Unveiling diverse AI applications in BIM: a quantitative mapping of techniques for 10 dimensions

The integration of Artificial Intelligence (AI) and Building Information Modeling (BIM) has attracted growing research interest, though studies remain concentrated on lower BIM dimensions, with limited attention to higher ones such as 9D (lean construction) and 10D (industrialized construction). This study systematically reviews 122 peer-reviewed articles to map AI applications across all ten BIM dimensions and project lifecycle phases using bibliometric, quantitative, and thematic analyses. Excel, VOSviewer, and NVivo were used for numerical, network, and qualitative analyses, respectively. The findings reveal that Machine Learning (ML) and Deep Learning (DL) are the most frequently used AI methods-appearing 87 and 82 times-mainly in 3D (72.1%) and 6D (23%) dimensions related to geometric modeling and sustainability. In contrast, 4D (time scheduling), 9D (lean), and 10D (industrialized construction) are critically underexplored. Lifecycle analysis shows the dominance of design and construction phases, while planning, maintenance, and end-of-life receive minimal focus. The study offers a comprehensive mapping of AI-BIM integration, highlights gaps in data-rich and sustainability-oriented areas, and suggests future research directions, such as integrating audio-visual data, transfer learning, and NLP or computer vision techniques to advance higher-dimensional and underrepresented lifecycle applications.

人工智能 (Artificial Intelligence) 与建筑信息模型 (Building Information Modeling) 的融合研究日益受到学界关注，但现有研究多集中于低维度BIM应用，对9D（精益建造）、10D（工业化建造）等高维度BIM的关注仍较为有限。本研究采用文献计量、定量与主题分析方法，对122篇同行评议论文开展系统性综述，旨在梳理人工智能在全部10个维度BIM及项目全生命周期各阶段的应用场景。研究分别采用Excel、VOSviewer与NVivo开展数值分析、网络分析与质性分析。结果显示，机器学习 (Machine Learning) 与深度学习 (Deep Learning) 是应用最广泛的人工智能技术，分别被提及87次与82次，且主要集中于与几何建模及可持续性相关的3D（72.1%）与6D（23%）维度。相较而言，4D（进度规划）、9D（精益建造）与10D（工业化建造）维度的研究仍严重不足。全生命周期分析结果表明，研究主要聚焦于设计与施工阶段，而规划、运维及项目终期阶段的相关研究极少。本研究全面梳理了人工智能与建筑信息模型融合的应用图景，指出了数据密集型与可持续性导向领域的研究空白，并提出了未来研究方向，例如融合音视频数据、迁移学习以及自然语言处理 (Natural Language Processing) 与计算机视觉 (Computer Vision) 技术，以推动高维度BIM及受关注不足的全生命周期应用研究发展。