The use of digital twins & AI in improving sustainable outcomes
A digital twin is a virtual representation of an object or system designed to reflect a real-world object accurately. They can range from a simple, stand-alone virtual interpretation of a product component to an intricate, dynamic representation of the lifecycle activities and assets within a building or even an entire city. They can be updated from real-time data and use simulations, machine learning and reasoning to help make decisions.
Digital twins are designed to enable a two-way flow of information. This occurs when object sensors provide relevant data to the system processor and then again when insights created by the processor are shared back with the original source object.
This call for abstracts seeks contributions from authors that:
• Can describe real-life examples of where the synergies between digital twins and AI exist in the design, construction and operation of built assets and how this is contributing to their sustainability.
• Are undertaking digital twin and AI R&D that can help to improve the sustainability of our built assets.
• Have encountered problems or limitations with digital twins and/or AI in the built environment and how these have been resolved.
• Can illustrate how digital twins and/or AI can start out on a small scale and grow over time, in line with data quality and understanding.
Abstracts for papers that cover any aspect of civil engineering across all types of infrastructure are welcome, as are papers that draw on the lessons learned from other industries and sectors and apply them to built assets. Authors should clearly draw out the sustainability benefits of digital twins and AI and, where possible.
Submission information
Submit your abstract by 30 April 2025
For more details here
