Multidimensional ergonomic latent modeling of prefabricated adaptive decoration by robotic 3D printing

Abstract Chain hotels constitute 40.09% of all hotels, with indoor room decorations typically undergoing replacement cycles every 2 to 5 years. Robotic 3D printing-enabled prefabrication offers transformative potential for sustainable construction, combining precision engineering with environmental consciousness. This study explores its application in the hospitality sector, addressing both sustainability and ergonomic data. Using AI-driven multidimensional ergonomic latent modeling, key environmental factors such as sound insulation, lighting adaptability, and air quality are evaluated for their impact on service flexibility, safety, and user experience. By aligning technological innovation, this research provides a latent model for advancing prefabricated building adaptive decoration and low-carbon hotel design. The study underscores the importance of interdisciplinary efforts to achieve global sustainability goals and redefines prefabrication technologies, especially robotic 3D printing prefabricated decoration, as catalysts for operational excellence and transformative industry standards in sustainable hospitality. Moreover, this work extends the exploration of robotic 3D printing prefabricated solutions to the context of extreme environments and even extraterrestrial buildings, such as building habitats on Mars or the Moon, contributing valuable human factors engineering insights for future adaptive built environment research in these challenging settings.