Exploring Gen-AI applications in building research and industry: A review

Hanlong Wan, Jian Zhang, Yan Chen, Weili Xu, Fan Feng
10/1/2024
cs.AIeess.IVeess.SY

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Abstract

This paper investigates the transformative potential of Generative AI (Gen-AI) technologies, particularly large language models, within the building industry. By leveraging these advanced AI tools, the study explores their application across key areas such as automated compliance checking and building design assistance. The research highlights how Gen-AI can automate labor-intensive processes, significantly improving efficiency and reducing costs in building practices. The paper first discusses the two widely applied fundamental models-Transformer and Diffusion model-and summarizes current pathways for accessing Gen-AI models and the most common techniques for customizing them. It then explores applications for text generation, such as compliance checking, control support, data mining, and building simulation input file editing. Additionally, it examines image generation, including direct generation through diffusion models and indirect generation through language model-supported template creation based on existing Computer-Aided Design or other design tools with rendering. The paper concludes with a comprehensive analysis of the current capabilities of Gen-AI in the building industry, outlining future directions for research and development, with the goal of paving the way for smarter, more effective, and responsive design, construction, and operational practices.

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Cite this paper

@article{wan2024exploring,
  title  = {Exploring Gen-AI applications in building research and industry: A review},
  author = {Hanlong Wan and Jian Zhang and Yan Chen and Weili Xu and Fan Feng},
  year   = {2024},
  eprint = {2410.01098},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.01098v2}
}

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