Integrated Framework for Tunnel Monitoring and Durability: Leveraging AI, Digital Twin, and Machine Learning for Sustainable Lifecycle Operations

Project description

This project focuses on revolutionising tunnel durability and monitoring by placing digitalisation at its core. The integrated framework will leverage AI, machine learning, and digital twins to optimise tunnel performance and sustainability throughout its lifecycle. A key feature is the development of cutting-edge digital tools and techniques to advance infrastructure management. The project will involve designing and implementing advanced sensor systems for real-time data collection, enabling a deep understanding of tunnel behaviour under diverse operational and environmental conditions. The student will actively contribute to the development and deployment of these monitoring techniques, gaining hands-on experience in sensor integration and data analysis.

The research methodology includes:
1. Sensor Development and Deployment: Designing, testing, and deploying advanced geotechnical and structural health monitoring sensors tailored to tunnel environments.
2. Digital Twin Integration: Creating dynamic virtual replicas of tunnel systems to visualise and predict their performance under varying scenarios.
3. AI and Machine Learning: Applying predictive models to identify potential risks, optimise maintenance, and ensure structural durability.
4. Lifecycle Digitalisation: Employing digital tools to analyse the sustainability and resilience of tunnels across their lifecycle.
By embedding the student in all stages of the project—from sensor design to real-time monitoring and digital twin simulations—this research offers a unique opportunity to contribute to advancing state-of-the-art digitalisation in underground infrastructure. This innovative approach will set new benchmarks for digital-driven, resilient tunnel systems while preparing the student for a leadership role in the future of smart infrastructure development.

Outcomes

Deliverables:
1. Integrated Digital Framework:
A robust framework combining AI, digital twin technology, and machine learning for real-time tunnel monitoring and durability assessment.
2. Sensor Development and Monitoring System:
Innovative geotechnical and structural health monitoring sensors, deployed for data collection and analysis to enhance tunnel performance insights.
3. Digital Twin Prototypes:
Interactive digital models of tunnels for simulating behaviour, predicting risks, and optimising lifecycle management.

Outcomes
1. Improved Tunnel Resilience:
Enhanced structural durability and operational efficiency, reducing the risk of failures and prolonging tunnel lifespans.
2. Sustainable Infrastructure Management:
Optimised lifecycle operations, lowering maintenance costs and environmental impacts through predictive analytics and digital tools.

Essential capabilities

Strong Analytical Skills: Ability to interpret and analyse complex datasets from sensors and monitoring systems. Proficiency in Programming: Experience with programming languages such as Python for data analysis and machine learning applications.

Desireable capabilities

Knowledge of Digital Twin Technology: Familiarity with creating and applying digital models in engineering contexts. Experience with Geotechnical/Structural Monitoring: Background in sensor technologies or structural health monitoring systems.

Expected qualifications (Course/Degrees etc.)

A bachelor’s degree in Civil Engineering, Geotechnical Engineering, Mechanical Engineering, or a related field. Coursework or experience in areas such as Artificial Intelligence, Machine Learning, Data Analysis, or Infrastructure Monitoring is highly desirable.