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.

翻译：建筑领域的人工智能日益依赖于建筑信息模型与知识图谱等结构化表示，然而早期建筑设计主要创建为缺乏显式空间、语义与性能结构的柔性边界表示模型。本文提出一种稳健的全自动化框架，可将非结构化的B-rep几何模型转化为基于知识图谱的建筑信息模型，并进一步转换为可执行的建筑能耗模型。该框架使人工智能能够显式解析建筑构件、空间拓扑及其相关的热工与性能属性。它集成了自动几何清理、多重自动空间生成策略、基于图的空间与构件拓扑提取、本体对齐的知识建模，以及基于本体的BIM与EnergyPlus能耗模型间的可逆转换。在参数化、草图式及真实建筑数据集上的验证表明，该框架具有高度稳健性、一致的拓扑重建能力与可靠的性能模型生成质量。通过衔接设计模型、BIM与BEM，本框架构建了一个面向人工智能的基础设施，将基于BIM与图的智能流程延伸至灵活的早期设计几何模型，从而支持通过基于学习的方法实现性能驱动的设计探索与优化。