A Review of the Development of Automated Metal Parts Modeling and Management Technologies Based on BIM and Artificial Intelligence

Zhuoxuan Li

doi:10.70393/6a69656173.333839

Authors

Zhuoxuan Li Suzhou University of Science and Technology

DOI:

https://doi.org/10.70393/6a69656173.333839

ARK:

https://n2t.net/ark:/40704/JIEAS.v4n1a02

Disciplines:

Artificial Intelligence Technology

Subjects:

Machine Learning

References:

42

Keywords:

Building Information Modeling, Artificial Intelligence, Metal Components, Point Cloud Semantic Segmentation, Scan-to-BIM

Abstract

Metal components, due to their high prefabrication rate and discrete manufacturing characteristics, have become an ideal carrier for verifying intelligent construction technologies. However, traditional modeling methods face bottlenecks such as low efficiency, poor robustness, and semantic gaps. This study systematically reviews the research progress of Building Information Modeling (BIM) and Artificial Intelligence (AI) in the automated modeling and life-cycle management of metal components. First, it elucidates the semantic support for metal structures in the IFC 4.3 standard and the theoretical basis of BIM-AI integration. Then, from a forward design perspective, it reviews parametric modeling, deep learning topology optimization, and generative adversarial networks (GANs), while from a reverse reconstruction perspective, it outlines point cloud semantic segmentation and Scan-to-BIM automation technologies. Further, it explores intelligent management methods such as knowledge graph compliance checks, digital twin frameworks, and visual defect detection. Finally, it points out key challenges and future development directions, including data heterogeneity, scarce annotations, and interpretability.

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Author Biography

Zhuoxuan Li, Suzhou University of Science and Technology

School of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.

References

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