Spatial Computing for Building Performance and Design

Accommodating urban population growth while reducing emissions from the built environment poses an unprecedented challenge to the architectural discipline. To enable more sustainable construction, the dissertation proposes a new computational design framework to investigate how building performance...

Full description

Bibliographic Details
Main Author: Weber, Ramon Elias
Other Authors: Mueller, Caitlin
Format: Thesis
Published: Massachusetts Institute of Technology 2024
Online Access:https://hdl.handle.net/1721.1/157180
_version_ 1824458025784573952
author Weber, Ramon Elias
author2 Mueller, Caitlin
author_facet Mueller, Caitlin
Weber, Ramon Elias
author_sort Weber, Ramon Elias
collection MIT
description Accommodating urban population growth while reducing emissions from the built environment poses an unprecedented challenge to the architectural discipline. To enable more sustainable construction, the dissertation proposes a new computational design framework to investigate how building performance from an environmental and user perspective relates to spatial design. The dissertation surveys existing computational methodologies for design automation and identifies new opportunities and value propositions for architectural computing in design guidance, feedback, and optimization. Exploring methods that can be used to generate and optimize structural systems of buildings and interior layouts, a specific focus lies in the design of residential buildings. By applying generative design methods to building analytics, new ways for estimating the embodied carbon of a building and the environmental impact of system-level design choices can be explored. First, the research demonstrates how generative geometric algorithms can be coupled with structural simulations to accurately predict the structural material quantity and, through that, the embodied carbon of a building in early stages of design. Second, a new method for representing, analyzing, and generating spatial layouts – the hypergraph – is proposed, that captures the characteristics of any given floor plan. Unveiling new architectural opportunities through automatic geometry creation, the hypergraph shows potential to improve the quality of residential spaces in terms of environmental performance and access to daylight. Enabling new design tools for architects, it offers creative applications and new collaborative workflows for incorporating new spatial metrics in the design process. Allowing for new quantitative insights in building performance, the research demonstrates that spatial efficiency can outperform envelope upgrades in terms of carbon emission savings.
first_indexed 2025-02-19T04:19:20Z
format Thesis
id mit-1721.1/157180
institution Massachusetts Institute of Technology
last_indexed 2025-02-19T04:19:20Z
publishDate 2024
publisher Massachusetts Institute of Technology
record_format dspace
spelling mit-1721.1/1571802024-10-10T03:50:28Z Spatial Computing for Building Performance and Design Weber, Ramon Elias Mueller, Caitlin Reinhart, Christoph Massachusetts Institute of Technology. Department of Architecture Accommodating urban population growth while reducing emissions from the built environment poses an unprecedented challenge to the architectural discipline. To enable more sustainable construction, the dissertation proposes a new computational design framework to investigate how building performance from an environmental and user perspective relates to spatial design. The dissertation surveys existing computational methodologies for design automation and identifies new opportunities and value propositions for architectural computing in design guidance, feedback, and optimization. Exploring methods that can be used to generate and optimize structural systems of buildings and interior layouts, a specific focus lies in the design of residential buildings. By applying generative design methods to building analytics, new ways for estimating the embodied carbon of a building and the environmental impact of system-level design choices can be explored. First, the research demonstrates how generative geometric algorithms can be coupled with structural simulations to accurately predict the structural material quantity and, through that, the embodied carbon of a building in early stages of design. Second, a new method for representing, analyzing, and generating spatial layouts – the hypergraph – is proposed, that captures the characteristics of any given floor plan. Unveiling new architectural opportunities through automatic geometry creation, the hypergraph shows potential to improve the quality of residential spaces in terms of environmental performance and access to daylight. Enabling new design tools for architects, it offers creative applications and new collaborative workflows for incorporating new spatial metrics in the design process. Allowing for new quantitative insights in building performance, the research demonstrates that spatial efficiency can outperform envelope upgrades in terms of carbon emission savings. Ph.D. 2024-10-09T18:26:40Z 2024-10-09T18:26:40Z 2024-09 2024-09-24T19:46:06.697Z Thesis https://hdl.handle.net/1721.1/157180 0000-0002-3856-531X In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology
spellingShingle Weber, Ramon Elias
Spatial Computing for Building Performance and Design
title Spatial Computing for Building Performance and Design
title_full Spatial Computing for Building Performance and Design
title_fullStr Spatial Computing for Building Performance and Design
title_full_unstemmed Spatial Computing for Building Performance and Design
title_short Spatial Computing for Building Performance and Design
title_sort spatial computing for building performance and design
url https://hdl.handle.net/1721.1/157180
work_keys_str_mv AT weberramonelias spatialcomputingforbuildingperformanceanddesign