THESIS
2023
1 online resource (xii, 61 pages) : illustrations (some color)
Abstract
The design of free-standing canopies has experienced significant advancements in the
architecture field, with numerous solutions developed over the past few decades. However,
there remains a need for further research focusing on the early-stage design phase, which is
both critical and time-consuming. Determining appropriate support structures for canopies
often relies on architects’ expertise and requires numerous repetitive tests. In this thesis, we
propose a novel workflow presenting an interactive interface specifically tailored for early-stage
canopy building design. By inputting canopy models and desired parameters, our
algorithm generates a reasonable column placement solution. Our approach centers around
solution optimization using the Capacity-constrained Voronoi Tessellation (C...[
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The design of free-standing canopies has experienced significant advancements in the
architecture field, with numerous solutions developed over the past few decades. However,
there remains a need for further research focusing on the early-stage design phase, which is
both critical and time-consuming. Determining appropriate support structures for canopies
often relies on architects’ expertise and requires numerous repetitive tests. In this thesis, we
propose a novel workflow presenting an interactive interface specifically tailored for early-stage
canopy building design. By inputting canopy models and desired parameters, our
algorithm generates a reasonable column placement solution. Our approach centers around
solution optimization using the Capacity-constrained Voronoi Tessellation (CCVT) algorithm,
which addresses placement optimization problems for the canopy building models.
The weight distribution of the canopy mesh is transformed into a heat map, which subsequently
serves as input for the CCVT algorithm after a series of smoothing and mapping
processes. This ensures that different parts of the canopy receive varying levels of support
based on their weights. By comparing the algorithm performance for multiple models, we demonstrate that our method produces satisfactory column placements during the early design
phase, which will effectively facilitate the initiation of a well-informed canopy design
process.
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