Learning to simulate and design for structural engineering
arXiv:2003.09103
Abstract
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.
arXiv commentICML2020
References in corpus (13)
- Semi-Supervised Classification with Graph Convolutional Networks
- Categorical Reparameterization with Gumbel-Softmax
- Soft Actor-Critic Algorithms and Applications
- Graph Neural Networks: A Review of Methods and Applications
- Junction Tree Variational Autoencoder for Molecular Graph Generation
- Learning to Simulate Complex Physics with Graph Networks
- Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
- Graph networks as learnable physics engines for inference and control
- Attention, Learn to Solve Routing Problems!
- Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
- Model-based Deep Hand Pose Estimation
- Learning to Infer and Execute 3D Shape Programs
- Unsupervised Image to Sequence Translation with Canvas-Drawer Networks