In light of the emergence of the concept of artificial intelligence (AI) and its various applications, it has become imperative to seek different solutions to address the accumulation of problems and deficiencies in residential buildings and avoid traditional periodic maintenance practices. This research fundamentally discusses the role of AI in achieving sustainable maintenance through its strategies to extend the assumed lifespan of the building and preserve residential real estate assets. This research assumes that the implementation of intelligent exploratory methods offers an effective solution by enabling unsupervised algorithms to efficiently find deviants from a large amount of data. AI is beneficial for both real-time and post-event processing. Where it can be applied to activate large amounts of data and have a chance of making sense of the data, various solutions can be achieved for sustainable maintenance in residential buildings to bridge the gap between the different sustainable maintenance programmes and their actual application, which is the primary objective of the research. The researcher will employ a theoretical framework to elucidate the concepts of sustainable maintenance, the assumed lifespan of a building, and AI applications. Subsequently, an analytical framework was used to analyze various international projects (the case studies) and identify the various strengths resulting. then will conclude by employing an applied framework to facilitate the implementation of smart exploratory methods through the benefits of results and recommendations to achieve sustainable maintenance, prolong the assumed lifespan of the building, and preserve residential real estate assets.
 
Special Issue of AEIC 2024 (Sustainable Architecture and Urban Design Session)