Semantic Mapping of Request for Information (Rfi) to BIM Elements with Large Language Models

Abstract: Requests for Information (RFIs) remain a critical yet poorly integrated component of construction workflows, typically managed as unstructured text disconnected from Building Information Modeling (BIM) data. Existing studies primarily focus on reducing RFI frequency or response time, with limited focus on linking RFI content to specific BIM elements. This study presents a data-driven framework that combines large language model (LLM)-based intent extraction with BIM metadata reasoning through a staged matching pipeline. By separating semantic cue extraction from rule-based element selection, the framework enhances interpretability and robustness. Validation on experimental and real-world datasets demonstrates reliable element and family-level retrieval.