ChatTwin: Bridging the usability gap to digital twin adoption in infrastructure operations and maintenance with a Natural Language Interface

Abstract

Digital twins have promising potential to enhance infrastructure operations and maintenance by enabling real-time monitoring and predictive maintenance. However, for professionals in the infrastructure industry with limited digital skills, existing digital twin systems can be complex and unintuitive, challenging to learn and use, requiring extensive prior training, and contributing to low adoption rates. This paper proposes ChatTwin, a natural language interface for infrastructure digital twins, designed to support immediate usability. By enabling users to interact with digital twins directly through natural language, ChatTwin allows users to complete tasks, access information, and explore functionality with minimal onboarding. ChatTwin was benchmarked with over 300 crowd-sourced prompts, and a user study (N = 24) was conducted. Results demonstrate that ChatTwin effectively interprets user input, improves task efficiency, reduces cognitive load, and enhances user experience, suggesting that it can make digital twins more accessible to professionals with lower technological familiarity, particularly during early adoption.