PSI - Issue 17

Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689

689

Al-Salih/ Structural Integrity Procedia 00 (2019) 000 – 000

8

Predicted crack lengths were examined for convergence values of 90% and 95%, and the results for each applicable load case are presented in Table 3. The crack characterization methodology consistently and significantly under-predicted the branched vertical crack. This is possibly due to the fact that the initiation of the horizontal branch has reduced the driving force being experienced by the vertical branch arresting growth and greatly reducing any crack movement.

6. Conclusions and Future Work

This research has evaluated a methodology for characterizing fatigue cracks in steel bridges. The methodology developed uses DIC displacement data to quantify crack length. Previous work had shown promise for the developed methodology, as it was able to accurately quantify crack length for in-plane cracks as well as out-of-plane, distortion-induced fatigue cracks. Application of the methodology to data collected for a geometrically complex, bifurcated crack proved difficult. On average, 95% convergence predicted crack lengths for the horizontal branch of the crack within 10% of the optically measured crack length. For the vertical branch of the crack, however, the DIC method significantly under-predicted crack length. It is hypothesized that this is because crack bifurcation reduced the driving force seen by the vertical branch of the crack, greatly reducing the crack opening when subjected to loading. Additionally, at low levels of load the calculations for convergence produce extremely varying results. This loading threshold appears to be below that which would be caused by normal truck traffic on a highway bridge. Questions still exist related to the limitations of the process to produce accurate and reliable results in non-ideal conditions. Testing using DIC has primarily occurred in a laboratory setting with idealized conditions. This means that artificial lighting was added, the cameras were in-focus, and the surface preparation was of high quality. Ongoing work is examining the limitations of the software and proposed algorithms, primarily in terms of lighting conditions, camera focus, and image stability. Identifying fatigue cracks using an automated methodology has the potential to reduce the cost of inspecting and maintaining steel highway bridges, and to increase safety for inspectors and the traveling public. Digital image correlation has shown potential in quantifying the lengths of both in-plane and out-of-plane fatigue cracks. Additional testing is needed to identify and evaluate the limitations of the developed method allowing for the possibility of moving towards future automated application of this tool in bridge inspections. Varying surface conditions also need to be studied, examining whether DIC is capable of collecting data without the application of an ideal high-contrast surface coating. Abdel-Qader, I., Abudayyeh, O., & Kelly, M. E., 2003. Analysis of edge-detection techniques for crack identification in bridges. Journal of Computing in Civil Engineering , 17(4), 255-263. Cha, Y. J., Choi, W., & Büyüköztürk, O., 2017. Deep Learning ‐ Based Crack Damage Detection Using Convolutional Neural Networks. Computer ‐ Aided Civil and Infrastructure Engineering , 32(5), 361-378. Connor, R. J., & Fisher, J. W., 2006. Identifying effective and ineffective retrofits for distortion fatigue cracking in steel bridges using field instrumentation. Journal of Bridge Engineering , 11(6), 745-752. Federal Highway Administration (FHWA). 2004. National bridge inspection standards , Federal Register, 69 (239) Kong, X. and Li, J. (2018). Vision-based Fatigue Crack Detection of Steel Structures Using Video Feature Tracking. Computer-Aided Civil and Infrastructure Engineering , 33(9), 783-799. Küntz, M., Jolin, M., Bastien, J., Perez, F., & Hild, F., 2006. Digital image correlation analysis of crack behavior in a reinforced concrete beam during a load test. Canadian Journal of Civil Engineering, 33(11), 1418-1425. doi:10.1139/l06-106 Sutton, M. A., 2007. Three-dimensional digital image correlation to quantify deformation and crack-opening displacement in ductile aluminum under mixed-mode I/III loading. Optical Engineering, 46(5), 051003. doi:10.1117/1.2741279 Whitehead, J., 2015. “Probability of detection study for visual inspection of steel bridges.” Master’ s Thesis, Purdue University, West Lafayette, IN. Yamaguchi, T., & Hashimoto, S., 2010. Fast crack detection method for large-size concrete surface images using percolation-based image processing. Machine Vision and Applications , 21(5), 797-809. Zhang, R., & He, L., 2012. Measurement of mixed-mode stress intensity factors using digital image correlation method. Optics and Lasers in Engineering, 50(7), 1001 1007. doi:10.1016/j.optlaseng.2012.01.009 Zhao Z, and Haldar A. 1996. Bridge fatigue damage evaluation and updating using non-destructive inspections. Engineering fracture mechanics . 53(5), 775-88 Zou, Q., Cao, Y., Li, Q., Mao, Q., & Wang, S. 2012. CrackTree: Automatic crack detection from pavement images. Pattern Recognition Letters, 33(3), 227-238. Acknowledgements 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