LLM based Digital Twin for Asset and Facility Management - 2026_IDR_DABC_9

Digital Twins (DTs) integrate sensor-generated data with geometric, semantic, and contextual information from Building Information Modelling (BIM), supporting the management and maintenance of buildings and infrastructure. However, their adoption in Asset and Facility Management (A&FM) is often constrained by the complexity of accessing and interpreting heterogeneous data.This research will investigate the use of Large Language Models (LLMs) as natural-language interfaces to Digital Twins, enabling users to access and interpret data without advanced technical expertise. It will develop and validate an LLM-enabled framework to transform Digital Twin data into actionable information supporting operational and strategic decision-making.