today's rapidly urbanizing world, the need for innovative, sustainable, and efficient infrastructure solutions has never been greater. Underground construction presents a promising avenue to address this challenge, providing the means to expand vital transportation networks, utility systems, and storage facilities while minimizing surface disruption. As urban populations continue to grow, the demand for underground infrastructure will surge, requiring novel approaches that can deliver resilient, cost-effective, and environmentally conscious solutions. This fellowship seeks to harness the power of advanced digital technologies to transform underground construction, aligning with the ongoing global push for smarter, more efficient infrastructure development. CHALLENGE & APPLICATION Underground construction offers immense potential, but it also comes with significant hurdles. The complexity of soil-fluid-structure interactions (SFS) poses challenges that impact construction processes, project timelines, and costs. Traditional methods often struggle to accurately model and simulate these interactions, leading to uncertainties and suboptimal designs. This fellowship addresses this challenge by integrating cutting-edge digital tools, including Building Information Modeling (BIM), digital twins, and advanced data analytics. By doing so, it aims to revolutionize how we approach underground construction, enabling accurate prediction of SFS interactions and optimizing construction methodologies. AIMS & OBJECTIVES The primary aim of this fellowship is to reshape the landscape of underground construction by seamlessly integrating digital technologies. The project's objectives are: 1. Develop advanced digital modeling techniques that accurately predict complex SFS interactions in underground construction scenarios. 2. Create a comprehensive digital twin that integrates real-time data, enabling continuous monitoring and predictive maintenance of underground construction processes. 3. Identify and deploy optimal real-time monitoring technologies to gather data for improving the accuracy of the digital twin. 4. Apply advanced data analytics to optimize construction processes, enabling what-if scenario forecasting and predictive maintenance models. 5. Facilitate knowledge transfer and dissemination of research outcomes to industry professionals, policymakers, and stakeholders, driving the adoption of digital technologies in underground construction.
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