한국공간구조학회논문집 (Journal of Korean Association for Spatial Structures)

  • 제18권4호
  • /
  • Pages.69-80
  • /
  • 2018
  • /
  • 1598-4095(pISSN)
  • /
  • 2287-7401(eISSN)
한국공간구조학회 (Korean Association for Spatial Structures)
권호 표지
DOI QR코드

DOI QR Code

딥러닝을 이용한 트러스 구조물의 정적 및 동적 거동 예측

Prediction of Static and Dynamic Behavior of Truss Structures Using Deep Learning

  • 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.)
  • 투고 : 2018.08.14
  • 심사 : 2018.10.16
  • 발행 : 2018.12.15
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

  1. Turing, A. M., "Computing Machinery and Intelligence", Mind 49, pp.433-460, 1950.
  2. Hajela, P., & Berke, L., "Neurobiological computational models in structural analysis and design", Computers & Structures, Vol.41, No.4, pp.657-667, 1991 https://doi.org/10.1016/0045-7949(91)90178-O
  3. Biedermann, J. D., "Representing Design Knowledge with Neural Networks", Computer-Aided Civil and Infrastructure Engineering, Vol.12, No.4, pp.277-285, 1997 https://doi.org/10.1111/0885-9507.00063
  4. Li, S., "Global flexibility simulation and element stiffness simulation in finite element analysis with neural network", Computer Methods in Applied Mechanics and Engineering, Vol.186, No.1, pp.101-108, 2000 https://doi.org/10.1016/S0045-7825(99)00108-5
  5. Lee, S. H., Ha, J. W., Zokhirova, M., Moon, H. J., & Lee, J. H., "Background Information of Deep Learning for Structural Engineering", Archives of Computational Methods in Engineering, Vol.25, No.1, pp.121-129, 2018 https://doi.org/10.1007/s11831-017-9237-0
  6. Samuel, A. L., "Some Studies in Machine Learning Using the Game of Checkers. II -Recent Progress", IBM Journal of Research and Development, Vol.11, No.6, pp.601-617, 1967 https://doi.org/10.1147/rd.116.0601
  7. Lee, S. H., & Lee, J. H., "Deep Learning for Structural Analysis", Journal of Korean Association for Spatial Structures, Vol.17, No.4, pp.10-15, 2017
  8. Pearson, K., & Lee, A., "On The Generalized Probable Error in Multiple Normal Correlation", Biometrika, Vol.6, No.1, pp.59-68, 1908 https://doi.org/10.1093/biomet/6.1.59
  9. Kromanis, R., & Kripakaran, P., "Predicting thermal response of bridges using regression models derived from measurement histories", Computers & Structures, Vol.136, pp.64-77, 2014 https://doi.org/10.1016/j.compstruc.2014.01.026
  10. Takayuki, O., "Deep Learning", Jpub Press, pp.203, 2016.
  11. Jo, T. H., "Deep Learning for Everyone", Gilbut INC., pp.308, 2017.
  12. Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R.,... Winkler, R., "The Accuracy of Extrapolation (Time Series) Methods: Results of a Forecasting Competition", Journal of Forecasting, Vol.1, No.2, pp.111-153, 1982 https://doi.org/10.1002/for.3980010202
  13. Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference for Learning Representations, USA, pp.1-15
  14. Lim, H. K., Kim, J. B., Kwon, D. H., & Han, Y. H., "Comparison Analysis of TensorFlow's Optimizer Based on MNIST's CNN Model", Journal of Advanced Technology Research, Vol. 2, No.1, pp.6-14, 2017