Enhancing the Sustainability of AI Technology in Architectural Design: Improving the Matching Accuracy of Chinese-Style Buildings

Abstract

This study discusses the application of AI technology in the design of traditional Chinese-style architecture, aiming to enhance AI’s matching accuracy and sustainability. Currently, there are limitations in AI technology in generating details of traditional Chinese-style architecture, so this study proposes a method of fine-tuning AI pre-training models, by extracting samples of traditional architectural style elements, to enhance the trajectory and output accuracy of AI generation. The research method includes constructing AI pre-training models, using DreamBooth and ControlNet tools for personalized training and perspective control. Through experimental verification, this study found that pre-trained models can effectively enhance the accuracy and controllability of AI in the preliminary design of architecture. At the same time, the application of ControlNet technology has significantly improved the accuracy and realism of architectural rendering. The value of this study lies in proposing a new method that combines AI technology with the process of traditional Chinese architectural design, which can help architects better protect and inherit the culture of traditional Chinese architecture. Through this method, it can reduce the difficulty of learning traditional Chinese architectural design, optimize the design process, enhance design efficiency, and provide strong support for the sustainable development of traditional Chinese architecture.