Structural optimization has gained popularity in modern structural design, helping to reduce material consumption while maintaining the structural performance of buildings. This process also significantly influences the architectural appearance, affecting various aspects such as cross-section sizing, structural forms, and the layout of structural members. Beyond minimizing materials or costs, structural optimization can serve as a powerful tool for making architecture more visually appealing. However, with the wide variety of structural optimization methods proposed, gaining a comprehensive overview has become challenging. To address this, a systematic mapping study has been conducted, focusing on methods introduced over the past decade. The relevant journal articles are categorized based on several factors, including types of optimization, materials used, structural typologies, areas of application, and optimization objectives. The results of this study provide both a broad overview of recent developments in structural optimization and valuable insights into research-rich and under-explored areas. Moreover, the paper discusses which types of structural optimization are more relevant when applied as part of the architectural design process. It is suggested that future research should focus on identifying gaps and challenges in effectively applying structural optimization to architectural design, thus enhancing both efficiency and aesthetic potential.

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.