A project proposing new ways to design, it looks at the application of machine learning and optimisation to drive the investigation of design options.
Taking parametric generative systems as a starting point this project aims to identify and implement means for designers and also non-specialists to influence design by giving high-level feedback on concrete, interactively generated options.
This input is embedded in a larger "mixed-initiative" process which uses machine learning to model user preferences (both individual and collective) as well as using optimisation to produce high performance designs.
Project investigating the use of the web as a medium to allow for wider input on architectural schemes.
Utilising the massive capabilities of ubiquitous modern web technology this project explores the potential of web connected computational systems to undertake far reaching user participation for design proposals, option selection and change generation.
This project aims to develop especially tailored web interfaces and server back ends to help wider communities (end-users, local-residents, general public) have real impact on design beyond typical client input and public exhibitions, by providing a web platform that allow them to interact with, comment on and edit 3D environments.
We are actively engaged in a number of big data projects.
These focus on how big data can be leveraged as a design tool.
This is focused in two primary directions:
firstly using social data to find insight into relationships and trends between people and the design of the existing built environment.
Secondly using big data to propose and tune new and novel design solutions based on existing data.
Currently this research focuses on fusing spatial mapping and social media data. This enables generates relationships between the two and enables more objective verifiable correlations between design configurations and social use and engagement with space. Spatial data at a range of scales from urban and infrastructural, building and master plan, through to office space design. Social data comes from both public sources relating to interaction between participants, private messaging as well as corporate and institutional networks.
A project in conjunction with a global consulting engineering firm, leveraging their large database of existing Building Information Models (BIM), and aiming to turn large amounts of unruly data into insight and useful tools.
We do this by utilising analytics and big data visualisation to gain a better understanding of BIM models. The analytics and visualisations are accessible, as collaborative effort is desired. These are then extended to developing predictive machine learning which helps as assistive technology for greater BIM productivity and model verification.
Got any interesting topics you would like us to look at or collaborate with us on?
We'd be keen to hear from you. Talk to us : sam_joyce[at]sutd.edu.sg
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Singapore University of Technology and Design, 8 Somapah Rd, Singapore, 487372
T: +65 6499 7454 E: sam_joyce [at] sutd.edu.sg   |   andre_chaszar [at] sutd.edu.sg