| dc.description.abstract | This dissertation presents a computational framework to effectively interpret the distribution of structural demand that emerges from the design of large-scale structural systems, and develops methods for its quantification and manipulation. Structural demand is the required strength and geometry of individual building components that emerges from design as a result of global geometry, topology, and loading. Existing metrics of structural performance fail to consider how variations in demand at the component level can lead to designs that are theoretically efficient but difficult to construct. This has led to a rejection of low-carbon, high-performance design solutions in practice, or the need for extensive post-hoc rationalization, both under the presumption of untenable design complexity for conventional building practices. This dissertation argues that an explicit consideration of the distribution of induced structural demand can bridge this gap between design intent and construction feasibility.
To achieve this, structural demand is interpreted as sets of geometric objects in n-dimensional feature spaces, where each dimension represents an independent component of demand, such as area, length, or stiffness. By directly visualizing the spatial distribution of demand, designers are presented with a richer context of non-physical structural design information, and can evaluate how decisions in structural form affect this distribution. Further, spatial interpretations of information allow for spatial metrics of similarity and variation to be defined, from which quantitative measures of design complexity are derived that account for the shape and distribution of demand. This framework, named \emph{Demand Space Analysis}, is explored in depth and applied to a range of structural scales, from the demand of truss elements and their connections, to the relationship between demand and fixed sets of capacity. Advancements in structural optimization are also presented, enabling more efficient and direct minimization of modern structural performance metrics, from which the relationship between design performance and demand complexity can be explored. Through case studies in each chapter, this dissertation demonstrates how geometric analysis of structural demand information can inform the designer of the implications of decisions on the perceived complexity of design, and provides tools for its quantification and reduction. | |
