Applications and Evaluation of AI Technologies in the Renovation of Old Buildings in Urban Centers

Abstract

Urban sprawl or expansion is a growing issue globally, and as technology continues to develop, the demand for residential areas continues to rise. Conflicts regarding the availability of resources for architecture also rise in rural areas; therefore, renovating old buildings in urban centers becomes an important concept. Since there are many challenges with renovating these buildings, the recent development of AI technologies will provide solutions to mitigate or reduce these challenges. This paper examines the application of AI technologies in architecture and how they contribute to redeveloping old buildings in urban areas. Technologies such as conditional Generative Adversarial Networks and deep neural networks can assist architects in standardizing floor plans and generating design modules. In addition, these technologies analyze data from existing buildings and, thus, can create the best possible plan for a building. While these technologies focus on simplifying the traditional process of architectural planning in areas such as generating programs and creating partitions, B-SMART reference architecture approaches architecture from the perspective of smart home building, therefore, implementing AI technologies into the lives of the residents. Furthermore, with AI technologies, future homes are created to be energy efficient and sustainable. Therefore, the application of AI will improve the sustainability of homes and overall benefit the environment.