계층적 분류 및 회귀 그래프 신경망을 이용한 최적의 트러스 구조 예측 방법Optimal Truss Structure Prediction Method Using Hierarchical Classification and Regression Graph Neural Network
- Other Titles
- Optimal Truss Structure Prediction Method Using Hierarchical Classification and Regression Graph Neural Network
- Authors
- 임태윤; 이승훈; 서민식; 민승재
- Issue Date
- Nov-2022
- Publisher
- 대한기계학회
- Keywords
- 위상최적화(Topology optimization); 그래프신경망(Graph neural network); 딥러닝(Deep learning); 트러스구조(Trussstructure); 최적설계(Optimal design); Ground structuremethod
- Citation
- 대한기계학회 2022년 학술대회, pp.1456 - 1460
- Indexed
- OTHER
- Journal Title
- 대한기계학회 2022년 학술대회
- Start Page
- 1456
- End Page
- 1460
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188509
- Abstract
- This paper proposes a graph neural network-based hierarchical classification and regression model to predict the optimal ground structure layout. A ground structure consists of a set of interconnected bars similar to a graph structure. The number of bars and joints depends on the size and configuration of the target design domains. Conventional neural networks, such as MLP and CNN, cannot handle such variable-dimensional data. Therefore, we adopted a graph neural network using the similarity between ground structure and graph. Only a few of the potential bar candidates remained after ground structure optimization converged. It causes the highly imbalanced distribution of bar areas, which is difficult to predict. Therefore, we construct the model to process a binary classification task to classify the presence or absence of remaining bars among potential bar candidates, and then sequentially process a regression task to predict the detailed value of the cross-sectional areas. To validate the proposed model, three types of ground structure optimization problems were defined: cantilever, simply supported, and L-shape beams. The model was evaluated with three evaluation metrics: classification, regression, and optimality. As a result, it predicts the presence or absence of the optimal ground structure with more than 95% accuracy and predicts the cross-sectional areas of the remaining bars with greater than 95% accuracy.
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