A Unified Engineering–Semantic Framework for Transforming AI-Generated Residential Floor Plans into CAD Drawings and BIM Models

Artificial intelligence has increasingly been applied to residential floor plan generation, producing outputs in diverse forms such as semantic raster images, graph-structured layouts, and vector polygons. However, these outputs still require substantial interpretation and reconstruction before they can enter conventional CAD and BIM workflows. This study develops a unified engineering–semantic framework for transforming heterogeneous AI-generated floor plans into editable CAD drawings and preliminary BIM information models. Three route-specific procedures—ColorPlan, TopoPlan, and PolyPlan—are established to parse and regularize the three representative input forms. The processed information is reorganized into a common engineering–semantic JSON core and subsequently mapped to native AutoCAD entities and Revit objects. The workflow was evaluated using 165 transformation-ready single-story residential samples. The effective CAD/BIM transformation rates reached 100.0%/97.7% for ColorPlan, 93.3%/85.3% for TopoPlan, and 100.0%/97.9% for PolyPlan. The results show that transformation stability is closely related to the completeness of semantic, geometric, topological, and opening information in the source representation. A controlled human–machine experiment further showed that the proposed workflow reduced CAD drafting time by approximately 85.2% and BIM modeling time by 90.0% compared with fully manual production. The study demonstrates a feasible approach for extending heterogeneous AI-generated layouts into editable and information-bearing engineering representations while substantially reducing repetitive drafting and preliminary modeling work.