A Comprehensive Review of BIM and Deep Learning Integration in Innovative Practices for Architectural Digital Transformation

Lin Yang

doi:10.70393/6a69656173.333030

Authors

Lin Yang Beijing Institute of Architectural Design Co., Ltd

DOI:

https://doi.org/10.70393/6a69656173.333030

ARK:

https://n2t.net/ark:/40704/JIEAS.v3n3a03

Disciplines:

Artificial Intelligence Technology

Subjects:

Machine Learning

References:

27

Keywords:

Building Information Modeling (BIM), Deep Learning, Architectural Digital Transformation, Semantic Segmentation, Design Optimization, Digital Twin, Anomaly Detection, Predictive Maintenance, Energy Modeling, Lifecycle Management

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.

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Author Biography

Lin Yang, Beijing Institute of Architectural Design Co., Ltd

Beijing Institute of Architectural Design Co., Ltd, China.

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