Steel and Composite Structures

  • 제18권5호
  • /
  • Pages.1103-1117
  • /
  • 2015
  • /
  • 1229-9367(pISSN)
  • /
  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Determination of displacement distributions in welded steel tension elements using digital image techniques

  • Sozen, Sahin (Department of Civil Engineering, Gaziosmanpasa University)
  • 투고 : 2014.02.13
  • 심사 : 2014.11.07
  • 발행 : 2015.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

It is known that material properties, connection quality and manufacturing methods are among the important factors directly affecting the behavior of steel connections and hence steel structures. The possible performance differences between a fabricated connection and its computer model may cause critical design problems for steel structures. Achieving a reliable design depends, however, on how accurately the material properties and relevant constitutive models are considered to characterize the behavior of structures. Conventionally, the stress and strain fields in structural steel connections are calculated using the finite elements method with assumed material properties and constitutive models. Because the conventional strain gages allow the measurement of deformation only at one point and direction for specific time duration, it is not possible to determine the general characteristics of stress-strain distributions in connections after the laboratory performance tests. In this study, a new method is introduced to measure displacement distribution of simple steel welded connections under tension tests. The method is based on analyzing digital images of connection specimens taken periodically during the laboratory tension test. By using this method, displacement distribution of steel connections can be calculated with an acceptable precision for the tested connections. Calculated displacements based on the digital image correlation method are compared with those calculated using the finite elements method.

키워드

참고문헌

  1. ASTM (2005), A370-05, Standard Test Methods and Definitions for Mechanical Testing of Steel Products, Society for Testing and Materials, USA.
  2. AWS, D. (2006), D1. 1/D1. 1M-Structural Welding Code-Steel; American Welding Society, New York, NY, USA.
  3. Basyigit, C., Comak, B., Kilincarslan, S. and Serkan Uncu, I. (2012), "Assessment of concrete compressive by image processing technique", Construct. Build. Mater., 37, 526-532. https://doi.org/10.1016/j.conbuildmat.2012.07.055
  4. Beckwith, T.G., Marangoni, R.D. and Lienhard, J.H. (2007), Mechanical Measurements, (6th Edition), Prentice Hall, NJ, USA.
  5. Chao, Y.J., Luo, P.F. and Kalthoff, J.F. (1998), "An experimental study of the deformation fields around a propagating crack tip", Exp. Mech., 38(2), 79-85. https://doi.org/10.1007/BF02321648
  6. Choi, S. and Shah, S.P. (1997), "Measurement of deformations on concrete subjected to compression using image correlation", Exp. Mech., 37(3), 307-313. https://doi.org/10.1007/BF02317423
  7. De Roover, C., Vantomme, J., Wastiels, J. and Taerwe, L. (2002), "Deformation analysis of a modular connection system by digital image correlation", Exp. Techniques, 26(6), 37-40. https://doi.org/10.1111/j.1747-1567.2002.tb00089.x
  8. Hassler, U., Rehak, M. and Ezrt, F. (2011), "An image processing approach for radioscopic inspection of turbine blades", Proceedigs of International Symposium on Digital Industrial Radiology and Computed Tomography, Fuerth, Germany, June.
  9. Iqbal, S.M., Gopal, A. and Sarma, A.S.V. (2011), "Volume estimation of apple fruits using image processing", In: Image Information Processing (ICIIP), Proceedings of 2011 International Conference on IEEE, Himcahal Pradash, India, November, (pp. 1-6).
  10. Jurjo, D.L.B.R., Magluta, C., Roitman, N. and Goncalves, P.B. (2010), "Experimental methodology for the dynamic analysis of slender structures based on digital image processing techniques", Mech. Syst. Signal Process., 24(5), 1369-1382. https://doi.org/10.1016/j.ymssp.2009.12.006
  11. Lecompte, D., Bossuyt, S., Cooreman, S., Sol, H. and Vantomme, J. (2007), "Study and generation of optimal speckle patterns for DIC", Proceedings of the Annual Conference and Exposition on Experimental and Applied Mechanics, Costa Mesa, CA, USA, June.
  12. Manual, C.F.S.D. (2002), American Iron and Steel Institute, Washington, D.C., USA.
  13. McNeill, S.R., Peters, W.H. and Sutton, M.A. (1987), "Estimation of stress intensity factor by digital image correlation", Eng. Fract. Mech., 28(1), 101-112. https://doi.org/10.1016/0013-7944(87)90124-X
  14. Pan, B. and Li, K. (2011), "A fast digital image correlation method for deformation measurement", Optic. Laser. Eng., 49(7), 841-847. https://doi.org/10.1016/j.optlaseng.2011.02.023
  15. Pan, B., Dafang, W. and Yong, X. (2012), "Incremental calculation for large deformation measurement using reliability-guided digital image correlation", Optic. Laser. Eng., 50(4), 586-592. https://doi.org/10.1016/j.optlaseng.2011.05.005
  16. Pannier, Y., Avril, S., Rotinat, R. and Pierron, F. (2006), "Identification of elasto-plastic constitutive parameters from statically undetermined tests using the virtual fields method", Exp. Mech., 46(6), 735-755. https://doi.org/10.1007/s11340-006-9822-x
  17. Rathod, V.R., Anand, R.S. and Ashok, A. (2012), "Comparative analysis of NDE techniques with image processing", Nondestruct. Test. Eva., 27(4), 305-326. https://doi.org/10.1080/10589759.2011.645820
  18. Sozen, S. and Guler, M. (2008), "Measurement of Small Strains in Steel Samples Using Digital Imaging Technigues", Proceedings of the 8th International Congress on Advances in Civil Engineering, Famagusta, North Cyprus, September.
  19. Sozen, S. and Guler, M. (2011), "Determination of displacement distributions in bolted steel tension elements using digital image techniques", Optic. Laser. Eng., 49,1428-1435. https://doi.org/10.1016/j.optlaseng.2011.07.002
  20. Spyrou, S. and Davison, J.B. (2001), "Displacement measurement in studies of steel T-stub connections", J. Construct. Steel Res., 57(6), 649-661. https://doi.org/10.1016/S0143-974X(01)00003-7
  21. Sutton, M.A., Wolters, W.J., Peters, W.H., Ranson, W.F. and McNeill, S.R. (1983), "Determination of displacements using an improved digital correlation method", Image Vision Comput., 1(3), 133-139. https://doi.org/10.1016/0262-8856(83)90064-1
  22. Swamy, M.S. and Holi, M.S. (2012), "Knee joint articular cartilage segmentation, visualization and quantification using image processing techniques: A review", Int. J. Comput. Appl., 42, 36-43.
  23. URL (2010), http://www.ansys.com
  24. URL (2009), http://www.mathworks.com
  25. Vanlanduit, S., Vanherzeele, J., Longo, R. and Guillaume, P. (2009), "A digital image correlation method for fatigue test experiments", Optic. Laser. Eng., 47(3), 371-378. https://doi.org/10.1016/j.optlaseng.2008.03.016
  26. Wang, T.T., Jaw, J.J., Chang, Y.H. and Jeng, F.S. (2009), "Application and validation of profile-image method for measuring deformation of tunnel wall", Tunn. Undergr. Space Technol., 24(2), 136-147. https://doi.org/10.1016/j.tust.2008.05.008
  27. Wang, Z.B., Yu, Z., Li, X.Y. and Luo, D.H. (2011), "A method of parameters estimation for traffic accidents by image and video processing", Int. J. Video Image Process. Network Secur., 11(6), 13-16.
  28. Yoneyama, S., Morimoto, Y. and Takashi, M. (2006), "Automatic evaluation of mixed-mode stress intensity factors utilizing digital image correlation", Strain, 42(1), 21-29. https://doi.org/10.1111/j.1475-1305.2006.00246.x
  29. Yu, Q., Zhu, W., Tang, C. and Yang, T. (2014), "Impact of rock microstructures on failure processes - Numerical study based on DIP Technique", Geomech. Eng., Int. J., 7(4), 375-401. https://doi.org/10.12989/gae.2014.7.4.375
  30. Yue, Z.Q., Chen, S. and Tham, L.G. (2003), "Finite element modeling of geomaterials using digital image processing", Comput. Geotech., 30(5), 375-397. https://doi.org/10.1016/S0266-352X(03)00015-6
  31. Zhang, R. and He, L. (2012), "Measurement of mixed-mode stress intensity factors using digital image correlation method", Optic. Laser. Eng., 50(7), 1001-1007. https://doi.org/10.1016/j.optlaseng.2012.01.009