AI enhancing prefabricated aesthetics and low carbon coupled with 3D printing in chain hotel buildings from multidimensional neural networks

Abstract

Abstract There are approximately 70,000 economy chain hotels worldwide, generating about 300 million tons of carbon dioxide annually. While reducing carbon emissions can lower energy consumption, these hotels must also continually attract guests to ensure revenue growth and achieve sustainable development. This study focuses on the application of Artificial Intelligence (AI) in the prefabricated renovation of hotels, investigating how AI plays a crucial role in coupling low-carbon construction and aesthetic design. Using multidimensional algorithms within machine learning (ML), neural networks (NN), and statistical modeling (SM), this paper analyzes the impact of AI-driven prefabricated room renovations on tourist satisfaction and carbon emissions. The results indicate that AI can not only optimize energy consumption and structural efficiency in the renovation process but also achieve low-carbon goals while maintaining high-quality aesthetic designs. This study offers new theoretical insights into the integration of low-carbon and aesthetic design, filling gaps in the current literature, providing a pathway for achieving sustainable development goals (SDG 7, 8, and 12), and offering valuable implications for robotic intelligent construction and 3D printing in prefabricated buildings industry.