Bridging Physical and Digital Realms: The Role of Digital Twins in Geotechnical Engineering

Digital twins, particularly “Asset Twins,” serve as virtual replicas of physical assets, continuously synchronized with real-time data. These digital counterparts bridge the physical and digital domains, facilitating real-time insights, accident analysis, workspace optimization, predictive maintenance, and other functionalities. Their applications extend across various sectors, including smart cities, underground structures, tunneling, and renewable projects. The global digital twin market is projected to reach USD 195.85 billion by 2030, driven by compound annual growth rates (CAGRs) between 35.7% and 45.1%. This paper demonstrates the application of digital twin models in geotechnical engineering, underpinned by digital simulations of physical assets utilizing cloud computing, Internet of Things (IoT) platforms, and machine learning. The development of physics-informed asset models within the digital twin framework involves four key steps: (1) quantifying uncertainty in input variables and propagation, (2) computational modeling (FE/FD modeling), (3) optimization and surrogate modeling, and (4) Bayesian updating. Since gaining prominence post-2017, particularly in IoT and predictive analysis, digital twins have proven effective in geotechnical projects, enhancing efficiency, safety, and cost-effectiveness. This paper includes physics-informed solutions for two case studies on tunneling-induced damage and explores applications in geotechnical engineering across various industries, including renewable energy. These examples illustrate how physics-informed modeling in digital twins can address challenges arising from unexpected variations and input parameter uncertainties throughout project stages, thereby enhancing decision-making and overall construction strategies. The paper also reviews the challenges of implementing digital twin frameworks in geotechnical projects and beyond, including the recruitment of skilled experts, management of complex data, overcoming industry conservatism, ensuring information alignment, cybersecurity, and managing cloud computing and data center costs. Effective leadership, emphasizing collaboration and a consultative approach, is crucial for the successful deployment of digital twin projects.