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PhD Defense Graph-Representation Learning for Human-Centered Analysis of Building Layouts
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Speaker: Vahid Azizi

Location : Remote via Webex

Committee: 

Prof. Mubbasir Kapadia (chair)

Prof. Mridul Aanjaneya

Prof. Gerard de Melo

Prof. M. Brandon Haworth

Event Type: PhD Defense

Abstract: Floorplans are a standard representation of building layouts. Computer-Aided Design (CAD) applications and existing Building Information Modeling tools rely on simple floorplan representations which are not amenable to automation (e.g., generative design), and do not account for how people inhabit and occupy the space -- two key challenges which must be addressed for intelligent human-aware building design. This thesis addresses these challenges by exploring the use of graph representation learning techniques to implicitly encode the latent state of floorplan configurations, which is more amenable to automation. Specifically, we use graphs as intermediate representation of floorplans. Rooms are nodes and edges indicate a connection between adjacent rooms, either through a door or passageway. The graphs are annotated with a variety of attributes which characterize the semantic, geometric, and dynamic properties of the floorplan with respect to human-centered criteria. To address the variation in dimensionality of graphs, we utilize an intermediate sequential representation (generated by random walks) which allow us to encode the graphical structure in a fixed-dimensional representation. We propose the use of RNN-based variational autoencoder architectures to embed attribute floorplans. We enhance graph-based representations of floorplans with human occupancy and behavior attributes which are extracted by statically analysing the floorplan geometry, and by running human behavior simulations on large datasets of real and procedurally generated synthetic floorplans. We explore the potential of these representations on a variety of tasks including finding semantically similar floorplans, floorplan optimization, and generative design. Our approach and techniques are extensively evaluated through a series of quantitative experiments, and user studies with expert architects to validate our findings. The qualitative, quantitative and user-study evaluations show that our embedding framework produces meaningful and accurate vector representations for floorplans. Our models and associated datasets have been made publicly available to encourage adoption and spark future research in the burgeoning research area of intelligent human-aware building design.

: 

https://rutgers.webex.com/rutgers/j.php?MTID=m8c804c3d35307d4262a12f0ebbc1e9ce
Tuesday, Dec 22, 2020 12:00 pm | 2 hours | (UTC-05:00) Eastern Time (US & Canada)
Meeting number: 120 843 3922
Password: WGjZNXei298
d48aa2baba7f48a8b2cbf88734375c26

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