摘要

智能建造(IC) 已成为集成人工智能 (AI) 和物联网 (IoT) 等先进信息技术来推动建筑业转型升级的新范式和新趋势。 然而，由于智能建造的跨学科特性，相关文献分散在不同的资料来源。因此，为全面了解智能建造的最新研究进展和未来机遇，并分析智能建造在发展中国家和发达国家的异同，本研究使用VOSviewer和Gephi对21世纪以来智能建造相关的文献进行了系统综述和对比分析。研究首选通过关键词筛选，从Web of Science (WOS)中搜索确定了2788篇相关文献，并通过共被引分析和合著分析等手段对论文进行了分析。 同时，为了分析发展中国家与发达国家之间智能建造技术发展的差异，研究选择中国作为发展中国家代表，通过共现分析对比了发展中国家中国作为与发达国家的文献差异。研究同时在基础研究与领域知识迁移，信息感知、融合与决策支持，以及具身AI等几方面定量分析文献的异同。最后，研究同时指出了四个未来的研究方向，即：1）数据融合与决策支持，2）知识表示、学习和利用，3）领域大模型构建与应用，以及4）可根据决策结果采取行动的具身AI。本研究为不同发展水平国家的建筑业学者、从业者提供了智能建造的相关文献概览与研究脉络，并给出了智能建造未来发展的建议，对未来学术界及产业界智能建造发展具有显著的积极意义。

Intelligent construction (IC) has emerged as a new approach to transforming the architecture, engineering, and construction (AEC) industry through the integration of advanced information technologies such as artificial intelligence (AI) and the Internet of Things (IoT). However, due to its interdisciplinary nature, the relevant documents on IC are diverse and fragmented. To provide a comprehensive understanding of research progress and future opportunities in IC and to offer suggestions for both developing and developed countries, this study employed VOSviewer and Gephi to conduct a comparative review of relevant literature from the 21st century. A keyword search of Web of Science (WOS) identified 2788 relevant documents which were subjected to an overall co-citation and co-authorship analysis. To illustrate the differences between developing and developed countries, China, a representative developing country, was taken as the candidate to be compared with developed countries via a co-occurrence analysis. Differences between China and developed countries in the three sub-directions of IC, research foundation and domain knowledge transformation; information perception, fusion, and decision making; and embodied AI, were qualitatively discussed. Finally, four future research directions were suggested: (1) data fusion and decision-making, (2) improving the accuracy and efficiency of knowledge representation, learning, and utilization, (3) the establishment of large, pre-trained models in the field, and (4) embodied AI for taking actions according to the decisions made. This paper provides an overview of the relevant literature and the IC context for practitioners and scholars in the AEC industry in countries with different levels of development, as well as suggestions for the future development of IC. The findings of this study can serve both academia and industry in promoting IC in the AEC industry.

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This research was funded by the National Natural Science Foundation of China, grant number 72091512, the National Key R&D Program of China, grant number 2018YFD1100900, the Research Project of China Civil Engineering Society (2023-05), and the Guangdong Science Foundation, grant numbers 2022A1515010174 and 2023A1515030169.

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