Geometry and Topology: Building Machine Learning Surrogate Models with Graphic Statics Method

This dissertation aims at developing a machine learning workflow in solving design-related problems, taking a data-driven structural design method with topological data using graphic statics as an example. It shows the advantages of building machine learning surrogate models for learning the design topology – the relationship of design elements. It reveals a future tendency of the coexistence of the human designer and the machine, in which the machine learns the appearance and correlation between design data, while the human supervises the learning process.

Theoretically, with the commencement of the age of Big Data and Artificial Intelligence, the usage of machine learning in solving design problems is widely applied. The existing research mainly focuses on the machine learning of the geometric data, however, the internal logic of a design is represented as the topology, which describes the relationship between each design element. The topology can not be easily represented for the human designer to understand, however it’s readable and understandable by the machine, which suggests a method of using machine learning techniques to learn the intrinsic logic of a design as the topology.

Technically, we propose to use machine learning as a framework and graphic statics as a supporting method to provide training data, suggesting a new design methodology by the machine learning of the topology. Different from previous geometry-based design, in which only the design geometry is presented and considered, in this new topology-based design, the human designer employs the machine and provides training materials showing the topology of a design to train the machine. The machine finds the design rules related to the topology and applies the trained machine learning models to generate new design cases as both the geometry and the topology. © 2022 Hao Zheng.