UNIVERSITY PARK, Pa. — Nathan Brown, assistant professor of architectural engineering in the Penn State College of Engineering, earned a five-year, $587,000 U.S. National Science Foundation (NSF) Early Career Development (CAREER) Award for a project titled, “Active Designer-Computer Collaborative Conceptual Design Using Data-driven Models.”
Brown shared insight on his work utilizing data science in early building design and discussed his goals for the project in the Q&A below.
Q: What do you want to understand or solve through this project?
Brown: We have lots of data that can help us design better buildings, such as simulations of how different design options will behave or data from how similar buildings have performed. However, this data can take time to generate, or it might not be clear enough for decisions made in early design when the concept is still in development. In this project, we will create and assess computational design tools that learn how building forms behave, predict performance metrics for new designs and provide useful suggestions for improving an initial design concept.
Q: How will advances in this area impact society?
Brown: We can measure a lot of a building’s impact on people and the environment, but not everything: We cannot measure our human values expressed through creating enriching and even beautiful spaces. Because of this, I do not see building design as a purely mathematical optimization problem where a computer can tell us exactly what to do. Simultaneously, engineering simulations and artificial intelligence can help us better understand the implications of our decisions and guide us toward efficient or sustainable outcomes. The goal of this research is to help develop better design tools that that help us make computational decisions during a building’s planning and construction. By using these tools to improve efficiency, we can more effectively create buildings that we want to live and work in for years to come.
Q: Will undergraduate or graduate students contribute to this research? How?
Brown: Graduate students across architecture, architectural engineering and related engineering fields will work on curating and structuring data about building design, training computer models with machine learning to predict good designs based on this data and prototyping design tools that visualize design outcomes and their expected performance. Undergraduate students will help with creating design case studies and analyzing data about potential outcomes and by participating in pilot studies. Additionally, we plan to run workshops that will help connect students and practicing engineers, allowing them to collaborate and practice computational thinking for real design problems.
Q: The NSF CAREER award not only funds a research project, but it also recognizes the potential of the recipient as a researcher, educator and leader in their field. How do you hope to fulfill that potential?
Brown: I have always been near the border of architecture and structural engineering as a researcher and educator. There can be culture clashes between these two professions — yet it has always fascinated me how they can be fused together to create amazing buildings and structures. While there is a lot of overlap between these fields, some architecture students may be very creative but lack the intuition or confidence to contribute to discussions about technical design challenges. Conversely, some engineering students know exactly how to calculate and properly size building elements, but they may find it difficult to influence the direction of a project beyond the narrow problem parameters they are given. My hope is to help both types of students see technical constraints as creative design opportunities while giving them the intuition and tools necessary to design great buildings.