In building design, the structural design is usually incorporated in the latter stages of the design, and the computational tools currently available focus more on converging and optimizing a single solution rather than providing an opportunity for the designer to explore design ideas. Incorporating structural design in the initial design phases can lead to more efficient and cost-effective structures, but in order to do so practical tools should be available to the designer to deepen the design space and allow them to efficiently explore it. Generative AI models could provide a solution to the problem and expand the design space by learning from the data provided. This thesis explores such a solution by means of training an AI model (Variational Autoencoder) on an artificial dataset of 2D lattice patterns. To do that, a dataset of 6 unique 2D lattice patterns is created and used to train a simple VAE model. The methodology used in this thesis is to validate if an AI can aid in the exploration of design ideas by providing a larger design space with newly generated data that contain learned features from the dataset rather than through constraints set by the designers. The results show that the VAE model can learn features and provide a greater diversity of design than the original dataset through newly generated designs. The output of the VAE model in this thesis is then explored for possible integrations into the design process for the early stages of design. For this, the generated patterns that are distinct and unique are identified and applied to a shell structure to explore topology design ideas.