Development of a Distortion-Induced Fatigue Crack Characterization Methodology Using Digital Image Correlation
Publication: Journal of Bridge Engineering
Volume 25, Issue 9
Abstract
Distortion-induced fatigue cracking is a primary maintenance and structural safety concern in steel bridges built prior to the 1980s in the United States. Manual, hands-on inspections are currently the primary method departments of transportation and other bridge owners use to identify and quantify fatigue cracks. To improve the efficacy of fatigue crack inspections, previous research has proposed and examined numerous fatigue crack detection approaches, including both user-implemented technology and structural health monitoring methods. However, these approaches typically require human presence and active participation at the location of interest or prolonged mechanical contact and continuous monitoring of the structure. This limits the effectiveness and flexibility of these approaches for inspecting a large number of fatigue-susceptible regions found on steel bridges. Recently, vision-based sensing technologies have been explored for applications related to damage detection and health assessment in civil infrastructure, as they offer the benefits of being low cost, noncontact, and deployable without human presence at the specific region of interest. This paper presents a digital image correlation-based methodology developed from in-plane compact fracture specimens for the detection and quantification of fatigue cracks. The effectiveness of the proposed methodology is further evaluated through experimental tests of a fatigue crack on a large-scale steel girder-to-cross-frame connection, similar to the out-of-plane fatigue cracks commonly found on steel highway bridges. Results indicate that the digital image correlation methodology can adequately characterize fatigue cracks, both in-plane and out-of-plane, in terms of crack length. This quantification from a noncontact inspection technology has the potential to lead to future automation of steel highway bridge fatigue inspections.
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Acknowledgments
Funding for this study was provided in part through the Mid-America Transportation Center via a grant from the U.S. Department of Transportation's University Transportation Centers Program, and this support is gratefully acknowledged. The views expressed in this paper are those of the authors and do not reflect the position of the sponsoring agency.
References
AASHTO. 2014. LRFD bridge design specifications. 7th ed. Washington, DC: AASHTO.
Alemdar, F., D. Nagati, A. Matamoros, C. Bennett, and S. Rolfe. 2014a. “Repairing distortion-induced fatigue cracks in steel bridge girders using angles-with-plate retrofit technique. I: Physical simulations.” J. Struct. Eng. 140 (5): 04014003. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000876.
Alemdar, F., T. Overman, A. Matamoros, C. Bennett, and S. Rolfe. 2014b. “Repairing distortion-induced fatigue cracks in steel bridge girders using angles-with-plate retrofit technique. II: Computer simulations.” J. Struct. Eng. 140 (5): 04014004. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000874.
Alipour, M., S. J. Washlesky, and D. K. Harris. 2019. “Field deployment and laboratory evaluation of 2D digital image correlation for deflection sensing in complex environments.” J. Bridge Eng. 24 (4): 04019010. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001363.
Anderson, T. L. 1995. Fracture mechanics-fundamentals and applications. 2nd ed. Boca Raton, FL: CRC Press.
ASCE. 2017. “2017 Infrastructure Report Card—Bridges.” Accessed August 30, 2019. https://www.infrastructurereportcard.org/wp-content/uploads/2017/01/Bridges-Final.pdf.
ASTM. 2018. Standard test method for fracture toughness. ASTM E1820-18. West Conshohocken, PA: ASTM.
ASTM. 2019. Standard specification for carbon structural steel. ASTM A36-19. West Conshohocken, PA: ASTM.
Busca, G., A. Cigada, P. Mazzoleni, and E. Zappa. 2014. “Vibration monitoring of multiple bridge points by means of a unique vision-based measuring system.” Exp. Mech. 54 (2): 255–271. https://doi.org/10.1007/s11340-013-9784-8.
Carroll, J., C. Efstathiou, J. Lambros, H. Sehitoglu, B. Hauber, S. Spottswood, and R. Chona. 2009. “Investigation of fatigue crack closure using multiscale image correlation experiments.” Eng. Fract. Mech. 76 (15): 2384–2398. https://doi.org/10.1016/j.engfracmech.2009.08.002.
Carroll, J. D., W. Abuzaid, J. Lambros, and H. Sehitoglu. 2013. “High resolution digital image correlation measurements of strain accumulation in fatigue crack growth.” Int. J. Fatigue 57: 140–150. https://doi.org/10.1016/j.ijfatigue.2012.06.010.
Chen, F., X. Chen, X. Xie, X. Feng, and L. Yang. 2013. “Full-field 3D measurement using multi-camera digital image correlation system.” Opt. Lasers Eng. 51 (9): 1044–1052. https://doi.org/10.1016/j.optlaseng.2013.03.001.
Collins, W., R. Sherman, R. Leon, and R. Connor. 2016. “State-of-the-art fracture characterization. I: Master curve analysis of legacy bridge steels.” J. Bridge Eng. 21 (12): 04016097. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000954.
Dhanasekar, M., P. Prasad, J. Dorji, and T. Zahra. 2019. “Serviceability assessment of masonry arch bridges using digital image correlation.” J. Bridge Eng. 24 (2): 04018120. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001341.
FHWA (Federal Highway Administration). 2004. “National bridge inspection standards.” Fed. Regist. 69 (239): 74419–74439.
Fisher, J. W. 1984. Fatigue and fracture in steel bridges: Case studies. Chichester, UK: Wiley.
Hamam, R., F. Hild, and S. Roux. 2007. “Stress intensity factor gauging by digital image correlation: Application in cyclic fatigue.” Strain 43 (3): 181–192. https://doi.org/10.1111/j.1475-1305.2007.00345.x.
Hassel, H. L., C. R. Bennett, A. B. Matamoros, and S. T. Rolfe. 2013. “Parametric analysis of cross-frame layout on distortion-induced fatigue in skewed steel bridges.” J. Bridge Eng. 18 (7): 601–611. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000388.
Helfrick, M. N., C. Niezrecki, P. Avitabile, and T. Schmidt. 2011. “3D digital image correlation methods for full-field vibration measurement.” Mech. Syst. Sig. Process. 25 (3): 917–927. https://doi.org/10.1016/j.ymssp.2010.08.013.
Hutt, T., and P. Cawley. 2009. “Feasibility of digital image correlation for detection of cracks at fastener holes.” NDT and E Int. 42 (2): 141–149. https://doi.org/10.1016/j.ndteint.2008.10.008.
Irwin, G. R. 1960. “Plastic zone near a crack and fracture toughness.” In Proc., 7th Sagamore Ordnance Materials Research Conf., 4, 63–78, New York: Syracuse University.
Lee, J. J., and M. Shinozuka. 2006. “A vision-based system for remote sensing of bridge displacement.” NDT and E Int. 39 (5): 425–431. https://doi.org/10.1016/j.ndteint.2005.12.003.
Lorenzino, P., G. Beretta, and A. Navarro. 2014. “Application of digital image correlation (DIC) in resonance machines for measuring fatigue crack growth.” Frattura Integr. Strutt. 8 (30): 369–374. https://doi.org/10.3221/IGF-ESIS.30.44.
Nowell, D., R. J. H. Paynter, and P. F. P. Matos. 2010. “Optical methods for measurement of fatigue crack closure: Moiré interferometry and digital image correlation.” Fatigue Fract. Eng. Mater. Struct. 33 (12): 778–790. https://doi.org/10.1111/j.1460-2695.2010.01447.x.
Pan, B., L. Tian, and X. Song. 2016. “Real-time, non-contact and targetless measurement of vertical deflection of bridges using off-axis digital image correlation.” NDT and E Int. 79: 73–80. https://doi.org/10.1016/j.ndteint.2015.12.006.
Parker, J. R. 2011. Algorithms for image processing and computer vision. 2nd ed. New York: Wiley.
Ribeiro, D., R. Calçada, J. Ferreira, and T. Martins. 2014. “Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system.” Eng. Struct. 75: 164–180. https://doi.org/10.1016/j.engstruct.2014.04.051.
Rupil, J., S. Roux, F. Hild, and L. Vincent. 2011. “Fatigue microcrack detection with digital image correlation.” J. Strain Anal. Eng. Des. 46 (6): 492–509. https://doi.org/10.1177/0309324711402764.
Sutton, M. A., J. Yan, X. Deng, C. Cheng, and P. D. Zavattieri. 2007. “Three-dimensional digital image correlation to quantify deformation and crack-opening displacement in ductile aluminum under mixed-mode I/III loading.” Opt. Eng. 46 (5): 051003. https://doi.org/10.1117/1.2741279.
Sutton, M. A., J. Orteu, and H. W. Schreier. 2009. Digital image correlation (DIC). Image correlation for shape, motion and deformation measurements: Basic concepts, theory and applications. New York: Springer.
Vanlanduit, S., J. Vanherzeele, R. Longo, and P. Guillaume. 2009. “A digital image correlation method for fatigue test experiments.” Opt. Lasers Eng. 47 (3–4): 371–378. https://doi.org/10.1016/j.optlaseng.2008.03.016.
Whitehead, J. 2015. “Probability of detection study for visual inspection of steel bridges.” M.S.C.E. thesis, Lyles School of Civil Engineering, Purdue Univ.
Zhang, R., and L. He. 2012. “Measurement of mixed-mode stress intensity factors using digital image correlation method.” Opt. Lasers Eng. 50 (7): 1001–1007. https://doi.org/10.1016/j.optlaseng.2012.01.009.
Zhao, Y., and W. M. K. Roddis. 2004. Fatigue prone steel bridge details: Investigation and recommended repairs. Final Rep. No. K-TRAN: KU-99-2. Topeka, KS: Kansas Dept. of Transportation.
Zhao, Z., and A. Haldar. 1996. “Bridge fatigue damage evaluation and updating using non-destructive inspections.” Eng. Fract. Mech. 53 (5): 775–788. https://doi.org/10.1016/0013-7944(95)00136-0.
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Published In
Journal of Bridge Engineering
Volume 25 • Issue 9 • September 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Sep 3, 2019
Accepted: Apr 22, 2020
Published online: Jun 30, 2020
Published in print: Sep 1, 2020
Discussion open until: Nov 30, 2020
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