Pixels to Pavements

Abstract

The convergence of machine learning (ML) and the built environment is redefining traditional design decision-making processes. This report explores the integration of ML within architecture, urban design, and urban planning, emphasizing its transformative potential as a design decision making tool. The report delves into the historical context of digital tools in architecture and examines how ML is currently utilized in the built environment. Through a detailed methodology, the report analyzes ML’s role as a computational design aid, as a design facilitator or augmenter, and as a co-designer. This report aims to connect the idea of machine learning’s use in design decision-making processes in the built Environment to my design project. The impact of a literature review and case studies has helped extract and implement different key methods of machine learning in various stages of my design project, such as the data manipulation stage, form finding stage, design intervention placement stage, and simulation analysis of and for design decisions stage. Critical analyses focus on the role of data quality, human agency, and the limitations of ML, such as algorithmic bias and the potential erosion of human creativity. This report contends that ML can profoundly influence and effectively dictate design decision making in both an architectural and urban design context, through its aid as a computational design tool, design facilitator, and co-designer. The discussion emphasizes the necessity of human expertise in interpreting ML outputs and proposes a collaborative approach between human intuition and ML capabilities. The report concludes by advocating for a continuous dialogue between technology and human creativity to ensure ML serves as a valuable tool in shaping the built environment rather than a replacement for human ingenuity.