论文标题
学习模拟和设计结构工程
Learning to simulate and design for structural engineering
论文作者
论文摘要
建筑物的结构设计过程是耗时且费力的。为了自动化此过程，结构工程师将优化方法与仿真工具相结合，以找到最小的建筑物质量，但受建筑法规的影响。但是，实际上，由于设计空间的较大尺寸，优化方法的迭代性质以及缓慢的仿真工具，实际上通常避免在大多数建筑物的次优设计上进行优化和妥协。在这项工作中，我们将建筑物结构作为图形制定，并创建端到端管道，可以通过训练和预训练的可区分的结构模拟器一起训练来提出柱和梁的最佳横截面。所提出的结构设计的性能与遗传算法（GA）优化的结构设计相媲美，并满足所有约束。随着建筑物质量减少的最佳结构设计不仅可以降低材料成本，还可以降低碳足迹。
The structural design process for buildings is time-consuming and laborious. To automate this process, structural engineers combine optimization methods with simulation tools to find an optimal design with minimal building mass subject to building regulations. However, structural engineers in practice often avoid optimization and compromise on a suboptimal design for the majority of buildings, due to the large size of the design space, the iterative nature of the optimization methods, and the slow simulation tools. In this work, we formulate the building structures as graphs and create an end-to-end pipeline that can learn to propose the optimal cross-sections of columns and beams by training together with a pre-trained differentiable structural simulator. The performance of the proposed structural designs is comparable to the ones optimized by genetic algorithm (GA), with all the constraints satisfied. The optimal structural design with the reduced the building mass can not only lower the material cost, but also decrease the carbon footprint.
