Characterizing Expert Behavior During Interactive Parametric Building Design

参数化设计策略使建筑-工程-施工行业的专家能够在早期建筑设计中快速考虑许多潜在的选择。 应用于参数模型的优化技术可以系统地找到定量设计目标（如能源使用或结构效率）方面的最佳选择。 这个项目将研究如何优化技术可以最好地帮助设计师在管理定性和定量的目标同时。 在更广泛的背景下，该项目将研究新兴的人机协作工具如何影响设计选择，认知过程如何与它们产生的结果相关，特别是它们如何在建筑工程中情境化。 该项目将描述专家设计师的行为，因为他们采用基于优化的参数化设计方法来制定和探索早期设计阶段的设计选项。 该项目包括教育和推广活动，例如行业研讨会，以重新培训传统模拟培训的工程师，使其掌握新的数字设计技术和软件。 这些活动将有助于培养一支在职业生涯中具有计算敏捷性的劳动力队伍，使其能够使用数字化工具设计出更节能、更安全、更耐用和更可持续的建筑。本研究将采用经验性的多方法研究设计，利用眼动追踪和观察数据来调查有经验的从业者的设计策略。 研究的主要问题是：“什么样的设计行为模式，建筑工程师采用，同时构建和探索一个参数化模型，使用基于优化的工具？” 专家建筑设计师将从建筑和工程设计公司招募，并将参与设计任务，采用数据知情的参数化建模。 他们的行为将被捕获使用时间分辨记录数据的真实设计任务的设计软件和眼动跟踪硬件。多个数据流将使用机器学习功能（如隐马尔可夫模型和其他统计方法）进行分析，以表征行为。 该项目将为基于证据的计算设计工具开发方法提供基础，并将有助于数字接口，优化，参数建模，多个实时数据流分析的设计理论。 一个相关的目标是提高教育建立设计专业人员在使用新兴的可视化编程接口，参数化设计，优化conceptions.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

Parametric design strategies allow experts in the Architecture-Engineering-Construction industry to rapidly consider many potential options in early building design. Optimization techniques applied to parametric models can systematically find the best options in terms of quantitative design goals such as energy use or structural efficiency. This project will investigate how optimization techniques can best assist designers in managing qualitative and quantitative goals simultaneously. In a broader contex, the project will study how emerging human-computer collaborative tools influence design choices, how cognitive processes relate to the outcomes they produce, and especially how they are contextualized in architectural engineering. The project will characterize the behavior of expert designers as they employ optimization-based parametric design methods to formulate and explore design options in early-stage design. The project includes educational and outreach activities such as industry workshops to retrain established, traditionally analog-trained engineers in new digital design techniques and software. These activities will help to prepare a workforce to be computationally agile in their careers, with the ability to use digital tools to design buildings that are more energy-efficient, safe, durable and sustainable. This project will employ an empirical multiple methods research design to investigate experienced practitioners’ design strategies using eye tracking and observational data. The main research question under study is: “What patterns of design behaviors do architectural engineers employ while constructing and exploring a parametric model using optimization-based tools?” Expert building designers will be recruited from architecture and engineering design firms and will participate in a design task employing data-informed parametric modeling. Their behaviors will be captured using time-resolved recorded data of the authentic design task from the design software and eyetracking hardware. The multiple streams of data will be analyzed using machine learning capabilities such as Hidden Markov Modelling and other statistical methods to characterize behaviors. The project will provide a foundation for evidence-based approaches to computational design tool development and will contribute to design theory in digital interfaces, optimization, parametric modeling, analysis of multiple real-time data streams. A related objective is to improve education of established design professionals in the use of emerging visual programming interfaces, parametric design, and optimization concepts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.