The phrase “work-life balance” can elicit eye rolls from architects. But machine learning–assisted design will relieve drudgery and allow greater artistry in the future of architecture.
by Mark Davis
For many architects, working nights and weekends is part of the job, especially when project deadlines loom. According to the 2018 Equity in Architecture report, 70% of architects surveyed don’t feel empowered to ask employers for work-life balance benefits, such as working remotely and flexible hours.
But in the future of architecture, automation and machine learning (ML) show promise in alleviating some of this pain, taking over menial tasks and giving architects more time for design creativity (and anything else they’d rather be doing).
In the past few years, my colleagues and I in Autodesk Research have been putting ML to the test for speculative architecture-design projects. We are seeing three ways that ML-assisted design has the potential to augment architects’ skills, improve productivity, and automate drudgery.
First, there’s “design automation,” or generative design, where the designer inputs constraints or parameters, and the algorithm creates design options. This is something the Autodesk Research team explored for Autodesk’s office in the MaRS Innovation District of Toronto, Canada.
Second is “design insight,” where the architect fully controls the design, but ML provides insight and suggestions on matters such as local building-code requirements. This gives architects more freedom to design, with helpful (but hands-off) guidance that can speed up their workflow—from planning to preconstruction.
Third is “design interaction,” where the ML software is cocreating the design with the architect and automating the more menial parts of the work. We recently explored this method with two of Autodesk’s partners, NVIDIA and Gensler. From the overlap of our common goals, a research project was born, one that suggests ML’s influence in building design and construction will be profound.
Read on HERE >>> Source: Redshift Could Machine Learning Help Work-Life Balance in Future of Architecture?