Generative Modelling and Artificial Intelligence for Structural Optimization of a Large Span Structure

In architectural and structural design, current modeling and analysis tools are extremely powerful and allow one to generate and analyze virtually any structural shape. However, most of them do not allow designers to integrate structural performance as an objective during conceptual design. As structural performance is highly linked to architectural geometry, there is a need for computational strategies allowing for performance-oriented structural design in architecture. Today more than ever, the success of optimization is highly dependent on Artificial Intelligence (AI) techniques. Thanks to these techniques, calculation tools can actively interact with the design process by directing the designer to the development of alternatives that show more compliance with project requirements and to search for solutions increasingly oriented to an optimized behavior of the final configuration. The aim of this thesis is the development of a long-span, grid roof structure for a flexible generation and optimization framework for practical use in the sense of a continuously accompanying design explorer, in which parameterization is adaptable and objective functions are changeable at any time during the design process. The user is supported in his/her understanding of correlations by identifying a multiplicity of optimal solutions utilizing state-of-the-art multi-objective search algorithms within the core of the framework. Considering the tool as an interactive design aid, an intuitive interface allowing for extensive manual guidance and verification of the search process is featured. User selection of preferred solutions supports man-machine-dialogue and incorporation of non- or not-yet quantifiable measures. The work will be presented in four chapters, first through the understanding of the concept of structural optimization and parametric design, supported by the enormous potential of computational and algorithm tools. Then optimization of the chosen case study will be examined by modifying design parameters to minimize the mass of the structure by using genetic algorithms.