Deep Learning in the Architecture, Engineering, and Construction (AEC) Industry: Methods, Challenges, and Emerging Opportunities

In recent years, deep learning (DL) has emerged as a transformative technology with significant potential to advance the Architecture, Engineering, and Construction (AEC) industry. DL enables automation, intelligent decision-making, and predictive analytics across various phases of construction, including design, site monitoring, safety management, and facility operations. Despite its growing adoption, research on the comprehensive methods, practical challenges and emerging opportunities of DL in the AEC industry remains limited. This study presents a state-of-the-art review of DL applications in the AEC industry by focusing on key methods, challenges, emerging opportunities and future research directions. A systematic literature review (SLR) was conducted in this study. Three major DL methods applied in the AEC industry were examined: (i) data-driven computer vision, (ii) natural language processing (NLP), and (iii) generative and simulation-based methods. Key challenges were identified: (i) data scarcity issues, (ii) high computational requirements, (iii) limited generalization across projects, (iv) human factors and resistance to adoption, and (v) lack of standardization and interoperability. Additionally, emerging opportunities and future research directions are highlighted: (i) advanced construction site monitoring and safety management, (ii) automated design and generative modeling, (iii) predictive maintenance and facility management, (iv) integration with robotics and autonomous construction systems, and (v) smart project management and decision support systems. This study advances a holistic understanding of DL in the AEC industry by systematically synthesizing current methods, challenges, and emerging trends. It establishes a structured foundation for future research to overcome technical, practical, and organizational challenges, thereby supporting the scalable, intelligent, and sustainable transformation of construction practices.