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Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities

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Darko, Amos, Chan, Albert P.C., Adabre, Michael A., Edwards, David J., Hosseini, M. Reza, Ameyaw, Ernest E.·2020·Automation in Construction
DOI 10.1016/j.autcon.2020.103081
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Abstract

Abstract The Architecture, Engineering and Construction (AEC) industry is fraught with complex and difficult problems. Artificial intelligence (AI) represents a powerful tool to assist in addressing these problems. Therefore, over the years, researchers have been conducting research on AI in the AEC industry (AI-in-the-AECI). In this paper, the first comprehensive scientometric study appraising the state-of-the-art of research on AI-in-the-AECI is presented. The science mapping method was used to systematically and quantitatively analyze 41,827 related bibliographic records retrieved from Scopus. The results indicated that genetic algorithms, neural networks, fuzzy logic, fuzzy sets, and machine learning have been the most widely used AI methods in AEC. Optimization, simulation, uncertainty, project management, and bridges have been the most commonly addressed topics/issues using AI methods/concepts. The primary value and uniqueness of this study lies in it being the first in providing an up-to-date inclusive, big picture of the literature on AI-in-the-AECI. This study adds value to the AEC literature through visualizing and understanding trends and patterns, identifying main research interests, journals, institutions, and countries, and how these are linked within now-available studies on AI-in-the-AECI. The findings bring to light the deficiencies in the current research and provide paths for future research, where they indicated that future research opportunities lie in applying robotic automation and convolutional neural networks to AEC problems. For the world of practice, the study offers a readily-available point of reference for practitioners, policy makers, and research and development (R&D) bodies. This study therefore raises the level of awareness of AI and facilitates building the intellectual wealth of the AI area in the AEC industry.

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Metadata

Authors
Darko, A., Chan, A. P. C., Adabre, M. A., Edwards, D. J., Hosseini, M. R., & Ameyaw, E. E.
Venue
Automation in Construction
Publisher
Elsevier BV
Published
2020-04
Volume
112
Pages
103081
ISSN
0926-5805 (ISSN-L)
DOI
10.1016/j.autcon.2020.103081
OpenAlex
W2999075180
Cited by
603
References
6
FWCI
38.40
Type
journal article
Language
en
Open access
green
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
Hong Kong Polytechnic University

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CoChat. (2026). Citation check: Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. https://app.cochat.ai/papers/10.1016/j.autcon.2020.103081
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Darko, A., Chan, A. P. C., Adabre, M. A., Edwards, D. J., Hosseini, M. R., & Ameyaw, E. E. (2020). Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in Construction. https://doi.org/10.1016/j.autcon.2020.103081

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