Predictive modeling: BIM command recommendation based on large-scale usage logs

Changyu Du

doi:10.1016/j.aei.2025.103574

2025-07-22

DOI：10.1016/j.aei.2025.103574

原文链接

摘要

En 中文

建筑信息模型（BIM）和基于模型的设计在建筑、工程和施工（AEC）行业的应用一直受到“使用BIM创建工具比传统二维绘图需要更多努力”这一观念的阻碍。为提高设计效率，本文提出了一种BIM命令推荐框架，该框架基于用户的历史交互实时预测最优下一步操作。我们针对大规模原始BIM日志数据提出了一种全面的过滤和增强方法，并引入了一种新颖的命令推荐模型。该模型基于为大型语言模型（LLMs）开发的最新Transformer骨干网络构建，并整合了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，该方法应用于超过320亿行来自全球BIM创建软件Vectorworks的真实日志数据。实验结果表明，我们的方法能够从不同国家、学科和项目中的匿名用户交互序列中学习通用且可泛化的建模模式。在生成下一个命令推荐时，本方法实现了约84%的Recall@10。代码可在以下地址获取：https://github.com/dcy0577/BIM-Command-Recommendation.git。

Keyword:

Building Information Modeling (BIM)

Sequential recommendation

BIM logs processing

Transformer

Large language model (LLM)

Machine learning

期刊

Advanced Engineering Informatics

IF:

9.9

论文数:

4.5K

被引数:

1.7W

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暂无机构信息

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