Summary of Generative Ai Application For Building Industry, by Hanlong Wan et al.
Generative AI Application for Building Industry
by Hanlong Wan, Jian Zhang, Yan Chen, Weili Xu, Fan Feng
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Image and Video Processing (eess.IV); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper examines the potential of large language models (LLMs) to transform the building industry. It explores how LLMs can be applied in areas such as energy code compliance, building design optimization, and workforce training. The study highlights how these AI tools can automate labor-intensive processes, improving efficiency, accuracy, and safety in building practices. Additionally, it addresses challenges associated with interpreting complex visual and textual data in architectural plans and regulatory codes, proposing innovative solutions for AI-driven compliance checking and design processes. Furthermore, the paper discusses the broader implications of AI integration, including the development of AI-powered tools for comprehensive code compliance across various regulatory domains and the potential for AI to revolutionize workforce training through realistic simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence (AI) can change the building industry. It shows how a special type of AI called large language models can help with things like making sure buildings follow energy codes, designing better buildings, and training workers. The study finds that these AI tools can make processes faster, more accurate, and safer. It also talks about some challenges in understanding complex data in architectural plans and regulatory codes, and suggests new ways to use AI for compliance checking and design. Finally, the paper thinks about what it means for the future of building with AI, like making training simulations more realistic. |
Keywords
» Artificial intelligence » Optimization
