(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
Single-family housing represents a critical sector in urban development, where the structural safety of foundations is essential to ensure habitability and the service life of buildings. This article presents a narrative review of the state of the art focused on the evolution of strip footing design methodologies, transitioning from empirical methods to advanced computational tools. The limitations of traditional construction practices and the accessibility barriers of current commercial software are analyzed. Likewise, the emerging potential of artificial intelligence and automated calculation is examined as solutions to optimize structural and economic efficiency. The findings suggest the existence of a technological gap in the social housing sector, which can be mitigated through the development of customized, low-cost tools that integrate machine learning techniques.