AI enhancing prefabricated aesthetics and low carbon coupled with 3D printing in chain hotel buildings from multidimensional neural networks DOI Creative Commons
Gangwei Cai, Yin Lou,

Feidong Lu

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 17, 2025

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

Язык: Английский

AI enhancing prefabricated aesthetics and low carbon coupled with 3D printing in chain hotel buildings from multidimensional neural networks DOI Creative Commons
Gangwei Cai, Yin Lou,

Feidong Lu

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 17, 2025

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

Язык: Английский

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