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트러스 구조물 사이즈 최적화를 위한 무응력 부재의 선택
이승혜,이종현,이기학,이재홍,Lee, Seunghye,Lee, Jonghyun,Lee, Kihak,Lee, Jaehong 한국공간구조학회 2021 한국공간구조학회지 Vol.21 No.1
This paper describes a novel zero-stress member selecting method for sizing optimization of truss structures. When a sizing optimization method with static constraints is implemented, the member stresses are affected sensitively with changing the variables. However, because some truss members are unaffected by specific loading cases, zero-stress states are experienced by the elements. The zero-stress members could affect the computational cost and time of sizing optimization processes. Feature selection approaches can be then used to eliminate the zero-stress member from the whole variables prior to the process of optimization. Several numerical truss examples are tested using the proposed methods.
위상 최적화를 위한 생산적 적대 신경망 기반 데이터 증강 기법
이승혜,이유진,이기학,이재홍,Lee, Seunghye,Lee, Yujin,Lee, Kihak,Lee, Jaehong 한국공간구조학회 2021 한국공간구조학회지 Vol.21 No.4
In this paper, a GAN-based data augmentation method is proposed for topology optimization. In machine learning techniques, a total amount of dataset determines the accuracy and robustness of the trained neural network architectures, especially, supervised learning networks. Because the insufficient data tends to lead to overfitting or underfitting of the architectures, a data augmentation method is need to increase the amount of data for reducing overfitting when training a machine learning model. In this study, the Ganerative Adversarial Network (GAN) is used to augment the topology optimization dataset. The produced dataset has been compared with the original dataset.