Automated conversion and semantic gap filling of material layer annotations from CAD drawings to semantically rich BIM objects

This study proposes a method for semantically enriching a building information model (BIM) by converting and supplementing material layer information in computer-aided-design (CAD) drawings into semantically rich BIM objects. Despite the widespread adoption of BIMs, practices relying on CAD drawings are still prevalent and fully automated conversion from CAD-to-BIM remains challenging. Previous studies on automatically converting 2D drawings into a BIM have primarily focused on geometric reconstruction, often overlooking textual data such as material layer annotations, which contain essential BIM object details. However, the recognition and conversion of material layer annotations to BIM objects pose several challenges, including inconsistent annotation formats, vague associations between annotations and objects, and missing material function and property information. To address these challenges, this paper introduces a four-step method: (1) semantic clustering of material layer annotations into groups corresponding to individual objects; (2) material layer function assignment using a newly proposed mistake-driven prompting technique, which incorporates feedback from common mistaken cases; (3) object type classification using a large language model based solely on material layer texts; and (4) automated generation of BIM objects semantically enriched with material property information in single-directional composite or light-framed structures. Validation on 90 real-world cases demonstrated a 99.9% weighted average adjusted Rand index for semantic clustering, a 99.5% weighted F1-score for layer function assignment, a 92.7% weighted F1-score for object type classification, and 100% accuracy in BIM object generation. This method is expected to enable a more complete 2D-to-BIM conversion by complementing geometric reconstruction with semantically rich material layer information.