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Estimation of Rotational Stiffness of Connections in Steel Moment Frames by using Artificial Neural Network

인공신경망을 이용한 철골모멘트골조 접합부의 회전강성 손상예측

  • Choi, Se-Woon (Dept. of Architecture, Daegu Catholic University)
  • 최세운 (대구가톨릭대학교 건축학부)
  • Received : 2017.08.22
  • Accepted : 2017.12.13
  • Published : 2018.01.01

Abstract

In this study, the damage detection method is proposed for the rotational stiffness of connections in steel moment frames by using artificial neural network(ANN). The flexural moment of columns, natural frequencies, modeshapes are used for the input layer in ANN while the damage index, that signify the damage level, is used for the output layer in ANN. The 5-story steel moment frame as an example structure is used to generate the train and test data. Total number of damage scenarios considered is 829. From the results of application, it is shown that the proposed method can accurately estimate the location and level of damages.

본 연구는 인공신경망을 이용해 철골모멘트골조의 접합부 손상을 예측하는 기법을 제안한다. 인공신경망의 입력층에는 기둥 부재의 휨모멘트, 고유진동수, 모드형상 정보가 사용되며, 출력층에는 구조물 접합부의 회전강성 손상지표가 사용한다. 손상지표는 각 접합부의 손상정도를 의미한다. 5층 철골모멘트골조 예제의 수치해석을 통해 훈련 및 검증용 데이터를 생성한다. 총 829가지의 손상 시나리오가 고려된다. 시뮬레이션은 OpenSees를 이용해 반복 실행하여 데이터를 얻도록 하였으며, 훈련용 데이터를 생성할 때 회전 강성의 손상은 1.0, 0.75, 0.5 등 세 가지 중 하나의 값을 가지도록 하였다. 예제 검증을 통해 제시하는 기법은 손상 위치 및 수준을 정확하게 예측하는 것으로 나타났다. 제시하는 기법은 손상지표, 1차, 2차 고유진동수 및 모드형상 등에 대해 매우 유사한 결과를 제시하는 것으로 확인되었다.

Keywords

References

  1. Yoo, S. H., and Lee, H. K. (2013), Damage Location Detection of Shear Building Structures Using Mode Shape, Journal of the Korea Institute for Structural Maintenance and Inspection, 17(1), 124-132. https://doi.org/10.11112/jksmi.2013.17.1.124
  2. Yoo, S. H. (2014), Damage Detection of Shear Building Structures Using Dynamic Response, Journal of the Korea Institute for Structural Maintenance and Inspection, 18(3), 101-107. https://doi.org/10.11112/JKSMI.2014.18.3.101
  3. Kim, J. T., Ryu, Y. S., Cho, H. M., and Stubbs, N. (2003), Damage Identification in Beam-Type Structures: Frequency-Based Method vs Mode-Shape-Based Method, Engineering Structures, 25(1), 57-67. https://doi.org/10.1016/S0141-0296(02)00118-9
  4. Peeters, B., Maeck, J., and De Roeck, G. (2001), Vibration-Based Damage Detection in Civil Engineering: Excitation Sources and Temperature Effects, Smart Materials and Structures, 10(3), https://doi.org/10.1088/0964-1726/10/3/314.
  5. Doebling, S. W., Farrar, C. R., and Prime, M. B. (1998), A Summary Review of Vibration-Based Damage Identification Methods, Shock and Vibration Digest, 30(2), 91-105. https://doi.org/10.1177/058310249803000201
  6. Fan, W., and Qiao, P. (2011), Vibration-Based Damage Identification Methods: A Review and Comparative Study, Structural Health Monitoring, 10(1), 83-111. https://doi.org/10.1177/1475921710365419
  7. Wang, Q., and Deng, X. (1999), Damage Detection with Spatial Wavelets, Internatnional Journal of Solids and Structures, 36(23), 3433-3468.
  8. Kim, H., and Melhem, H. (2004), Damage Detection of Structures by Wavelet Analysis, Engineering Structures, 26(3), 347-362. https://doi.org/10.1016/j.engstruct.2003.10.008
  9. Pandey, A. K., Biswas, M., and Samman, M. M. (1991), Damage Detection from Changes in Curvature Mode Shapes, Journal of Sound and Vibration, 145(2), 321-332. https://doi.org/10.1016/0022-460X(91)90595-B
  10. Shi, Z., Law, S. S., and Zhang, L. (2000), Structural Damage Detection from Modal Strain Energy Change, Journal of Engineering Mechanics, 126(12), 1216-1223. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:12(1216)
  11. Pandey, A. K., and Biswas, M. (1994), Damage Detection in Structures using Changes in Flexibility, Journal of Sound and Vibration, 169(1), 3-17. https://doi.org/10.1006/jsvi.1994.1002
  12. Yan, W. J., Huang, T. L., and Ren, W. X. (2010), Damage Detection Method based on Element Modal Strain Energy Sensitivity, Advances in Structural Engineering, 13(6), 1075-1088. https://doi.org/10.1260/1369-4332.13.6.1075
  13. Li, H., Wang, J. and Hu, S. L. J. (2008), Using Incomplete Modal Data for Damage Detection in Offshore Structures, Ocean Engineering, 35(17), 1793-1799. https://doi.org/10.1016/j.oceaneng.2008.08.020
  14. Law, S. S., Shi, Z. Y., and Zhang, L. M. (1998), Structural Damage Detection From Incomplete and Noisy Modal Test Data, Journal of Engineering Mechanics, 124(11), 1280-1288. https://doi.org/10.1061/(ASCE)0733-9399(1998)124:11(1280)
  15. Shi, Z. Y., Law, S. S., and Zhang, L. M. (2000), Damage Localization by Directly Using Incomplete Mode Shapes, Journal of Engineering Mechanics, 126(6), 656-660. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:6(656)
  16. Park, N. G., and Park, Y. S. (2003), Damage Detection Using Spatially Incomplete Frequency Response Funcitons, Mechanical Systems and Signal Processing, 17(3), 519-532. https://doi.org/10.1006/mssp.2001.1423
  17. Cha, Y. J., and Buyukozturk, O. (2015), Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization, Computer-Aided Civil and Infrastructure Engineering, 30, 347-358. https://doi.org/10.1111/mice.12122
  18. Kang, F., Li, J. J., and Xu, Q. (2012), Damage Detection based on Improved Particle Swarm Optimization using Vibration Data, Applied Soft Computing, 12, 2329-2335. https://doi.org/10.1016/j.asoc.2012.03.050
  19. Seyedpoor, S. M. (2012), A Two Stage Method for Structural Damage Detection Using A Modal Strain Eenrgy Based Index and Particle Swarm Optimization, International Journal of Non-Linear Mechanics, 47, 1-8.
  20. Perera, R., Ruiz, A., and Manzano, C. (2007), An Evolutionary Multiobjetive Framework for Structural Damage Localization and Quantification, Engineering Structures, 29, 2540-2550. https://doi.org/10.1016/j.engstruct.2007.01.003
  21. Kim, S. J., and Choi, S. W. (2016), A Numerical Study to Estimate the Lateral Responses of Steel Moment Frames Using Strain Data, Journal of the Korea Institute for Structural Maintenance and Inspection, 20(6), 113-119. https://doi.org/10.11112/jksmi.2016.20.6.113
  22. Rafiq, M. Y., Bugmann, G., and Easterbrook, D. J. (2001), Neural Network Design for Engineering Applications, Computers & Structures, 79(17), 1541-1552. https://doi.org/10.1016/S0045-7949(01)00039-6
  23. Flood, I., and Kartam, N. (1994), Neural Networks in Civil Engineering I: Principles and Understanding, Journal of Computing in Civil Engineering, 8(2), 131-148. https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(131)
  24. Wu, X., Ghaboussi, J., and Garrett, J. H. (1992), Use of Neural Networks in Detection of Structural Damage, Computers & Structures, 42(4), 649-659. https://doi.org/10.1016/0045-7949(92)90132-J
  25. Adeli, H. (2001), Neural Networks in Civil Engineering: 1989-2000, Compute-Aided Civil and Infrastructure Engineering, 16(2), 126-142. https://doi.org/10.1111/0885-9507.00219
  26. Hibbeler, R. C. (2011), Structural Analysis, Prentice Hall, 451-486.
  27. Brincker, R., Zhang, L. and Andersen, P. (2001), Modal Identification of Output-only Systems Using Frequency Domain Decomposition, Smart Materials and Structures, 10, 441-445. https://doi.org/10.1088/0964-1726/10/3/303
  28. Weng, J. H., Loh, C. H., Lynch, J. P., Lu, K. C., Lin, P. Y., and Wang, Y. (2008), Output-only Modal Identification of a Cable-Stayed Bridge Using Wireless Monitoring Systems, Engineering Structures, 30, 1820-1830. https://doi.org/10.1016/j.engstruct.2007.12.002
  29. Michel, C., Gueguen, P., El Arem, S., Mazars, J., and Kotronis, P. (2010), Full-scale Dynamic Response of an RC Building Under Weak Seismic Motions Using Earthquake Recordings, Ambient Vibrations and Modelling, Earthquake Engineering & Structural Dynamics, 39, 419-441.
  30. Chang, S. J., and Kim, N. S. (2008), Estimation of Displacement Response from the Measured Dynamic Strain Signals Using Mode Decomposition Technique, KSCE Journal of Civil Engineering, 28, 507-515.