In Tension: Computational exploration of the design space of tensile network structures
Name
Burke-aburke3-smbt-arch-2024-thesis.pdf
Description
Thesis PDF
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15.6 MB
Format
Adobe PDF
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41e545e6e06d47bc210c0dc54bb8ecf2
Author(s)
Burke, Adam T.
Advisor(s)
Mueller, Caitlin
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
Cable and rope net structures are lightweight tensile systems and generally cannot resist
compression or bending. Tensile network structures are often used to span long distances
without intermediate supports and have found applications in art, architecture, and structural engineering due to their physical and visual lightness. However, the design of tensile
net structures is generally challenging since their form cannot be arbitrarily defined. Instead
a process of form-finding must be used to establish a geometry where all edges of the network
carry only tensile forces.
Physical models and computational methods can be used for the form-finding of tensile
network structures; however the primary challenge in the design process is the adjustment of
the network parameters to achieve a specific design. Recent work has shown that automatic
differentiation software packages can be used to efficiently design funicular structures (that
is, those that work in pure tension or pure compression) with additional designer driven
objectives, but these techniques remain largely inaccessible to general designers, architects,
and engineers due to the involved process of problem setup and limited interactivity of
existing tools.
To address this limitation, I introduce a new tool set consisting of two main components, Ariadne and Theseus. These components take advantage of automatic differentiation
of objective functions for efficient tensile network simulation and provide a user interface
for architects, engineers, and other designers as a plugin for a commonly used 3d modeling
software. In this thesis, I outline the structure and features of this tool set, show results of
networks optimized with different composable objectives, and show some fabricated examples. Next, I explore the the generation of more complex 3d network topologies through a
procedural shape grammar. Finally, I explore the use of differentiable simulation in conjunction with machine learning techniques to optimize the geometry of tensile networks using
semantic input and to develop an implicit representation of the space of equal edge length
tensed network poses. Together, this new tool set and additional methods enable a more expansive exploration of the design space of tensile networks where design intent and practical
constraints are respected.
MIT Department
Massachusetts Institute of Technology. Department of Architecture
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