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dc.contributor.advisorJohn A. Ochsendorf.en_US
dc.contributor.authorMueller, Caitlin Ten_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Architecture.en_US
dc.date.accessioned2014-11-04T20:27:56Z
dc.date.available2014-11-04T20:27:56Z
dc.date.copyright2014en_US
dc.date.issued2014en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/91293
dc.descriptionThesis: Ph. D. in Architecture: Building Technology, Massachusetts Institute of Technology, Department of Architecture, 2014.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 199-206).en_US
dc.description.abstractThis dissertation focuses on computational strategies for incorporating structural considerations into the earliest stages of the architectural design process. Because structural behavior is most affected by geometric form, the greatest potential for structural efficiency and a harmony of design goals occurs when global formal design decisions are made, in conceptual design. However, most existing computational tools and approaches lack the features necessary to take advantage of this potential: architectural modeling tools address geometry in absence of performance, and structural analysis tools require an already determined geometrical form. There is a need for new computational approaches that allow designers to explore the structural design space, which links geometric variation and performance, in a free and interactive manner. The dissertation addresses this need by proposing three new design space strategies. The first strategy, an interactive evolutionary framework, balances creative navigation of the design space with a focus on performance. The original contributions of this strategy center on enhanced opportunities for designer interaction and control. The second strategy introduces structural grammars, which allow for the formulation of broad and diverse design spaces that span across typologies. This strategy extends existing work in geometry-based shape grammars by incorporating structural behavior in novel ways. Finally, the third strategy is a surrogate modeling approach that approximates the design space to enable fast and responsive design environments. This strategy contributes new ways for non-experts to use this machine-learning-based methodology in conceptual design. These three complementary strategies can be applied independently or in combination, and the dissertation includes a discussion about possibilities and techniques for integrating them. Finally, the dissertation concludes by reflecting on its potential impact on design in practice, and by outlining important areas for future work. Key words: conceptual structural design, design space exploration, structural optimization, interactive evolutionary algorithm, structural grammar, surrogate modeling, structural design toolsen_US
dc.description.statementofresponsibilityby Caitlin T. Mueller.en_US
dc.format.extent206 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectArchitecture.en_US
dc.titleComputational exploration of the structural design spaceen_US
dc.typeThesisen_US
dc.description.degreePh. D. in Architecture: Building Technologyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Architecture.en_US
dc.identifier.oclc893430996en_US


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