Intelligent design of shear wall layout based on graph neural networks |
| |
| Affiliation: | 1. School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing, PR China;2. Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, PR China;3. State Key Laboratory of Virtual Reality Technology and System, Beijing, PR China;1. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. Beijing Xinghang Mechanical-Electrical Eqiupment Co., Ltd., Beijing 100074, China;3. AVIC Manufacturing Technology Institute, Beijing 100024, China;4. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China;1. ISAE-SUPMECA, Quartz Laboratory, Saint-Ouen, France;2. Roberval Laboratory, University of Technology of Compiègne, Compiègne, France;3. Laboratory of Mechanics of Sousse, National Engineering School of Sousse, University of Sousse, Sousse, Tunisia;1. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan;2. Science & Technology Policy Research and Information Center, National Applied Research Laboratories, Taipei 106, Taiwan;1. Department of Civil Engineering, Monash University, Melbourne, Australia;2. School of Engineering, RMIT University, Melbourne, Australia;3. School of Housing, Building, and Planning, Universiti Sains, Malaysia;4. Department of Civil and Environmental Engineering, University of Alberta, Canada |
| |
| Abstract: | Structural scheme design of shear wall structures is important because it is the first stage that guides the project along its entire structural design process and significantly impacts the subsequent design stages. Design methods for shear wall layouts based on deep generative algorithms have been proposed and achieved some success. However, current generative algorithms rely on pixel images to design shear wall layouts, which have many model parameters and require intensive calculations. Moreover, it is challenging to use pixel image-based methods to reflect the topological characteristics of structures and connect them with the subsequent design stages. The above defects can be effectively solved by representing a shear wall structure in graph data form and adopting graph neural networks (GNNs), which have a robust topological-characteristic-extraction capability. However, there is no existing research using GNN methods in the design of shear wall structures owing to the lack of graph representation methods and high-quality structural graph data for shear walls. Therefore, this study develops an intelligent design method for shear wall layouts based on GNNs. Two graph representation methods for a shear wall structure—graph edge representation and graph node representation—are examined. A data augmentation method for shear wall structures in graph data form is established to enhance the universality of the GNN performance. An evaluation method for both graph representation methods is developed. Case studies show that the shear wall layout designed using the established GNN method is highly similar to the design by experienced engineers. |
| |
| Keywords: | Graph neural network Graph representation method Shear wall structure Intelligent shear wall layout design Deep learning |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
