Journal of KIBIM (한국BIM학회 논문집)

Korean Institute of Building Information Modeling (한국BIM학회)
권호 표지
DOI QR코드

DOI QR Code

CNN deep learning based estimation of damage locations of a PSC bridge using static strain data

정적 변형률 데이터를 사용한 CNN 딥러닝 기반 PSC 교량 손상위치 추정

  • 한만석 (인하대학교 토목공학과) ;
  • 신수봉 (인하대학교 사회인프라공학과) ;
  • 안효준 (인하대학교 토목공학과)
  • Received : 2020.06.30
  • Accepted : 2020.06.30
  • Published : 2020.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

As the number of aging bridges increases, more studies are being conducted on developing effective and reliable methods for the assessment and maintenance of bridges. With the advancement in new sensing systems and data learning techniques through AI technology, there is growing interests in how to evaluate bridges using these advanced techniques. This paper presents a CNN(Convolution Neural Network) deep learning based technique for evaluating the damage existence and for estimating the damage location in PSC bridges using static strain data. Simulation studies were conducted to investigate the proposed method with error analysis. Damage was simulated as the reduction in the stiffness of a finite element. A data learning model was constructed by applying the CNN technique as a type of deep learning. The damage status and its location were estimated using data set built through simulation. It was assumed that the strain gauges were installed in a regular interval under the PSC bridge girders. In order to increase the accuracy in evaluating damage, the squared error between the intact and measured strains are computed and applied for training the data model. Considering the damage occurring near the supports, the results of error analysis were compared according to whether strain data near the supports were included.

Keywords

References

  1. Brandon, R. (2019). How to Convert an RGB Image to Grayscale. End-to-End Machine Learning Library, https://brohrer.github.io/convert_rgb_to_grayscale.html (Nov. 14. 2019)
  2. Daphne, C. (2018). An intuitive guide to Convolutional Neural Networks. freeCodeCamp, https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural-networks-260c2de0a050 (Apr. 24. 2018)
  3. Djemana, M., Hrairi, M., Al Jeroudi, Y. (2017). Using electromechanical impedance and extreme learning machine to detect and locate damage in structures. J. of Nondestructive Evaluation, pp. 36-39.
  4. Gui, G., Pan, H., Lin, Z., Li, Y., Yuan, Z. (2017). Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection. KSCE J. of Civil Engineering, 21(2), pp. 523-534. https://doi.org/10.1007/s12205-017-1518-5
  5. Han, M. (2019). Construction of Digital Twin for Bridges and Development of Damage Localization Process using CNN Deep Learning. Masters Thesis, Inha University.
  6. KISTEC. (2017). Specification for detailed violation of safety inspection and precision safety diagnosis (bridge).
  7. Lee, K., Jeong, S., Sim, S. H., Shin, D. H. (2019). A Novelty detection approach for tendons of prestressed concrete bridges based on a convolutional autoencoder and acceleration Data. Sensors, 19(7),1633, pp. 1-27. https://doi.org/10.1109/JSEN.2018.2879233
  8. Malekjafarian, A., Golpayegani, F., Moloney, C., Clarke, S. (2019). A machine learning approach to bridge-damage detection using responses measured on a passing vehicle. Sensors, 19, 4035, pp. 1-19. https://doi.org/10.1109/JSEN.2018.2879233
  9. Mangalathu, S., Hwang, S. H., Choi, E., Jeon, J. S. (2019). Rapid seismic damage evaluation of bridge portfolios using machine learning techniques. Engineering Structures, 201(2019), 109785, pp. 1-12.
  10. MLIT. (2015). the Limit-State based Bridge Design Specification. Korean Ministry of Land, Infrastructure and Transport.
  11. Neves, A. C., Gonz lez, I., Leander, J., Karoumi, R. (2018). A new approach to damage detection in bridges using machine learning. Lecture Notes in Civil Engineering, 5, pp. 73-84. https://doi.org/10.1007/978-3-319-67443-8_5
  12. Pathirage, C. S. N., et al. (2018). Structural damage identification based on autoencoder neural networks and deep learning. Engineering Structures, 172, pp. 13-28. https://doi.org/10.1016/j.engstruct.2018.05.109
  13. Rageh, A., Eftekhar, A. S., Linzell, D. G. (2020). Steel railway bridge fatigue damage detection using numerical models and machine learning: Mitigating influence of modeling uncertainty. Int. J. of Fatigue, 134, 105458, pp. 1-21.
  14. Sun, S., Liang, L., Li, M., Li, X. (2018). Vibration-based damage detection in bridges via machine learning. KSCE J. of Civil Engineering, 22(12), pp. 5123-5132. https://doi.org/10.1007/s12205-018-0318-x
  15. Taeho, J. (2019). Everyone's Deep Learning, 5th ed., Gilbut, pp. 221-233.