Designing Decisions

Abstract

Architectural design requires balancing creativity with practical constraints, yet many early-stage decisions are made intuitively with limited evaluation of their impacts. In the context of increasing demand for affordable housing, there is a need for design workflows that better integrate efficiency, constructibility, and material performance at the schematic design stage. This thesis investigates how data-informed computational design workflows can support more evidence based early decision making within timber prefabricated housing design. Using a Research by Design (RbD) methodology supported by data-informed evaluation, the study combines iterative prototyping with computational evaluation to compare prefabricated timber systems within a four-unit medium-density housing proposal. Two evaluative matrices were developed: one to assess prefabrication systems across material use, life cycle considerations, performance, and construction criteria, and another to evaluate how architectural design decisions influenced efficiency and spatial outcomes across multiple design iterations. Functioning as decision-making tools, the matrices enabled qualitative design intentions to be tested against quantitative performance criteria while avoiding over-determination of architectural exploration. The findings demonstrate that the iterative, computationally informed design workflow effectively integrates evaluation with architectural intent. The analysis clarifi es how early design decisions influence efficiency and spatial quality while also highlighting the limitations of computational precision and the continuing need for architectural judgment. Overall, the research shows that data-informed workflows can enhance design decision-making without replacing intuition, supporting more transparent, evidence-based, and efficient design processes.