GAN-based method for generative design of visual comfort in underground space

Abstract

Existing methods for the generative design of architecture mainly focus on layout planning, and the genetic algorithm is popular in related research. However, the genetic algorithm is not available for non-quantitative problems (e.g., visual comfort) and inefficient for complex problems. In response, recent studies used the generative adversarial network (GAN) as the generative model to realize efficient generation. However, existing GAN-based methods for generative design are not effective enough because the output of their models is a two-dimensional image, which must be reconstructed to a three-dimensional (3D) model in practical engineering applications. In this study, a visual comfort generative network (VCGN) based on parametric modeling and a semi-supervised generative adversarial network (SGAN) is proposed for a more effective generation. The VCGN contains three parts, namely the parametric and teacher models and the SGAN. The parametric model is designed to generate a 3D underground space model by controlling the key parameters. The teacher model rates the visual comfort level of the generated model and trains the SGAN and could be a neural network or another criterion. The SGAN is designed to learn parameter distribution and generate parameters. Different from the SGAN mentioned by Odena, the SGAN used herein comprises four parts: two generators, a discriminator, and a classifier. The first generator is designed to generate parameters, while the second is used to simulate the parametric modeling process. The discriminator is supposed to distinguish simulated results from real results. The classifier is designed to classify the visual comfort levels. Moreover, a spatial color-generation task in underground space is applied as a case study to validate the effectiveness of the VCGN. A comparison of the random sampling method and the existing GAN-based method shows that the VCGN could generate more high-comfort underground space models than other methods for the same amount of time. In addition, a comparison of the underground space models under different comfort levels generated by the VCGN illustrates that the VCGN has the potential to generate an underground space model with a specified comfort level.