Computational Methods and Artificial Intelligence in The Architectural Pre-Design Process (Case Study: House Design)


  • Widi Cahya Yudhanta University Technology of Yogyakarta
  • Irwan Yudha Hadinata Lambung Mangkurat University



Architectural computing, Space Syntax , Artificial Intelligence, Design Method, Stable Diffusion


The current development of information technology infrastructure has positively impacted the ease with which people can carry out work activities physically and creatively. Artificial intelligence, which is currently starting to penetrate the world of visual design and architecture, is starting to develop from integrating design methods to methods of presenting or communicating architecture. In this study, aspects of artificial intelligence are used as a pre-design method, which is considered to help speed up decisions in design communication. The problem raised in this study is how optimal the involvement of computing and artificial intelligence is in providing pre-design considerations in the case of simple residential houses, especially in the analysis, space, and visual aspects. This study aims to obtain the methods and influence of computing and artificial intelligence in formulating pre-designs and generating understanding regarding the methods of their use. Several analysis tools are used for the spatial approach: the space syntax using DepthmapX software, solar analysis using the Shadedat plugin, and the visual shape of the building using Stable Diffusion. This research uses an experimental approach to obtain the desired design results. This study's findings explain that applying appropriate computational and artificial intelligence methods will produce an effective and efficient design process. With the correct sequence, it will have a measurable design process that can be assessed well.


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How to Cite

W. C. Yudhanta and I. Y. . Hadinata, “Computational Methods and Artificial Intelligence in The Architectural Pre-Design Process (Case Study: House Design)”, JARINA, vol. 3, no. 1, pp. 53–60, Feb. 2024.