Search for: All records

In architecture and engineering, design professionals may use the term “optimization” to describe a range of design approaches. These working definitions of optimization may not align with one another, or with the formal definition of mathematical optimization in engineering education. This paper presents a thematic analysis of 13 interviews with design professionals who use optimization in their work. Using the communication theory of coordinated management of meaning (CMM) to understand how the interviewer and interviewee were negotiating possible definitions, four themes are identified: optimization as performance improvement, as achieving varied goals, as a systematic process, and as a formal problem structure with variables and objectives, which is most aligned with the mathematical definition. Interviewees used these varied definitions dynamically in conversation, which informs researchers and educators about their potential use in practice. 

Engaging with performance feedback in early building design often involves building a custom parametric model and generating large datasets, which is not always feasible. Alternatively, large parametric datasets of general design problems and filtering methods could be used together to explore specific design decisions. This paper investigates the generalizability of a method that dynamically assesses variable importance and likely influence on performance objectives as a precomputed design space is filtered down. The method first trains linear model trees to predict building performance objectives across a generic design space. Leaf node models are then aggregated to provide feedback on variable importance in different design space regions. This approach is tested on three design problems that vary in number of variables, samples, and design space structure to reveal advantages and potential limitations of the method. Algorithm improvements are proposed, and general recommendations are developed to apply it on future datasets. 

Parametric optimization techniques allow building designers to pursue multiple performance objectives, which can benefit the overall design. However, the strategies used by architecture and engineering graduate students when working with optimization tools are unclear, and ineffective computational design procedures may limit their success as future designers. In response, this re-search identifies several designerly behaviors of graduate students when responding to a conceptual building design optimization task. It uses eye-tracking, screen recording, and empirical methods to code their behaviors following the situated FBS framework. From these data streams, three different types of design iterations emerge: one by the designer alone, one by the optimizer alone, and one by the designer incorporating feedback from the optimizer. Based on the timing and frequency of these loops, student participants were characterized as completing partial, crude, or complete optimization cycles while developing their designs. This organization of optimization techniques establishes reoccurring strategies employed by developing designers, which can encourage future pedagogical approaches that empower students to incorporate complete optimization cycles while improving their designs. It can also be used in future research studies to establish clear links between types of design optimization behavior and design quality.