The complexity of BIM software presents significant barriers to the widespread adoption of BIM and model-based design within the Architecture, Engineering, and Construction (AEC) sector. End-users frequently express concerns regarding the additional effort required to create a sufficiently detailed BIM model when compared with conventional 2D drafting. This study explores the potential of sequential recommendation systems to accelerate the BIM modeling process. By treating BIM software commands as recommendable items, we introduce a novel end-to-end approach that predicts the next-best command based on user historical interactions. Our framework extensively preprocesses real-world, large-scale BIM log data, utilizes the transformer architectures from the latest large language models as the backbone network, and ultimately results in a prototype that provides real-time command suggestions within the BIM authoring tool Vectorworks. Subsequent experiments validated that our proposed model outperforms the previous study, demonstrating the immense potential of the recommendation system in enhancing design efficiency.

翻译：BIM软件的复杂性严重阻碍了建筑、工程与施工（AEC）领域对BIM及基于模型设计的广泛采用。与传统二维绘图相比，终端用户普遍担忧创建足够详细的BIM模型需要额外投入大量精力。本研究探索了序列推荐系统在加速BIM建模过程中的潜力。通过将BIM软件命令视为可推荐项，我们提出了一种新颖的端到端方法，该方法基于用户历史交互预测最优后续命令。我们的框架对真实世界的大规模BIM日志数据进行了深度预处理，采用最新大语言模型中的Transformer架构作为主干网络，最终开发出可在Vectorworks建模工具内提供实时命令建议的原型系统。后续实验验证表明，所提模型性能优于既有研究，充分证明了推荐系统在提升设计效率方面的巨大潜力。