Performance-based, AI-ML-assisted Generative EA Design with Bio-inspired Topological Optimisations of a 50m, 3D-printed Steel Bridge
| Auteur(s): |
Thomas Spiegelhalter
(College of Architecture, Communication and the Arts, Florida International University, Miami, FL, USA)
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|---|---|
| Médium: | article de revue |
| Langue(s): | anglais |
| Publié dans: | 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 |
| Abstrait: | 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. |
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sur cette fiche - Reference-ID
10741299 - Publié(e) le:
25.09.2023 - Modifié(e) le:
31.01.2024
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