Performance-based, AI-ML-assisted Generative EA Design with Bio-inspired Topological Optimisations of a 50m, 3D-printed Steel Bridge

T. Spiegelhalter
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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.
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基于性能,ai - ml辅助生成EA设计与生物启发拓扑优化的50米3d打印钢桥
使用进化算法(EA)技术和拓扑优化(TO)的ai - ml辅助生成设计(GD)在过去几年中经历了巨大的增长。因此,AI和GD在许多领域都有重要的应用,例如工业与产品设计,医学,合成生物学,基础设施,建筑,工程与建设(AEC)。本研究论文讨论了AI-ML辅助、云计算和ea驱动的生成式设计的基于性能的工作流程,并进行了拓扑优化,以减少重量和成本。所讨论的研究是一个轻量级的现实世界的混合,在德国授予50米机器人3d打印bluemint®钢桥设计和现成的钢管预制,于2023年6月完成。具有有限元结构分析(FEA)和云驱动深度神经网络(GNN)场景的生成桥设计将展示受生物学启发的全球最大的3d打印线弧增材制造(WAAM)行人/自行车桥。
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