This paper aims to explore alternative representations of the physical architecture using its real-world sensory data through Artificial Neural Networks (ANNs), which is a simulated form of cognition having the ability to learn. In the project developed for this research, a detailed 3-D point cloud model is produced by scanning a physical structure with LiDAR. Then, point cloud data and mesh models are divided into parts according to architectural references and part-whole relationships with various techniques to create datasets. A Deep Learning Model is trained using these datasets, and new 3-D models produced by Deep Generative Models are examined. These new 3-D models, which are embodied in different representations, such as point clouds, mesh models, and bounding boxes, are used as a design vocabulary, and combinatorial formations are generated from them.