Performance-based, AI-ML-assisted Generative EA Design with Bio-inspired Topological Optimisations of a 50m, 3D-printed Steel Bridge
| Author(s): |
Thomas Spiegelhalter
(College of Architecture, Communication and the Arts, Florida International University, Miami, FL, USA)
|
|---|---|
| Medium: | journal article |
| Language(s): | English |
| Published in: | International Journal of Structural and Civil Engineering Research, 2023, n. 3, v. 12 |
| Page(s): | 63-67 |
| DOI: | 10.18178/ijscer.12.3.63-67 |
| Abstract: | AI-ML-assisted Generative Design (GD) using Evolutionary Algorithms (EA) techniques and Topology Optimization (TO) has undergone massive growth over the past few years. As a result, AI and GD have essential applications in many fields, such as Industrial & Product Design, Medicine, Synthetic Biology, Infrastructure, Architecture, Engineering & Construction (AEC). This research paper discusses the performance-based workflows for AI-ML assisted, cloud computation and EA-driven Generative Design with topological optimisation to reduce weight and cost. The discussed research is a lightweight real-world hybrid, awarded 50 m robot 3d-printed bluemint®steel bridge design and off-the-shelf steel tube prefabrication in Germany, completed in June 2023. [3] The generative bridge design with finite element structural analysis (FEA) and cloud-driven deep neural network (GNN) scenarios will demonstrate the largest 3d-printed Wire-and-arc Additive Manufacturing (WAAM) pedestrian/bicycle bridge inspired by biology worldwide. |
| Keywords: |
additive manufacturing artificial intelligence evolutionary algorithm Generative Design topological optimizations
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10741299 - Published on:
25/09/2023 - Last updated on:
31/01/2024
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