Accurate representation of building semantics, encompassing both generic object types and specific subtypes, is essential for effective AI model training in the architecture, engineering, construction, and operation (AECO) industry. Conventional encoding methods (e.g., one-hot) often fail to convey the nuanced relationships among closely related subtypes, limiting AI's semantic comprehension. To address this limitation, this study proposes a novel training approach that employs large language model (LLM) embeddings (e.g., OpenAI GPT and Meta LLaMA) as encodings to preserve finer distinctions in building semantics. We evaluated the proposed method by training GraphSAGE models to classify 42 building object subtypes across five high-rise residential building information models (BIMs). Various embedding dimensions were tested, including original high-dimensional LLM embeddings (1,536, 3,072, or 4,096) and 1,024-dimensional compacted embeddings generated via the Matryoshka representation model. Experimental results demonstrated that LLM encodings outperformed the conventional one-hot baseline, with the llama-3 (compacted) embedding achieving a weighted average F1-score of 0.8766, compared to 0.8475 for one-hot encoding. The results underscore the promise of leveraging LLM-based encodings to enhance AI's ability to interpret complex, domain-specific building semantics. As the capabilities of LLMs and dimensionality reduction techniques continue to evolve, this approach holds considerable potential for broad application in semantic elaboration tasks throughout the AECO industry.

翻译：在建筑、工程、施工与运维（AECO）行业中，准确表征建筑语义——涵盖通用对象类型与特定子类型——对于有效的AI模型训练至关重要。传统编码方法（如独热编码）往往无法传达紧密相关子类型间的细微关系，限制了AI的语义理解能力。为克服这一局限，本研究提出一种新颖的训练方法，采用大语言模型（LLM）嵌入（如OpenAI GPT和Meta LLaMA）作为编码，以保留建筑语义中更精细的区分特征。我们通过训练GraphSAGE模型对五个高层住宅建筑信息模型（BIM）中的42种建筑对象子类型进行分类，从而评估所提方法的有效性。实验测试了多种嵌入维度，包括原始高维LLM嵌入（1,536、3,072或4,096维）以及通过Matryoshka表示模型生成的1,024维压缩嵌入。实验结果表明，LLM编码性能优于传统独热编码基线，其中llama-3（压缩）嵌入的加权平均F1分数达到0.8766，而独热编码仅为0.8475。这些结果凸显了利用基于LLM的编码来增强AI理解复杂领域特定建筑语义能力的潜力。随着LLM和降维技术的持续发展，该方法在AECO行业语义细化任务中具有广泛应用的巨大前景。