
Earthquakes are common and profound natural disasters that pose serious threats to human life and economic activities. High-precision earthquake simulation and visualisation is an effective means to reduce loss of life and to future-proof building and urban planning designs. It is widely used in fields such as virtual production, filmmaking, gaming, emergency response, robotics training and education.听
However, existing methods of earthquake simulation typically require substantial computing resources, rarely operating in real time. That means their data processing is falling short in efficiency and interactive capabilities. Equally, visual fidelity tends to be on the lower spectrum. This means current methods pose challenges to an integration with virtual production systems as these rely on real-time and high-fidelity rendering and intuitive visual design workflows.
In response to this, a team comprising researchers from Royal College of Art鈥檚听SNAP Visualisation Lab听and 国民彩票 iCinema, in collaboration with London-based architecture studio Foster + Partners Ltd,听integrate ANSYS material physics simulation with the听Unreal Engine 5听fracturing system to simulate earthquakes in听Unreal听at high accuracy while maintaining visual fidelity and real-time interactivity.听
The core questions driving this research are:
- How can we integrate advanced physical simulation with high-fidelity rendering technologies of game engines?
- How to ensure the system meets the authenticity (super realistic and believable) and computational efficiency needs for Virtual Production and Extended Reality (XR)?
- How to make the earthquake simulation environment both accurate and user friendly?
The research focuses in particular on enhancing the ANSYS material bridge plugin, on expanding听Unreal's fractured material library, and on improving overall user friendliness. For example, they are implementing dynamic deformations of the ground caused by earthquake waves to enhance simulation precision and realism. To furnish a seamless XR experience, the team is developing cross-platform interfaces to support applications from immersive surround projectors, XR devices and CoStar facilities, ensuring real-time interaction. Outcomes of the research will include a real-time emergency response demo to demonstrate future application potential.
The research is funded by the听XR Network+ Virtual Production in the Digital Economy听project, supported by the UK鈥檚 Engineering & Physical Sciences Research Council (Grant Ref: EP/W020602/1).
- Overview
- Publications
Project Director:听A/Prof. Ali Asadipour
Project Collaborators and Partners:听Laureate Prof. Dennis Del Favero FAHA, Dr Marina Konstantatou (Foster + Partners Ltd), Mr Yitong Sun (RCA)
Project Title:听Development of a High-Fidelity Earthquake Simulation Environment for Virtual Production Based on Unreal Engine
Project Funding:听 UK Engineering & Physical Sciences Research Council鈥檚 XR Network+ Virtual Production in the Digital Economy project (EP/W020602/1)
2024-25
Sun, Y., H. Wang, Z. Zhang, C. Diels & A. Asadipour (2023). 鈥淩ESenv: A Realistic Earthquake Simulation Environment Based on Unreal Engine.鈥澨齀n听N. Pelechano, F. Liarokapis, D. Rohmer & A. Asadipour (eds.),听International Conference on Interactive Media, Smart Systems and Emerging Technologies (IMET). Barcelona: Eurographics Association.听
Song, Y., M. Pagnucco, F. Wu, A. Asadipour & M.J. Ostwald (2024). 鈥淚ntelligent Architectures for Extreme Event Visualisation.鈥 In D. Del Favero, S. Thurow, M. J. Ostwald, & U. Frohne (eds.),听Climate Disaster Preparedness: Reimagining Extreme Events through Art and Technology听(pp. 37鈥48). Cham: Springer.
Green, C., B. Smaill & S. Cubitt (2024). 鈥淚conographies of Climate Catastrophe: The Representation of Climate Change in Art and Film.鈥 In听D. Del Favero,听S. Thurow, M.J. Ostwald & U. Frohne (eds.),听Climate Disaster Preparedness: Reimagining Extreme Events through Art and Technology听(pp. 93鈥106). Cham: Springer.
Moinuddin, K., C. Tirado Cortes, A. Hassan, G. Accary & F. Wu (2024). 鈥淪imulation of Extreme Fire Event Scenarios Using Fully Physical Models and Visualisation Systems.鈥 In听D. Del Favero, S. Thurow, M.J. Ostwald & U. Frohne (eds.),听Climate Disaster Preparedness: Reimagining Extreme Events through Art and Technology(pp. 49鈥63). Cham: Springer.听
Roussel, R., S. Jacoby & A.听Asadipour听(2024). 鈥淩obust Building Identification from Street Views Using Deep Convolutional Neural Networks,鈥澨Buildings听14.3: 578.听