Structural optimization for diversified designs in the conceptual design in architecture using shape grammar

In architectural and structural design, computational methods have transformed the conceptual phase, allowing the exploration of diverse and efficient solutions. This paper introduces a new framework that integrates shape grammar, genetic algorithms, and clustering techniques to optimize structural designs in the early stages of architectural workflows. Unlike traditional optimization methods that aim for a single optimal solution, this approach generates multiple rational design options, balancing structural performance, aesthetics, and project constraints. Shape grammar effectively manages complex topologies with intuitive inputs, enabling designers to create varied structural forms while understanding the underlying rules and processes. Algorithmically, shape grammar offers significant design variety while maintaining designer control and insight into each configuration’s production. This research focuses on developing a tool for designing 2D trusses and frame systems, with the aim of creating a functional prototype and verifying its performance through a case study. The method uses shape grammar for initial topologies, clustering algorithms to group similar designs, and genetic algorithms to refine and optimize each cluster. This ensures efficient and diverse designs, providing a strong foundation for future developments. The framework’s effectiveness is demonstrated in a case study of a simplified large-scale convention hall, where these computational techniques generate innovative structural layouts. The results show the framework’s ability to produce diverse designs meeting architectural and structural needs, offering advantages over traditional parametric design. This research contributes to architectural and structural design by providing a powerful tool for early design stages, where multiple options are crucial. The framework enhances the creative potential and aligns with the requirements of structural engineering, such as safety and performance. By promoting efficiency, diversity, and adaptability, this approach can transform the way architects and engineers tackle complex design challenges, leading to more innovative building practices.

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Publication Details

Journal
International Journal of Architectural Computing
Published
2025-07-05
DOI
https://doi.org/10.1177/14780771251352949
Primary Topic
Topology Optimization in Engineering
Type
article
Field-Weighted Citation Impact
0.00
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article

Structural optimization for diversified designs in the conceptual design in architecture using shape grammar

Anders Rønnquist, Marcin Luczkowski, Bunji Izumi, Bendik Manum et al.
International Journal of Architectural Computing
Topology Optimization in Engineering
article

Structural optimization for diversified designs in the conceptual design in architecture using shape grammar

Anders Rønnquist, Marcin Luczkowski, Bunji Izumi, Bendik Manum, Sverre Magnus Haakonsen
article en

Abstract

In architectural and structural design, computational methods have transformed the conceptual phase, allowing the exploration of diverse and efficient solutions. This paper introduces a new framework that integrates shape grammar, genetic algorithms, and clustering techniques to optimize structural designs in the early stages of architectural workflows. Unlike traditional optimization methods that aim for a single optimal solution, this approach generates multiple rational design options, balancing structural performance, aesthetics, and project constraints. Shape grammar effectively manages complex topologies with intuitive inputs, enabling designers to create varied structural forms while understanding the underlying rules and processes. Algorithmically, shape grammar offers significant design variety while maintaining designer control and insight into each configuration’s production. This research focuses on developing a tool for designing 2D trusses and frame systems, with the aim of creating a functional prototype and verifying its performance through a case study. The method uses shape grammar for initial topologies, clustering algorithms to group similar designs, and genetic algorithms to refine and optimize each cluster. This ensures efficient and diverse designs, providing a strong foundation for future developments. The framework’s effectiveness is demonstrated in a case study of a simplified large-scale convention hall, where these computational techniques generate innovative structural layouts. The results show the framework’s ability to produce diverse designs meeting architectural and structural needs, offering advantages over traditional parametric design. This research contributes to architectural and structural design by providing a powerful tool for early design stages, where multiple options are crucial. The framework enhances the creative potential and aligns with the requirements of structural engineering, such as safety and performance. By promoting efficiency, diversity, and adaptability, this approach can transform the way architects and engineers tackle complex design challenges, leading to more innovative building practices.

International Journal of Architectural Computing
Norwegian University of Science and Technology (NO)
Openalex Percentile: Top 83%
Topology Optimization in Engineering
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