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Research Status and Prospect of Machine Learning in Construction 3D Printing (2023-02)

10.1016/j.cscm.2023.e01952

Geng Songyuan, Luo Qiling, Liu Kun, Li Yunchao, Hou Yuchen,  Long Wujian
Journal Article - Case Studies in Construction Materials

Abstract

3D printing brings new opportunities for the development of civil engineering. Machine learning (ML) is also widely used in various fields of 3D printing. To describe the main research issues and potential future applications of ML for construction 3D printing, it is essential to comprehensively overview the current research topics. This paper aims to review, summarize, analyze, and introduce the research progress of ML applications in construction 3D printing, followed by a discussion of the current challenges and future research scope. Firstly, the classification and working principle of 3D printing and ML technology were introduced. Secondly, in the aspects of the design of construction materials, control of printing process, and quality inspection of construction components, the application status of ML in construction 3D printing was discussed. Based on this, in terms of interlayer bond performance enhancement, real-time status monitoring, and anisotropic behavior control, the future potential and challenges of ML in construction 3D printing were summarized, to provide a reference for promoting the realization of high-efficiency, intelligence, and sustainability in the field of civil engineering.

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BibTeX 
@article{geng_luo_liu_li.2023.RSaPoMLiC3P,
  author            = "Songyuan Geng and Qiling Luo and Kun Liu and Yunchao Li and Yuchen Hou and Wujian Long",
  title             = "Research Status and Prospect of Machine Learning in Construction 3D Printing",
  doi               = "10.1016/j.cscm.2023.e01952",
  year              = "2023",
  journal           = "Case Studies in Construction Materials",
}
Formatted Citation

S. Geng, Q. Luo, K. Liu, Y. Li, Y. Hou and W. Long, “Research Status and Prospect of Machine Learning in Construction 3D Printing”, Case Studies in Construction Materials, 2023, doi: 10.1016/j.cscm.2023.e01952.

Geng, Songyuan, Qiling Luo, Kun Liu, Yunchao Li, Yuchen Hou, and Wujian Long. “Research Status and Prospect of Machine Learning in Construction 3D Printing”. Case Studies in Construction Materials, 2023. https://doi.org/10.1016/j.cscm.2023.e01952.