Three decades of machine learning with neural networks in computer-aided architectural design (1990–2021)

Abstract

Abstract Over the past years, computational methods based on deep learning—that is, machine learning with multilayered neural networks—have become state-of-the-art in main research areas in computer-aided architectural design (CAAD). To understand current trends of CAAD with deep learning, to situate them in a broader historical context, and to identify future research challenges, this article presents a systematic review of publications that apply neural networks to CAAD problems. Research papers employing neural networks were collected, in particular, from CumInCad a major open-access repository of the CAAD community and categorized into different types of research problems. Upon analyzing the distribution of the papers in these categories, namely, the composition of research subjects, data types, and neural network models, this article suggests and discusses several historical and technical trends. Moreover, it identifies that the publications analyzed typically provide limited access to important research components used as part of their deep learning methods. The article points out the importance of sharing training experiments and data, of describing the dataset, dataset parameters, dataset samples, model, learning parameters, and learning results to support reproducibility. It proposes a guideline that aims at increasing the quality and availability of CAAD research with machine learning.