Abstract

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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