Timber joints analysis and design using shape and graph grammar-based machine learning approach: synthetic data preparation of timber joints’ shape and graph grammar for machine learning application

Timber joints had been applied as one of the primary methods across different cultures of building construction. The technique of crafting timber joints uses simple geometry to connect different components without the need of adhesives or fixings. Digitalisation and computational design method provided a new approach to developing complex timber joint connections. By combining this traditional technique with computational design methods, shape, and graph grammar opened new opportunities in reinterpreting timber joint designs. In this paper, we proposed a timber joints' synthetic dataset preparation using shape grammar and graph grammar for machine learning applications. The research focused on designing a prototype of a shape grammar extraction system and graph extraction system manually and using Topologic in Sverchok, Blender, with a discussion on how to shape grammar applications help to analysis and create a larger database for future machine learning development of this project.