Integrating expertise and parametric analysis for a data-driven decision-making practice

This study explores the integration of expert design intuition and parametric data analysis. While traditional professional design expertise helps to rapidly frame relevant aspects of the design problem and produce viable solutions, it has limitations in addressing multi-criteria design problems wit...

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Veröffentlicht in:International journal of architectural computing 2020-12, Vol.18 (4), p.424-440
Hauptverfasser: Bernal, Marcelo, Okhoya, Victor, Marshall, Tyrone, Chen, Cheney, Haymaker, John
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container_issue 4
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container_title International journal of architectural computing
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creator Bernal, Marcelo
Okhoya, Victor
Marshall, Tyrone
Chen, Cheney
Haymaker, John
description This study explores the integration of expert design intuition and parametric data analysis. While traditional professional design expertise helps to rapidly frame relevant aspects of the design problem and produce viable solutions, it has limitations in addressing multi-criteria design problems with conflicting objectives. On the other hand, parametric analysis, in combination with data analysis methods, helps to construct and analyze large design spaces of potential design solutions and tradeoffs, within a given frame. We explore a process whereby expert design teams propose a design using their current intuitive and analytical methods. That design is then further optimized using parametric analysis. This study specifically explores the specification of geometric and material properties of building envelopes for two typically conflicting objectives: daylight quality and energy consumption. We compare performance of the design after initial professional design exploration, and after parametric analysis, showing consistently significant performance improvement after the second process. The study explores synergies between intuitive and systematic design approaches, demonstrating how alignment can help expert teams efficiently and significantly improve project performance.
doi_str_mv 10.1177/1478077120940975
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