Automation in Construction (2000–2023): Science Mapping and Visualization of Journal Publications

This paper presents a scientometric review that provides a quantitative perspective on the evolution of Automation in Construction Journal (AICJ) research, emphasizing its developmental paths and emerging trends. The study aims to analyze the journal’s growth and citation impact over time. It also s...

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Bibliographic Details
Published in:Buildings (Basel) Vol. 15; no. 15; p. 2789
Main Authors: Marzouk, Mohamed, Bin Mahmoud, Abdulrahman A., Al-Gahtani, Khalid S., Adel, Kareem
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2025
Subjects:
Analysis
Artificial intelligence
Automation
Automation in Construction
Bibliographic records
Bibliometrics
Biblioshiny
Business
Citation analysis
Citations
Civil engineering
Co authorship
Collaboration
Computer vision
Construction
Cooperation
Deep learning
Digitization
Documents
Evolution
Keywords
Knowledge management
knowledge structure
Learning algorithms
Machine learning
Machine vision
Optimization
Quantitative analysis
Rankings
Research methodology
Robotics
Science
Scientometrics
Software
Software utilities
Three dimensional printing
Trends
VOSviewer
Colombia
United States
Colorado
China
United Kingdom
British Columbia
Australia
ISSN:2075-5309, 2075-5309
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Summary:This paper presents a scientometric review that provides a quantitative perspective on the evolution of Automation in Construction Journal (AICJ) research, emphasizing its developmental paths and emerging trends. The study aims to analyze the journal’s growth and citation impact over time. It also seeks to identify the most influential publications and the cooperation patterns among key contributors. Furthermore, the study explores the journal’s primary research themes and their evolution. Accordingly, 4084 articles were identified using the Web of Science (WoS) database and subjected to a multistep analysis using VOsviewer version 1.6.18 and Biblioshiny as software tools. First, the growth and citation of the publications over time are inspected and evaluated, in addition to ranking the most influential documents. Second, the co-authorship analysis method is applied to visualize the cooperation patterns between countries, organizations, and authors. Finally, the publications are analyzed using keyword co-occurrence and keyword thematic evolution analyses, revealing five major research clusters: (i) foundational optimization, (ii) deep learning and computer vision, (iii) building information modeling, (iv) 3D printing and robotics, and (v) machine learning. Additionally, the analysis reveals significant growth in publications (54.5%) and citations (78.0%) from 2018 to 2023, indicating the journal’s increasing global influence. This period also highlights the accelerated adoption of digitalization (e.g., BIM, computational design), increased integration of AI and machine learning for automation and predictive analytics, and rapid growth of robotics and 3D printing, driving sustainable and innovative construction practices. The paper’s findings can help readers and researchers gain a thorough understanding of the AICJ’s published work, aid research groups in planning and optimizing their research efforts, and inform editorial boards on the most promising areas in the existing body of knowledge for further investigation and development.
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ISSN:2075-5309
2075-5309
DOI:10.3390/buildings15152789