Predicting semantic building information (BIM) with Recurrent Neural Networks

Recent advances in technology established artificial intelligence (AI) as a crucial domain of computer science for industry, research and everyday life. Even though computer-aided architectural design (CAAD) and digital semantic building models (BIM) are essential aspects of the contemporary architectural design process, the acquisition of proper data proves challenging and AI methods are absent in established design software. An option to acquire rich data are design protocol studies sequenced through meaningful relations. However, this data requires a framework for pre-processing and training artificial neural networks (ANN). In this paper, we present our research on BIM and AI for autocompletion through suggesting further design steps to improve the design process of the early design stages, based on the methods of the 'metis' projects. We propose a recurrent neural network (RNN) model to predict future design phases through sequential learning of cognitive sequences, utilising enriched sketch protocol data.