Characterizing Expert Behavior During Interactive Parametric Building Design

描述交互式参数化建筑设计过程中专家行为的特征

• 批准号: 2033332
• 负责人: Nathan Brown
• 金额: $ 31.66万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033332&HistoricalAwards=false
• 关键词: Characterizing; Expert; Behavior; During; Interactive

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.

参数化设计策略使建筑-工程-建筑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在能源使用或结构效率等定量设计目标方面找到最佳选项。这个项目将调查优化技术如何最好地帮助设计师同时管理定性和定量目标。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被背景化。该项目将描述专家设计师的行为，因为他们使用基于优化的参数设计方法来制定和探索早期设计中的设计选项。该项目包括教育和外联活动，如行业讲习班，对传统上接受模拟培训的老牌工程师进行新的数字设计技术和软件方面的再培训。这些活动将有助于帮助劳动力在其职业生涯中灵活计算，有能力使用数字工具来设计更节能、安全、耐用和可持续的建筑。这个项目将采用经验性的多方法研究设计，利用眼球跟踪和观察数据来调查有经验的从业者的设计策略。正在研究的主要研究问题是：“建筑工程师在使用基于优化的工具构建和探索参数模型时，采用了什么设计行为模式？”专业建筑设计师将从建筑和工程设计公司招聘，并将参与一项采用数据信息参数建模的设计任务。他们的行为将使用来自设计软件和眼线跟踪硬件的真实设计任务的时间分辨记录数据来捕获。将使用机器学习能力，如隐马尔可夫模型和其他统计方法来分析多个数据流，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、多实时数据流分析的设计理论。一个相关的目标是改善现有设计专业人员在使用新兴的可视化编程界面、参数设计和优化概念方面的教育。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。