Tsiliakos, M; (2024) Fast Geometry Optimizations for Architectural Workflows: A Case Study on the Computational Geometry of High-Rises. In: Johnson, Jason S and Nahmad Vazques, Alicia and Forward, Kristen and Gardner, Guy and Hapton, Daniel and Parker, Matthew and Rhee, Jinmo and Taron, Joshua, (eds.) ACADIA 2024: Designing Change - Proceedings Volume 1 for the 2024 Association for Computer Aided Design in Architecture Conference. (pp. pp. 635-644). Association for Computer Aided Design in Architecture (ACADIA): Calgary, Canada.

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Abstract

Streamlined geometry optimization processes are crucial for maintaining the integrity and feasibility of geometrically complex forms. These geometries, in addition to the aesthetic goals of the designers, must also adhere to the reality of fabrication, material properties, and statutory constraints. Traditional purely cost-reducing optimizations, or methods borrowed from other domains, such as computer graphics, can deviate from the original design intent and have reduced efficiency when used in architecture. It is therefore essential to strike a balance between optimization and the desired aesthetics for architectural solutions. Integrating these strategies late in the design process, as post-rationalizations, often leads to time-consuming and expensive revisions, that once again compromise the viability of the designs. This can be prevented by seamlessly integrating said optimizations into the design cycles, ensuring the constant delivery of project iterations, hence being fast, efficient, and reliable, coupling performance with design intention. This paper presents an integrated process for fast computational geometry optimizations within architectural workflows, enhancing design fidelity and minimizing dependencies on time-consuming and isolated post-rationalization efforts. By choosing a challenging, doubly curved façade high-rise as a case study, we aim to demonstrate how simplifying multi-objective problems can expedite optimization processes that are specific to architectural design. We advocate that the adoption of pre-initialized geometrical configurations, fast iterative searches, force-driven optimizations, and analytical geometry approximations, can, when paired with modular parametric models and interoperable data, streamline the design process, and allow for quick, yet informed decision making.

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