Featured Research

CNNs for analyzing urban visual experience Previous studies have described the effects of urban attributes such as the Spatial Openness Index (SOI) on pedestrians’ experience. SOI uses 3-dimensional ray casting to quantify the volume of visible space from a single viewpoint. The higher the SOI value, the higher the perceived openness and the lower the…

Real-time Fabrication Analysis of Molds for Concrete Elements In his PhD. Thesis, Dr. Austern developed an analysis method which evaluates the fabrication parameters of complex geometries. The method predicts the feasibility, material use, and machining time required for fabricating moulds. It achieves this by interrogating geometric properties instead of the traditional machining simulations. Using the…

Automated Security Design Despite advances in computational design in the building sector, security system design is still based on outdated software and design paradigms. This leads to long design times as well as to inadequate system designs. In his role as Chief Science Officer for BeamUP, Dr. Austern developed a new system design method that…

Reading Legacy 2D plans with Machine Learning As Chief Science Officer of BeamUP, Dr. Austern invented and helped develop a unique process which converts unstructured, 2D CAD files to intelligent 3-dimensional models which represent building geometry. BeamUP’s process combines geometric and statistical Heuristic algorithms, with state-of-the-art Machine Learning models. The process can automatically identify space…

CGANs for generating architectural interior designs In interior space planning, the furnishing stage usually entails manual iterative processes. Machine learning has the potential to automate and improve these processes while maintaining creativity and quality. The aim of this study is to develop a furnishing method that leverages machine learning as a means for enhancing design…

Digital Automation for Custom-to-fit 3D Weft-Knitted Garments Automated flatbed weft-knitting technology holds the potential to significantly increase product variety, allow for customization, and reduce production stages. Computer numerically Controlled (CNC) knitting machines can produce a knit-to-wear garment, tailored to one’s specific size, based on given digital parameters. This research aims to POC a new 3D…

Applying Graph Neural Networks on BIM data Automatic Classification of building elements using Machine Learning (ML) is a powerful technique for constructing, enriching, and validating Building Information Models (BIM). We propose improving existing classification methods by introducing contextual information into the model via Graph Neural Networks (GNNs).  In this approach, we build a graph data…