In this study, an algorithm applying deep learning to the truss structures was proposed. Deep learning is a method of raising the accuracy of machine learning by creating a neural networks in a computer. Neural networks consist of input layers, hidden layers and output layers. Numerous studies have focused on the introduction of neural networks and performed under limited examples and conditions, but this study focused on two- and three-dimensional truss structures to prove the effectiveness of algorithms. and the training phase was divided into training model based on the dataset size and epochs. At these case, a specific data value was selected and the error rate was shown by comparing the actual data value with the predicted value, and the error rate decreases as the data set and the number of hidden layers increases. In consequence, it showed that it is possible to predict the result quickly and accurately without using a numerical analysis program when applying the deep learning technique to the field of structural analysis.

1. 서론
 2. 연구 방법
  2.1 데이터셋 생성 단계
  2.2 훈련 단계
 3. 10-bar 트러스
  3.1 데이터셋 크기에 따른 학습 모델의 정적 및동적 해석 결과
  3.2 훈련 횟수에 따른 학습 모델의 정적 및 동적 해석 결과
 4. 25-bar 트러스
  4.1 데이터셋 크기에 따른 학습 모델의 정적 및동적 해석 결과
  4.2 훈련 횟수에 따른 학습 모델의 정적 및 동적 해석 결과
 5. 결론
 References

• 심은아(세종대학교 건축공학과) | Sim Eun-A (Dept. of Architectural Engineering, Sejong Univ.)
• 이승혜(세종대학교 건축공학과) | Lee Seunghye (Dept. of Architectural Engineering, Sejong Univ.)
• 이재홍(세종대학교 건축공학과) | Lee Jaehong (Dept. of Architectural Engineering, Sejong Univ.) 교신저자