Journal of Korean Association for Spatial Structures (한국공간구조학회논문집)

Korean Association for Spatial Structures (한국공간구조학회)
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Prediction of Static and Dynamic Behavior of Truss Structures Using Deep Learning

딥러닝을 이용한 트러스 구조물의 정적 및 동적 거동 예측

  • Sim, Eun-A (Dept. of Architectural Engineering, Sejong Univ.) ;
  • Lee, Seunghye (Dept. of Architectural Engineering, Sejong Univ.) ;
  • Lee, Jaehong (Dept. of Architectural Engineering, Sejong Univ.)
  • Received : 2018.08.14
  • Accepted : 2018.10.16
  • Published : 2018.12.15
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Abstract

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.

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