Abstract
3D printing is evolving at a fast pace in both the manufacturing and construction sectors. These advancements can greatly benefit these industries. However, the 3D printing of concrete structures presents some challenges due to defects in the 3D concrete printed elements. Hence, this study systematically reviews Artificial Intelligence (AI)-driven techniques, such as Computer Vision and Machine Learning, to identify surface defects that can occur in 3D-printed cementitious material structures. The adopted methodology was the PRISMA statement with the aim of reporting the systematic review and meta-analysis. Two well-known databases, Web of Science and Scopus, were utilised for data extraction of articles published during the past 10 years, between 2014 and May 2025. The initial search provided 110 articles, both conference and journal papers; after screening, only 11 were left for the final review assessment. The smaller number of the final articles shows that much work is still needed in this area. It has been observed that various computer vision and machine learning-based methodologies were employed to classify defects in 3D concrete printed structures. Deep learning algorithms, such as YOLO and RT-DETR, were featured as the most efficient in real-time defect detection and quality monitoring. It was also observed that real-time monitoring systems attached to 3D printers help in reducing the material wastage, which is essential to meet the sustainable goals. However, more work is still required to underline the defects of 3D-printed cementitious material, probably with the involvement of AI image processing tools and techniques. This can help to automate the defects in 3D-printed structures, and by this, the productivity could be enhanced.
Recommended Citation
M. A. Musarat et al., "Computer Vision and Machine Learning Approaches for Defect Detection In 3D-Printed Cementitious Materials: A Systematic Review," Infrastructures, vol. 11, no. 5, article no. 159, MDPI, May 2026.
The definitive version is available at https://doi.org/10.3390/infrastructures11050159
Department(s)
Civil, Architectural and Environmental Engineering
Publication Status
Open Access
Keywords and Phrases
additive manufacturing; artificial intelligence; cementitious materials; computer vision; defects; machine learning
International Standard Serial Number (ISSN)
2412-3811
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2026 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 May 2026
Included in
Civil Engineering Commons, Engineering Education Commons, Materials Science and Engineering Commons, Structural Engineering Commons, Transportation Engineering Commons
Comments
Missouri University of Science and Technology, Grant None