Machine-learning assisted topology optimization for architectural design with artistic flavor

Abstract

A machine-learning assisted topology optimization approach is proposed for architectural design with artistic flavor. This work establishes a novel framework to systematically integrate structural topology optimization with subjective human design preferences. To embed artistic flavor into the design, neural style transfer technique is adopted for measuring and generating the prior knowledge from a reference image with concerned artistic flavor. With the use of different convolutional layers in the VGG-19 (Visual Geometry Group) model-based CNN (Convolutional Neural Network), both style and content of the artistic flavor from low to high levels of abstraction can be constructed. Then, the measured knowledge can be integrated into pixel-based topology optimization as a formal similarity constraint. Both 2D and 3D problems are solved to illustrate the effectiveness of the proposed approach where inheritance of artistic heritage can be achieved in a systematic manner.