Automated Structural Design Generation Using Generative Design Tools

Traditional structural design in civil engineering often involves manual calculations, iterative modeling,
and reliance on engineering judgment, which can be time-consuming and prone to error. With
advancements in Building Information Modeling (BIM), parametric tools, and artificial intelligence,
generative design has emerged as a powerful method to enhance efficiency and innovation. This paper
explores the integration of generative design tools such as Autodesk Dynamo, Rhino-Grasshopper, and
AI-based optimization algorithms to automate structural frame generation. By defining parameters like
load conditions, material types, and spatial constraints, multiple optimized design alternatives can be
generated and evaluated automatically. A parametric model is developed to support automated workflows,
and a mid-rise building is used as a case study to validate performance. Comparative analysis shows that
generative models achieve better material efficiency, cost savings, and structural performance compared to
manual design methods. The study demonstrates the potential of generative design to improve early-stage
planning and decision-making in structural engineering.