🤖 AI Summary
This work proposes the first fully automated framework to transform unstructured B-rep geometry from early architectural design into a knowledge graph–based building information model (BIM) and a corresponding executable building energy model (BEM). Addressing the limitations of conventional B-rep representations—which lack explicit spatial, semantic, and performance-aware structures—the method integrates automatic geometry cleaning, multi-strategy space generation, graph-based topological extraction, ontology-aligned modeling, and bidirectional BIM–EnergyPlus conversion. This enables, for the first time, a robust end-to-end mapping from freeform design geometries to semantically rich BIMs and reversible BEMs. Experimental validation on parametric, sketch-based, and real-world building datasets demonstrates high topological consistency and reliable energy performance modeling, establishing a critical infrastructure for AI-driven, performance-informed design in the early stages of architectural development.
📝 Abstract
Artificial intelligence in construction increasingly depends on structured representations such as Building Information Models and knowledge graphs, yet early-stage building designs are predominantly created as flexible boundary-representation (B-rep) models that lack explicit spatial, semantic, and performance structure. This paper presents a robust, fully automated framework that transforms unstructured B-rep geometry into knowledge-graph-based Building Information Models and further into executable Building Energy Models. The framework enables artificial intelligence to explicitly interpret building elements, spatial topology, and their associated thermal and performance attributes. It integrates automated geometry cleansing, multiple auto space-generation strategies, graph-based extraction of space and element topology, ontology-aligned knowledge modeling, and reversible transformation between ontology-based BIM and EnergyPlus energy models. Validation on parametric, sketch-based, and real-world building datasets demonstrates high robustness, consistent topological reconstruction, and reliable performance-model generation. By bridging design models, BIM, and BEM, the framework provides an AI-oriented infrastructure that extends BIM- and graph-based intelligence pipelines to flexible early-stage design geometry, enabling performance-driven design exploration and optimization by learning-based methods.