A Conceptual Decision Tree for the Automation of Architecture Applied to Housing

Abstract

In 1994, the Nelson Mandela government identified the lack of housing as the most severe social problem faced by South Africa. However, housing information is often inaccessible to those affected by inadequate low-cost housing. The right information at the right time has the potential to not only address the inadequacy of low-cost housing but also to address, in part, other social ills such as unemployment, poverty and government corruption, as well as larger issues such as the climate emergency. This paper presents a conceptual decision tree to govern the knowledge management of architectural information for the possible automation of architecture. Knowledge management, big data and machine learning are the precursors of artificial intelligence, a technology that could further aid in addressing the inadequacy of low-cost housing. A decision tree is the first step. For this paper, the information within frames forms the nodes on the conceptual decision tree. This decision tree is presented graphically and tested hypothetically on a low-cost housing unit. The research findings indicate that there is a noteworthy overlap in architectural information of low-cost housing information presented thereby validating the possible benefit of architectural information knowledge management.

A comparison of low-cost housing units for varying climatic regions in South Africa: a knowledge management approach

“…A decision tree acts as a decision support tool with nodes that demarcate variables (Kami nski et al, 2017). A decision tree in which decisions in the architectural process become nodes is argued as the possible first step towards managing knowledge and eventually automating architecture (van Tonder and Brink, 2022).…”