딥러닝 기반 교량 손상추정을 위한 Generative Adversarial Network를 이용한 가속도 데이터 생성 모델

• 영문명: Generative Model of Acceleration Data for Deep Learning-based Damage Detection for Bridges Using Generative Adversarial Network
• 발행기관: 한국BIM학회
• 저자명: 이강혁(Lee, Kanghyeok) 신도형(Shin, Do Hyoung)
• 간행물 정보: 『KIBIM Magazine』9권 1호, 42~51쪽, 전체 10쪽
• 주제분류: 공학 > 건축공학
• 파일형태: PDF
• 발행일자: 2019.03.30

영문 초록

Maintenance of aging structures has attracted societal attention. Maintenance of the aging structure can be efficiently performed with a digital twin. In order to maintain the structure based on the digital twin, it is required to accurately detect the damage of the structure. Meanwhile, deep learning-based damage detection approaches have shown good performance for detecting damage of structures. However, in order to develop such deep learning-based damage detection approaches, it is necessary to use a large number of data before and after damage, but there is a problem that the amount of data before and after the damage is unbalanced in reality. In order to solve this problem, this study proposed a method based on Generative adversarial network, one of Generative Model, for generating acceleration data usually used for damage detection approaches. As results, it is confirmed that the acceleration data generated by the GAN has a very similar pattern to the acceleration generated by the simulation with structural analysis software. These results show that not only the pattern of the macroscopic data but also the frequency domain of the acceleration data can be reproduced. Therefore, these findings show that the GAN model can analyze complex acceleration data on its own, and it is thought that this data can help training of the deep learning-based damage detection approaches.

목차

1. 서 론

2. 선행연구조사

3. Generative Adversarial Network (GAN)

4. 기초 가속도 데이터생성을 위한 시뮬레이션 PSC-I교 모델링

5. GAN을 통해 생성된 가속도 데이터 비교

6. 결 론

키워드

Generative Adversarial Network Generative Model Damage Detection Digital Twin Maintenanc Generative Adversarial Network 생성모델 손상추정기법 디지털 트윈 유지관리