用320亿条用户日志训练模型，实时推荐BIM操作下一步。
Predictive Modeling: BIM Command Recommendation Based on Large-scale Usage Logs
- 基于Transformer架构，融合用户历史操作序列预测命令。
- 在320亿条全球真实日志上实现Recall@10达84%。
- 适合建筑、工程领域提升BIM软件使用效率的从业者。
建筑、工程与施工（AEC）行业对建筑信息模型（BIM）的采用受到使用BIM建模工具比传统2D绘图更耗时的印象制约。为提升设计效率，本文提出一种基于大规模用户交互日志的BIM命令推荐框架，可实时预测最优下一步操作。我们提出了全面的原始BIM日志数据清洗与增强方法，并引入一种新型命令推荐模型。该模型基于专为大语言模型（LLMs）设计的先进Transformer骨干网络，结合自定义特征融合模块、专用损失函数和定向学习策略。在案例研究中，该方法应用于从全球BIM建模软件Vectorworks收集的超过320亿行真实日志数据。实验结果表明，该方法能从不同国家、专业和项目中匿名用户操作序列中学习通用且可泛化的建模模式。在生成下一步命令推荐时，达到约84%的Recall@10。代码已公开：https://github.com/dcy0577/BIM-Command-Recommendation.git。
原文摘要 · Abstract (English)
The adoption of Building Information Modeling (BIM) and model-based design within the Architecture, Engineering, and Construction (AEC) industry has been hindered by the perception that using BIM authoring tools demands more effort than conventional 2D drafting. To enhance design efficiency, this paper proposes a BIM command recommendation framework that predicts the optimal next actions in real-time based on users' historical interactions. We propose a comprehensive filtering and enhancement method for large-scale raw BIM log data and introduce a novel command recommendation model. Our model builds upon the state-of-the-art Transformer backbones originally developed for large language models (LLMs), incorporating a custom feature fusion module, dedicated loss function, and targeted learning strategy. In a case study, the proposed method is applied to over 32 billion rows of real-world log data collected globally from the BIM authoring software Vectorworks. Experimental results demonstrate that our method can learn universal and generalizable modeling patterns from anonymous user interaction sequences across different countries, disciplines, and projects. When generating recommendations for the next command, our approach achieves a Recall@10 of approximately 84%. The code is available at: https://github.com/dcy0577/BIM-Command-Recommendation.git
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