Designing With Data: Moving Beyond The Design Space Catalog

Brown, Nathan C.; Mueller, Caitlin

doi:10.52842/conf.acadia.2017.154

Cited by 15 publications

(2 citation statements)

References 15 publications

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“…Studies on accuracy often do not consider the context of the architectural design process, where faster methods and simplifications are often necessary. This is because there is a lower level of detail in the early design stages, and the demands for responses are more qualitative, such as understanding which type of solution performs better than another (Brown & Mueller, 2017;Ellis & Mathews, 2001;D. H. W. Li, Cheung, & Lau, 2006;Picco, Lollini, & Marengo, 2014).…”

Section: Simulation In Daylighting For Architecturementioning

confidence: 99%

Comparison of fast daylighting climate-based simulation methods for parametric design: two-phase, three-phase method and path tracing

Queiroz,

Fernandes,

Pereira

2023

Architectural Engineering and Design Management

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Computer modeling and simulation are essential for predicting architectural solutions performance. When integrated into the design process; they are known as performance-based design. During the design process, fast analytical cycles are required. However, daylighting simulations are challenging due to computational processing and time requirements. This paper presents a comparative study of fast daylight simulation methods, two-phase, three-phase and path tracing, in a hypothetical commercial building located in Miami, Florida. Five types of static glass and one electrochromic glass were tested with six parametric models of a translucent horizontal shading device. Daysim was used as reference. The two-phase method produced results similar to the reference results with faster processing times for static facades. For dynamic facades, the path tracing method with the tested interface enabled faster simulations and parametric variations. However, the three-phase method yielded a lower average error.

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Section: Simulation In Daylighting For Architecturementioning

confidence: 99%

Comparison of fast daylighting climate-based simulation methods for parametric design: two-phase, three-phase method and path tracing

Queiroz,

Fernandes,

Pereira

2023

Architectural Engineering and Design Management

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“…Beyond technological advancements, the successful implementation of AI in the AEC sector requires robust interdisciplinary collaboration. It is not just about integrating AI tools but ensuring that AI experts, environmental scientists, urban planners, and architects collaboratively harness these tools to create sustainable and efficient designs [41]. This collaborative approach ensures that the technological solutions align with the real-world complexities of urban environments and architectural challenges.…”

Section: Ai In the Aec Sectormentioning

confidence: 99%

Advanced Digital Tools for Data-Informed and Performance-Driven Design: A Review of Building Energy Consumption Forecasting Models Based on Machine Learning

Di Stefano,

Ruta,

Masera

2023

Applied Sciences

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Cities and buildings represent the core of human life, the nexus of economic activity, culture, and growth. Although cities cover less than 10% of the global land area, they are notorious for their substantial energy consumption and consequential carbon dioxide (CO2) emissions. These emissions significantly contribute to reducing the carbon budget available to mitigate the adverse impacts of climate change. In this context, the designers’ role is crucial to the technical and social response to climate change, and providing a new generation of tools and instruments is paramount to guide their decisions towards sustainable buildings and cities. In this regard, data-informed digital tools are a viable solution. These tools efficiently utilise available resources to estimate the energy consumption in buildings, thereby facilitating the formulation of effective urban policies and design optimisation. Furthermore, these data-driven digital tools enhance the application of algorithms across the building industry, empowering designers to make informed decisions, particularly in the early stages of design. This paper presents a comprehensive literature review on artificial intelligence-based tools that support performance-driven design. An exhaustive keyword-driven exploration across diverse bibliographic databases yielded a consolidated dataset used for automated analysis for discerning the prevalent themes, correlations, and structural nuances within the body of literature. The primary findings indicate an increasing emphasis on master plans and neighbourhood-scale simulations. However, it is observed that there is a lack of a streamlined framework integrating these data-driven tools into the design process.

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WOOD, CAD AND AI: Digital Modelling as Place of Convergence of Natural and Artificial Intelligent to Design Timber Architecture

Bianconi

Filippucci

2019

Lecture Notes in Civil Engineering

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Designing With Data: Moving Beyond The Design Space Catalog

Cited by 15 publications

References 15 publications

Comparison of fast daylighting climate-based simulation methods for parametric design: two-phase, three-phase method and path tracing

Comparison of fast daylighting climate-based simulation methods for parametric design: two-phase, three-phase method and path tracing

Advanced Digital Tools for Data-Informed and Performance-Driven Design: A Review of Building Energy Consumption Forecasting Models Based on Machine Learning

WOOD, CAD AND AI: Digital Modelling as Place of Convergence of Natural and Artificial Intelligent to Design Timber Architecture

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