Publication
Abstract
The paper is an extensive review looking at the convergence of Building Information Model (BIM) with deep learning (DL) in the digital transformation of architecture. As BIM evolves from a 3D model-centric design tool to a knowledge-based decision support system, the fusion with deep learning will provide strong support for intelligent automation, predictive analytics, and semantic understanding. We systematically evaluate the hybrid approaches of deep learning for design optimization, semantic segmentation, anomaly detection, energy modeling, construction scheduling, and lifecycle management in this paper. A concluding section of the article is devoted to trendy aspects such as multimodal data fusion, generative models, model-based interoperability, and digital twin alignment. With this integration, BIM transforms into a self-adapting, real-time system that assists decision-makers in making informed choices during design and construction, and also in operating and maintaining the building over its long life, thereby changing architectural processes and enabling more sustainable, efficient, and resilient buildings.
Keywords
Building Information Modeling (BIM) , Deep Learning , Architectural Digital Transformation , Semantic Segmentation , Design Optimization , Digital Twin , Anomaly Detection , Predictive Maintenance , Energy Modeling , Lifecycle Management .
Metadata
Disciplines: Artificial Intelligence Technology
Subjects: Machine Learning
Cite This Article
APA Style
Yang, L. (2025). A comprehensive review of bim and deep learning integration in innovative practices for architectural digital transformation. Journal of Industrial Engineering and Applied Science, 3(3), 23-31. https://doi.org/10.70393/6a69656173.333030
Acknowledgments
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
FUNDING
INSTITUTIONAL REVIEW BOARD STATEMENT
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
INFORMED CONSENT STATEMENT
CONFLICT OF INTEREST
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
AUTHOR CONTRIBUTIONS
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