Optimization Strategies of Architecture and Engineering Graduate Students: Responding to Data During Design

Both architects and engineers increasingly use design optimization in the early stages, but it is unclear how designers’ disciplinary background may influence their optimization strategies. In considering designs with multiple conflicting objectives, large datasets of options are often produced, which can be difficult to navigate. Architects and engineers may engage with optimization tools and their feedback differently based on their background, which can affect collaborative efforts and influence design outcomes. In this study, graduate architecture and engineering students with experience in optimization responded to a design task with both quantitative and qualitative goals. The task required participants to establish and explore their own parametric design variables, producing large datasets with numerical and visual feedback. Screen recordings of the design sessions were analyzed to characterize optimization events initiated by the designers, revealing when and how often they ran optimization routines and how they reviewed the optimization feedback. The study showed that the architecture students tended to use optimization later and iterate less than the engineering students, who relied on quantitative data more often to edit their design space and justify their decisions. Future efforts to incorporate design optimization into graduate education should be cognizant of these differences, especially in multi-disciplinary settings that encourage architects and engineers to mutually engage with data during collaborative design.