- Email: [email protected]
Evaluation of a Digital Image Correlation Bridge Inspection Methodology on Complex Distortion-Induced Fatigue Cracking
Available online at www.sciencedirect.com
ScienceDirect
Available online at www.sciencedirect.com
ScienceDirect
Structural Integrity Procedia 00 (2019) 000–000
Available online at www.sciencedirect.com
Structural Integrity Procedia 00 (2019) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Structural Integrity 17 (2019) 682–689
ICSI 2019 The 3rd International Conference on Structural Integrity 2019 The 3rd International Conference on Structural Integrity Evaluation of a ICSI Digital Image Correlation Bridge Inspection Methodology on Distortion-Induced Fatigue Cracking Evaluation of aComplex Digital Image Correlation Bridge Inspection Methodology on
Hayder Al-Saliha, Mary Junoa, William Collinsa*, Caroline BennettaCracking , Jian Lia, and Elaina J. Sutleya Complex Distortion-Induced Fatigue University of Kansas Department of Civil, Environmental, and Architectural Engineering, 2150 Learned Hall 1530 W. 15 th Stree, Lawrence, KS 66045
a
Hayder Al-Saliha, Mary Junoa, William Collinsa*, Caroline Bennetta, Jian Lia, and Elaina J. Sutleya a University of Kansas Department of Civil, Environmental, and Architectural Engineering, 2150 Learned Hall 1530 W. 15 th Stree, Lawrence, KS 66045 Abstract
Distortion-induced fatigue cracking in aging steel bridges is a primary concern for many bridge owners and stakeholders, accounting for the Abstract majority of fatigue cracks in bridges in the United States. Currently Departments of Transportation primarily use visual inspections to locate and characterize fatigue cracks. However, this approach has drawbacks, as recent studies have shown that visual inspections are not able to consistently Distortion-induced fatigue cracking in aging steel bridges is a primary concern for many bridge owners and stakeholders, accounting for the identify realistically-sized fatigue cracks in highway infrastructure. Additionally, it requires significant time and cost to perform visual inspections, majority of fatigue cracks in bridges in the United States. Currently Departments of Transportation primarily use visual inspections to locate and and both inspectors and the traveling public are placed at risk during the inspection. Bridge owners and stakeholders would benefit from the characterize fatigue cracks. However, this approach has drawbacks, as recent studies have shown that visual inspections are not able to consistently development of inspection techniques that do not rely on hands-on human visual inspection – to reduce the risks of harm to the inspectors and traveling identify realistically-sized fatigue cracks in highway infrastructure. Additionally, it requires significant time and cost to perform visual inspections, public, increase reliability, and decrease the time and cost of performing inspections. Vision-based technologies are an active area of research in the and both inspectors and the traveling public are placed at risk during the inspection. Bridge owners and stakeholders would benefit from the field of structural health monitoring, aimed at developing alternatives to manual inspection for identifying damage to transportation infrastructure. development of inspection techniques that do not rely on hands-on human visual inspection – to reduce the risks of harm to the inspectors and traveling The majority of these studies have focused on macro-indicators of damage, including identifying excessive corrosion, concrete deck deterioration, public, increase reliability, and decrease the time and cost of performing inspections. Vision-based technologies are an active area of research in the and large displacements due to substructure movement. Digital image correlation (DIC) is one such vision-based technology that shows promise for field of structural health monitoring, aimed at developing alternatives to manual inspection for identifying damage to transportation infrastructure. detecting and characterizing fatigue cracks, but investigations and applications have thus far been limited to simple, in-plane cracking. ThreeThe majority of these studies have focused on macro-indicators of damage, including identifying excessive corrosion, concrete deck deterioration, dimensional DIC measurements have the ability to capture full-field displacements and surface strains, allowing for the potential of identifying and and large displacements due to substructure movement. Digital image correlation (DIC) is one such vision-based technology that shows promise for characterizing out-of-plane fatigue cracks, such as those occurring on steel bridges exposed to loading through differential girder displacement. Since detecting and characterizing fatigue cracks, but investigations and applications have thus far been limited to simple, in-plane cracking. Threethe majority of fatigue cracks arise from out-of-plane loading, investigating the efficacy of DIC for detecting distortion-induced fatigue cracks is of dimensional DIC measurements have the ability to capture full-field displacements and surface strains, allowing for the potential of identifying and clear value. This paper describes an experimental study in which a scaled steel girder-to-cross-frame specimen was cyclically loaded to produce characterizing out-of-plane fatigue cracks, such as those occurring on steel bridges exposed to loading through differential girder displacement. Since geometrically complex distortion-induced fatigue cracking. To examine the potential usefulness of DIC for future automated bridge inspections the the majority of fatigue cracks arise from out-of-plane loading, investigating the efficacy of DIC for detecting distortion-induced fatigue cracks is of resulting fatigue cracks were characterized using DIC and visual inspection. The methodology applied proved successful at quantifying certain aspects clear value. This paper describes an experimental study in which a scaled steel girder-to-cross-frame specimen was cyclically loaded to produce of the complex, bifurcating cracks, but had difficulty characterizing crack segments that are no longer actively loaded. Additional work is needed to geometrically complex distortion-induced fatigue cracking. To examine the potential usefulness of DIC for future automated bridge inspections the improve the accuracy and reliability of the crack detection results for this technology to potentially be used as an automated inspection tool in the resulting fatigue cracks were characterized using DIC and visual inspection. The methodology applied proved successful at quantifying certain aspects future. of the complex, bifurcating cracks, but had difficulty characterizing crack segments that are no longer actively loaded. Additional work is needed to improve the accuracy and reliability of the crack detection results for this technology to potentially be used as an automated inspection tool in the © 2019 The Authors. Published by Elsevier B.V. future. Peer-review under responsibility of the ICSI 2019 organizers. © Published by Elsevier B.V.B.V. © 2019 2019The TheAuthors. Authors. Published by Elsevier Peer-review responsibility of the ICSIICSI 20192019 organizers. Peer-reviewunder under responsibility of the organizers.
* Corresponding author. Tel.: +1-785-864-0672; fax: +1-785-864-5631. E-mail address: [email protected] * Corresponding Tel.: +1-785-864-0672; fax: +1-785-864-5631. 2452-3216 © 2019 author. The Authors. Published by Elsevier B.V. E-mail address: [email protected] Peer-review under responsibility of the ICSI 2019 organizers. 2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers.
2452-3216 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. 10.1016/j.prostr.2019.08.091
2
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689
683
Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
Keywords: fatigue; distortion-induced fatigue; cross-frame; digital image correlation; bridge inspection.
1. Introduction and Background 1.1. Fatigue Cracking and Inspection of Bridges Distortion-induced fatigue cracks account for almost 90% of fatigue cracks in aging steel bridges in the United States (Connor and Fisher 2006). Older steel bridges were often designed such that no connection was provided between the flanges and connection plates. When a bridge experiences traffic loading, it undergoes differential deflection between the girders. This allows cross-frames to push or pull on girder webs resulting in secondary out-of-plane stresses applied to the weak web gap regions causing distortion-induced fatigue. In order to mitigate the impact of distortion-induced fatigue, aging bridges are required to undergo regular inspections, and often require repairs and retrofitting. Bridge inspections are typically performed on a 24-month cycle (FHWA 2004), and visual inspection is the most common approach for detecting fatigue cracks. One of the challenges with visual bridge inspection is that fatigue cracks are initially small, and therefore difficult to detect through visual inspections. Undetected cracks, however, can propagate to a size that has the potential to compromise the structural integrity of the bridge. Although these inspections are required to improve the safety of bridge infrastructure by identifying and monitoring cracks over time, manual visual inspections are expensive, time consuming, and dangerous for bridge inspectors and drivers. Additionally, successfully identifying realistic fatigue cracks has been shown to be extremely difficult (Whitehead 2015; Zhao and Haldar 1996). Researchers in various fields have examined technologies to detect and monitor cracks, but many of the approaches are dependent on sensors or other physical attachments. This prevents the detection methods from effectively monitoring the various fatigue susceptible regions on steel bridges. A vision-based, non-contact approach that does not require physical attachment would allow for large areas of bridges to be surveyed in a much safer and efficient manner. While research on vision-based crack detection methods have been conducted, testing has primarily occurred under idealized conditions looking only at in-plane fatigue loading or at cracks in non-metallic materials. Few research programs have evaluated visionbased crack detection methods on out-of-plane loading with the complex geometries found on steel highway bridges. In this paper, the performance of vision-based crack detection is being examined using digital image correlation on an out-of-plane test setup with a geometrically complex crack. 1.2. Computer Vision Computer vision refers to technology that uses optics and computer algorithms to collect information from pictures and videos. Various forms of computer vision have been used in engineering and material science disciplines to characterize mechanical parameters. Many researchers have evaluated the potential of using computer vision in the context of crack detection for various materials. For example, researchers have used edge detection to find edge-like features in digital images, leading to the detection and localization of cracks in concrete surfaces (Abdel-Qader et al. 2003). There has been work to develop algorithms to remove short, thick, or exceedingly linear edges, in the hopes of detecting cracking in concrete and asphalt pavement (Yamaguchi and Hashimoto 2010; Zou et al. 2012; Cha et al. 2017). These materials typically have large crack openings, as well as high contrast between cracked and uncracked regions; due to differences in the material, the application of edge detection in evaluating cracks is challenging for steel bridges. In metallic materials, there is a high rate of false positives when using edge detection to identify cracks. The false positives occur from inadvertent detection of corrosion, surface textures, defects, and component boundaries. Kong and Li developed a computer vision strategy to detect fatigue cracks by tracking structural surface motion in a short video, but identification of the crack tip remains a challenge (2018). 1.3. Digital Image Correlation Digital image correlation (DIC) is a subcategory of computer vision that uses image analysis to generate surface displacement measurements. The full-field displacement measurements can then be used to develop three-dimensional strain fields. DIC software can be utilized for both two-dimensional and three-dimensional analysis, depending on the number of cameras used when testing. DIC
684
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689 Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
3
uses a series of images taken during loading and compares the images to obtain relative displacement and strain. DIC has shown potential for detecting and characterizing fatigue cracks, but testing has been limited to simplified test setups. DIC has been used in material testing to measure deformation and strain, and DIC can serve as an alternative to traditional sensing technology, such as strain gauges. Using DIC to evaluate cracking has been applied in the calculations of stress intensity factors (Zhang and He 2012) and to detect cracks in a concrete structure (Küntz et al. 2006). While research on vision-based crack detection methods have been conducted, testing has primarily occurred under idealized conditions looking only at in-plane fatigue loading or at cracks in non-metallic materials. Very few studies have been conducted evaluating vision-based crack detection methods on out-of-plane loading with the complex geometries found on steel highway bridges. Researchers have theorized that out-of-plane test setups have not been evaluated due to the inherent complexity and sophistication required (Sutton et al. 2007). Obtaining accurate results from DIC is dependent on the preparation of the specimen, the camera setup, calibration, and image collection. The preparation of the specimen involves applying a high-contrast random speckle pattern to the material surface, creating points of reference for image comparison. The necessary camera setup is dependent on the complexity of the specimen being tested. In-plane fatigue specimens experiencing no out-of-plane deformations require only two-dimensional analyses, necessitating the use of only a single camera. When testing for out-of-plane displacement, however, two cameras are required to calculate a three-dimensional displacement field. 2. DIC Crack Characterization Methodology 2.1. Initial Testing and Methodology Development To move towards automated fatigue crack inspection, crack characterization must be quantified. Initial single-camera DIC testing was performed on a compact (C(T)) specimen subjected to in-plane loading on a servo-hydraulic testing machine. The C(T) specimen was 6.35 mm (0.25 in.) thick with a width of 127 mm (5.0 in.). The large specimen was capable of accommodating extensive crack growth, allowing for testing at a variety of crack lengths. As bridge loading is highly variable, multiple load cases were defined and applied based on stress intensity ranges of 11, 22, 33, 44, and 55 MPa√m (10, 20, 30, 40, and 50 ksi√in). Crack tip plasticity was limited during testing by applying the lowest stress intensity first and increasing to the highest. DIC data was recorded for each load case at crack lengths ranging from 12.7 to 50.8 mm (0.5 to 2.0 in.) in 12.7 mm (0.5 in.) increments. Although the general crack location could be identified in the visualized DIC results, the goal of automation prompted the development of algorithms to determine crack length from the DIC data. The twenty in-plane data sets were used to develop a method for this quantification. 2.2. Crack Characterization Methodology Although edge detection algorithms often produce false positive results when attempting to identify fatigue cracks on steel bridges, they can work well for images analyzed with DIC. Images of DIC-produced surface displacement contours were analyzed using commercially-available edge detection algorithms to initially identify the crack path. The coordinates of the crack path were extended linearly beyond the end of the crack tip as identified by edge detection. This linear extension of the crack path was necessary due to the inability of the edge detection process to accurately identify the crack tip. After identification of the crack path, differential surface displacements across the crack and perpendicular to the idealized crack path were calculated. This is schematically represented in Fig. 1a. Relative displacement at each point along the crack path, Δi, was then divided by the maximum relative displacement, Δmax. A convergence value was defined by subtracting this ratio of relative displacements from 100%, calculated as in Eq. (1) and plotted in Fig. 1b. Theoretically, convergence values should approach 100% near the crack tip as crack openings go to zero. However, known crack tip locations on the C(T) specimens typically corresponded with convergence values between 90% and 95%. At higher load cases convergence values typically decreased, indicating relative displacement continued beyond the actual crack tip location. This is likely due to larger amounts of crack tip plasticity occurring at higher applied stress intensities. 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 = 100% −
∆𝑖𝑖
∆𝑚𝑚𝑚𝑚𝑚𝑚
(1)
2.3. Application to Distortion-Induced Fatigue Loading The crack characterization methodology based on in-plane C(T) specimens was applied to data obtained from a half-scale girderto-cross-frame subassembly. This bridge component specimen allowed for loading with a realistic distortion-induced fatigue
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689
Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
4
685
mechanism. Loading out-of-plane for 21,000 cycles produced a vertical fatigue crack at the stiffener to web weld. The 44.5 mm (1.75 in.) crack was composed of three linear sections, with two vertical segments connected by small diagonal segment. Similar to the inplane C(T) tests, multiple load levels were defined based on realistic fatigue loading. A stereo-camera setup was used to obtain threedimensional displacement data, and convergence was calculated based on displacement in the direction perpendicular to the girder web. Using convergence values of 90% and 95%, crack lengths were calculated using the DIC crack characterization methodology. For all load cases, the average total crack length corresponding to 90% convergence was 40.6 mm (1.59 in.), while 95% convergence predicted an average crack length of 45.0 mm (1.77 in.). These predictions under-predict and over-predict the crack length by 9% and 1%, respectively, indicating the 90% to 95% convergence range is appropriate for distortion-induced fatigue cracks subjected to outof-plane loading. However, it should be noted that the although more geometrically-complex than the linear crack on the in-plane C(T) specimen, the out-of-plane fatigue crack was fairly simple for a distortion-induced fatigue crack. Often cracks on steel highway bridges can be extremely complex, with multiple cracks located in the same region and bifurcation creating two or more crack tips on individual cracks.
Fig. 1. a) Relative displacement along length of crack path and b) Convergence along length of crack path
3. Objective The objective of the study discussed herein was to evaluate the ability of a DIC-based fatigue crack characterization methodology to quantify a complex, bifurcated out-of-plane crack subjected to distortion-induced fatigue. A branching crack was tested under outof-plane loading, and DIC data was collected at various load levels. The efficacy of the DIC crack characterization methodology was examined for two bifurcated crack tips emanating from a single crack. 4. Experimental Approach 4.1. Girder Test Setup and Loading A girder-to-cross-frame subassembly, shown in Fig. 2a, was used for distortion-induced fatigue testing. The half-scale girder, fabricated from A36 steel, had a length of 2845 mm (112 in.), a depth of 917 mm (36.1 in.), and a web thickness of 10 mm (0.375 in.). The bottom flange of the girder subassembly was connected to the reaction floor of the laboratory, restraining it from out-of-plane motion. This restraint approximates how the axial stiffness of a concrete deck restrains out-of-plane motion of a bridge flange. At the girder mid-span a cross-frame was installed and attached to a connection plate. The connection plate was welded to the girder web only, creating the web gap region found on older structures. Load was vertically applied to the cross-frame through the use of a hydraulic actuator, the attachment of which can be seen in the top left corner of Fig. 2a. Vertical displacements at the end of the crossframe produce realistic distortion-induced fatigue loading in the web gap region, resulting in load applied vertically and out-of-plane with respect to the girder web.
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689
686
Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
5
4.2. Complex Crack Geometry The previously existing fatigue crack was loaded cyclically at a range from 2.2 to 25.5 kN (0.5 to 5.75 kips) for 73,000 additional cycles, causing crack propagation and bifurcation. This is highlighted in Fig. 2b, where black lines have been drawn over the fatigue crack. One branch of the bifurcated crack, segment B-C, propagated vertically up the stiffener-to-web weld, while segment B-D grew horizontally into the girder web. For the purposes of crack characterization, these will be presented as two separate cracks with a shared initiation site: the vertical branch (A-B-C) and the horizontal branch (A-B-D). Inspection of the web gap revealed the total length of the vertical branch was 90.3 mm (3.55 in.) and the total length of the horizontal branch was 75.1 mm (2.95 in.).
Fig. 2. a) Distortion-induced fatigue subassembly and b) bifurcated crack in the web gap region
4.3. Loading Protocol Finite element analysis was used to determine appropriate levels of load for the distortion-induced fatigue subassembly. An analytical model of a steel girder bridge was evaluated with the AASHTO fatigue truck. Differential girder displacements determined in the model were scaled for the test specimen resulting in a target deflection of 1.25 mm (0.05 in.). This deflection corresponded to an applied actuator load of 6.6 kN (1.5 kips), and seven load cases were defined both above and below this threshold. Each load case, presented in Table 1, had a minimum load of 0.89 kN (0.2 kips) simulating dead load. Table 1. Loading Protocol for Distortion-Induced Fatigue Testing Load Case
Load Range, kN (kips)
LC1
0.89-2.2 (0.2-0.5)
LC2
0.89-4.4 (0.2-1.0)
LC3
0.89-6.7 (0.2-1.5)
LC4
0.89-8.9 (0.2-2.0)
LC5
0.89-11.1 (0.2-2.5)
LC6
0.89-13.3 (0.2-3.0)
LC7
0.89-15.6 (0.2-3.5)
5. Results 5.1. DIC Results DIC data was collected for the bifurcated out-of-plane crack for each load case. Typical DIC results, visualized for both strain and displacement, are shown in Fig. 3. The majority of the crack is clearly visible in both the strain and displacement images, although the
6
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689
Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
687
vertical branch is difficult to see. Due to the complex geometry of the crack and multi-axial fatigue loading, displacements were examined in all three principal directions, and resultant differential displacements were calculated for use with the crack characterization methodology. As described above, differential displacements were taken orthogonal to the crack path, and convergence was calculated along the length of the crack path.
Fig. 3. Typical DIC results of the distortion-induced fatigue specimen visualized in terms of a) strain and b) displacement
5.2. Horizontal Branch Characterization Results Horizontal branch relative displacement along the crack path is presented in Fig. 4a for all load cases. Calculated convergence values are shown in Fig. 4b. In the figures the actual crack length of 75.1 mm (2.95 in.) is represented by a vertical dotted line. It should be noted that displacements caused by load case 1 were extremely small, causing extreme variation in convergence values. Due to this, load case 1 convergence values are not presented. Using 90% and 95% convergence, crack lengths were predicted and compared with the optically measured length. The predicted crack lengths and error, expressed as a percentage, are presented in Table 2. Using a value of 90% convergence under-predicted crack lengths by an average of 27%. The 95% convergence performed much better for the horizontal branch, under-predicting crack length by an average of only 10%.
Fig. 4. Horizontal branch a) Relative displacement and b) Convergence of relative displacement
5.3. Vertical Branch Characterization Results Relative displacement along the vertical branch crack path for all load cases is shown in Fig. 5a, and convergence values are presented in Fig. 5b. The optically measured crack length of 90.3 mm (3.55 in.) is indicated by the vertical dotted lines. Again, load case 1 convergence values are not presented as small displacements cause extreme variability. It can be seen that for most load cases relative displacement values approach zero well before the tip of the crack causing convergence values to greatly under-predict crack length.
688
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689 Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
7
Table 2. Horizontal branch length characterization 90% Convergence Load Case
Crack Length, mm (in.)
Error, %
LC1
N/A
LC2
50.2 (1.98)
LC3
50.2 (1.98)
95% Convergence Crack Length, mm (in.)
Error, %
N/A
N/A
N/A
-33
65.5 (2.58)
-13
-33
64.7 (2.55)
-14
LC4
51.2 (2.02)
-32
64.3 (2.53)
-14
LC5
51.8 (2.04)
-31
64.4 (2.54)
-14
LC6
60.9 (2.40)
-19
71.9 (2.83)
-4
LC7
63.2 (2.49)
-16
73.8 (2.90)
-2
Average
54.6 (2.15)
-27
67.4 (2.65)
-10
Fig. 5. Vertical branch a) Relative displacement and b) Convergence of relative displacement
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. Table 3. Vertical branch length characterization 90% Convergence Load Case
Crack Length, mm (in.)
Error, %
LC1
N/A
LC2
51.0 (2.01)
LC3
50.2 (1.98)
95% Convergence Crack Length, mm (in.)
Error, %
N/A
N/A
N/A
-44
56.3 (2.22)
-38
-44
54.3 (2.14)
-40
LC4
51.3 (2.02)
-43
54.7 (2.15)
-39
LC5
51.9 (2.04)
-43
55.0 (2.17)
-39
LC6
53.7 (2.11)
-40
57.0 (2.24)
-37
LC7
54.3 (2.14)
-40
57.5 (2.26)
-36
Average
53.0 (2.05)
-42
55.8 (2.20)
-38
8
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689
Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
689
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. 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 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), 10011007. 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.
ScienceDirect
Available online at www.sciencedirect.com
ScienceDirect
Structural Integrity Procedia 00 (2019) 000–000
Available online at www.sciencedirect.com
Structural Integrity Procedia 00 (2019) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Structural Integrity 17 (2019) 682–689
ICSI 2019 The 3rd International Conference on Structural Integrity 2019 The 3rd International Conference on Structural Integrity Evaluation of a ICSI Digital Image Correlation Bridge Inspection Methodology on Distortion-Induced Fatigue Cracking Evaluation of aComplex Digital Image Correlation Bridge Inspection Methodology on
Hayder Al-Saliha, Mary Junoa, William Collinsa*, Caroline BennettaCracking , Jian Lia, and Elaina J. Sutleya Complex Distortion-Induced Fatigue University of Kansas Department of Civil, Environmental, and Architectural Engineering, 2150 Learned Hall 1530 W. 15 th Stree, Lawrence, KS 66045
a
Hayder Al-Saliha, Mary Junoa, William Collinsa*, Caroline Bennetta, Jian Lia, and Elaina J. Sutleya a University of Kansas Department of Civil, Environmental, and Architectural Engineering, 2150 Learned Hall 1530 W. 15 th Stree, Lawrence, KS 66045 Abstract
Distortion-induced fatigue cracking in aging steel bridges is a primary concern for many bridge owners and stakeholders, accounting for the Abstract majority of fatigue cracks in bridges in the United States. Currently Departments of Transportation primarily use visual inspections to locate and characterize fatigue cracks. However, this approach has drawbacks, as recent studies have shown that visual inspections are not able to consistently Distortion-induced fatigue cracking in aging steel bridges is a primary concern for many bridge owners and stakeholders, accounting for the identify realistically-sized fatigue cracks in highway infrastructure. Additionally, it requires significant time and cost to perform visual inspections, majority of fatigue cracks in bridges in the United States. Currently Departments of Transportation primarily use visual inspections to locate and and both inspectors and the traveling public are placed at risk during the inspection. Bridge owners and stakeholders would benefit from the characterize fatigue cracks. However, this approach has drawbacks, as recent studies have shown that visual inspections are not able to consistently development of inspection techniques that do not rely on hands-on human visual inspection – to reduce the risks of harm to the inspectors and traveling identify realistically-sized fatigue cracks in highway infrastructure. Additionally, it requires significant time and cost to perform visual inspections, public, increase reliability, and decrease the time and cost of performing inspections. Vision-based technologies are an active area of research in the and both inspectors and the traveling public are placed at risk during the inspection. Bridge owners and stakeholders would benefit from the field of structural health monitoring, aimed at developing alternatives to manual inspection for identifying damage to transportation infrastructure. development of inspection techniques that do not rely on hands-on human visual inspection – to reduce the risks of harm to the inspectors and traveling The majority of these studies have focused on macro-indicators of damage, including identifying excessive corrosion, concrete deck deterioration, public, increase reliability, and decrease the time and cost of performing inspections. Vision-based technologies are an active area of research in the and large displacements due to substructure movement. Digital image correlation (DIC) is one such vision-based technology that shows promise for field of structural health monitoring, aimed at developing alternatives to manual inspection for identifying damage to transportation infrastructure. detecting and characterizing fatigue cracks, but investigations and applications have thus far been limited to simple, in-plane cracking. ThreeThe majority of these studies have focused on macro-indicators of damage, including identifying excessive corrosion, concrete deck deterioration, dimensional DIC measurements have the ability to capture full-field displacements and surface strains, allowing for the potential of identifying and and large displacements due to substructure movement. Digital image correlation (DIC) is one such vision-based technology that shows promise for characterizing out-of-plane fatigue cracks, such as those occurring on steel bridges exposed to loading through differential girder displacement. Since detecting and characterizing fatigue cracks, but investigations and applications have thus far been limited to simple, in-plane cracking. Threethe majority of fatigue cracks arise from out-of-plane loading, investigating the efficacy of DIC for detecting distortion-induced fatigue cracks is of dimensional DIC measurements have the ability to capture full-field displacements and surface strains, allowing for the potential of identifying and clear value. This paper describes an experimental study in which a scaled steel girder-to-cross-frame specimen was cyclically loaded to produce characterizing out-of-plane fatigue cracks, such as those occurring on steel bridges exposed to loading through differential girder displacement. Since geometrically complex distortion-induced fatigue cracking. To examine the potential usefulness of DIC for future automated bridge inspections the the majority of fatigue cracks arise from out-of-plane loading, investigating the efficacy of DIC for detecting distortion-induced fatigue cracks is of resulting fatigue cracks were characterized using DIC and visual inspection. The methodology applied proved successful at quantifying certain aspects clear value. This paper describes an experimental study in which a scaled steel girder-to-cross-frame specimen was cyclically loaded to produce of the complex, bifurcating cracks, but had difficulty characterizing crack segments that are no longer actively loaded. Additional work is needed to geometrically complex distortion-induced fatigue cracking. To examine the potential usefulness of DIC for future automated bridge inspections the improve the accuracy and reliability of the crack detection results for this technology to potentially be used as an automated inspection tool in the resulting fatigue cracks were characterized using DIC and visual inspection. The methodology applied proved successful at quantifying certain aspects future. of the complex, bifurcating cracks, but had difficulty characterizing crack segments that are no longer actively loaded. Additional work is needed to improve the accuracy and reliability of the crack detection results for this technology to potentially be used as an automated inspection tool in the © 2019 The Authors. Published by Elsevier B.V. future. Peer-review under responsibility of the ICSI 2019 organizers. © Published by Elsevier B.V.B.V. © 2019 2019The TheAuthors. Authors. Published by Elsevier Peer-review responsibility of the ICSIICSI 20192019 organizers. Peer-reviewunder under responsibility of the organizers.
* Corresponding author. Tel.: +1-785-864-0672; fax: +1-785-864-5631. E-mail address: [email protected] * Corresponding Tel.: +1-785-864-0672; fax: +1-785-864-5631. 2452-3216 © 2019 author. The Authors. Published by Elsevier B.V. E-mail address: [email protected] Peer-review under responsibility of the ICSI 2019 organizers. 2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers.
2452-3216 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. 10.1016/j.prostr.2019.08.091
2
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689
683
Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
Keywords: fatigue; distortion-induced fatigue; cross-frame; digital image correlation; bridge inspection.
1. Introduction and Background 1.1. Fatigue Cracking and Inspection of Bridges Distortion-induced fatigue cracks account for almost 90% of fatigue cracks in aging steel bridges in the United States (Connor and Fisher 2006). Older steel bridges were often designed such that no connection was provided between the flanges and connection plates. When a bridge experiences traffic loading, it undergoes differential deflection between the girders. This allows cross-frames to push or pull on girder webs resulting in secondary out-of-plane stresses applied to the weak web gap regions causing distortion-induced fatigue. In order to mitigate the impact of distortion-induced fatigue, aging bridges are required to undergo regular inspections, and often require repairs and retrofitting. Bridge inspections are typically performed on a 24-month cycle (FHWA 2004), and visual inspection is the most common approach for detecting fatigue cracks. One of the challenges with visual bridge inspection is that fatigue cracks are initially small, and therefore difficult to detect through visual inspections. Undetected cracks, however, can propagate to a size that has the potential to compromise the structural integrity of the bridge. Although these inspections are required to improve the safety of bridge infrastructure by identifying and monitoring cracks over time, manual visual inspections are expensive, time consuming, and dangerous for bridge inspectors and drivers. Additionally, successfully identifying realistic fatigue cracks has been shown to be extremely difficult (Whitehead 2015; Zhao and Haldar 1996). Researchers in various fields have examined technologies to detect and monitor cracks, but many of the approaches are dependent on sensors or other physical attachments. This prevents the detection methods from effectively monitoring the various fatigue susceptible regions on steel bridges. A vision-based, non-contact approach that does not require physical attachment would allow for large areas of bridges to be surveyed in a much safer and efficient manner. While research on vision-based crack detection methods have been conducted, testing has primarily occurred under idealized conditions looking only at in-plane fatigue loading or at cracks in non-metallic materials. Few research programs have evaluated visionbased crack detection methods on out-of-plane loading with the complex geometries found on steel highway bridges. In this paper, the performance of vision-based crack detection is being examined using digital image correlation on an out-of-plane test setup with a geometrically complex crack. 1.2. Computer Vision Computer vision refers to technology that uses optics and computer algorithms to collect information from pictures and videos. Various forms of computer vision have been used in engineering and material science disciplines to characterize mechanical parameters. Many researchers have evaluated the potential of using computer vision in the context of crack detection for various materials. For example, researchers have used edge detection to find edge-like features in digital images, leading to the detection and localization of cracks in concrete surfaces (Abdel-Qader et al. 2003). There has been work to develop algorithms to remove short, thick, or exceedingly linear edges, in the hopes of detecting cracking in concrete and asphalt pavement (Yamaguchi and Hashimoto 2010; Zou et al. 2012; Cha et al. 2017). These materials typically have large crack openings, as well as high contrast between cracked and uncracked regions; due to differences in the material, the application of edge detection in evaluating cracks is challenging for steel bridges. In metallic materials, there is a high rate of false positives when using edge detection to identify cracks. The false positives occur from inadvertent detection of corrosion, surface textures, defects, and component boundaries. Kong and Li developed a computer vision strategy to detect fatigue cracks by tracking structural surface motion in a short video, but identification of the crack tip remains a challenge (2018). 1.3. Digital Image Correlation Digital image correlation (DIC) is a subcategory of computer vision that uses image analysis to generate surface displacement measurements. The full-field displacement measurements can then be used to develop three-dimensional strain fields. DIC software can be utilized for both two-dimensional and three-dimensional analysis, depending on the number of cameras used when testing. DIC
684
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689 Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
3
uses a series of images taken during loading and compares the images to obtain relative displacement and strain. DIC has shown potential for detecting and characterizing fatigue cracks, but testing has been limited to simplified test setups. DIC has been used in material testing to measure deformation and strain, and DIC can serve as an alternative to traditional sensing technology, such as strain gauges. Using DIC to evaluate cracking has been applied in the calculations of stress intensity factors (Zhang and He 2012) and to detect cracks in a concrete structure (Küntz et al. 2006). While research on vision-based crack detection methods have been conducted, testing has primarily occurred under idealized conditions looking only at in-plane fatigue loading or at cracks in non-metallic materials. Very few studies have been conducted evaluating vision-based crack detection methods on out-of-plane loading with the complex geometries found on steel highway bridges. Researchers have theorized that out-of-plane test setups have not been evaluated due to the inherent complexity and sophistication required (Sutton et al. 2007). Obtaining accurate results from DIC is dependent on the preparation of the specimen, the camera setup, calibration, and image collection. The preparation of the specimen involves applying a high-contrast random speckle pattern to the material surface, creating points of reference for image comparison. The necessary camera setup is dependent on the complexity of the specimen being tested. In-plane fatigue specimens experiencing no out-of-plane deformations require only two-dimensional analyses, necessitating the use of only a single camera. When testing for out-of-plane displacement, however, two cameras are required to calculate a three-dimensional displacement field. 2. DIC Crack Characterization Methodology 2.1. Initial Testing and Methodology Development To move towards automated fatigue crack inspection, crack characterization must be quantified. Initial single-camera DIC testing was performed on a compact (C(T)) specimen subjected to in-plane loading on a servo-hydraulic testing machine. The C(T) specimen was 6.35 mm (0.25 in.) thick with a width of 127 mm (5.0 in.). The large specimen was capable of accommodating extensive crack growth, allowing for testing at a variety of crack lengths. As bridge loading is highly variable, multiple load cases were defined and applied based on stress intensity ranges of 11, 22, 33, 44, and 55 MPa√m (10, 20, 30, 40, and 50 ksi√in). Crack tip plasticity was limited during testing by applying the lowest stress intensity first and increasing to the highest. DIC data was recorded for each load case at crack lengths ranging from 12.7 to 50.8 mm (0.5 to 2.0 in.) in 12.7 mm (0.5 in.) increments. Although the general crack location could be identified in the visualized DIC results, the goal of automation prompted the development of algorithms to determine crack length from the DIC data. The twenty in-plane data sets were used to develop a method for this quantification. 2.2. Crack Characterization Methodology Although edge detection algorithms often produce false positive results when attempting to identify fatigue cracks on steel bridges, they can work well for images analyzed with DIC. Images of DIC-produced surface displacement contours were analyzed using commercially-available edge detection algorithms to initially identify the crack path. The coordinates of the crack path were extended linearly beyond the end of the crack tip as identified by edge detection. This linear extension of the crack path was necessary due to the inability of the edge detection process to accurately identify the crack tip. After identification of the crack path, differential surface displacements across the crack and perpendicular to the idealized crack path were calculated. This is schematically represented in Fig. 1a. Relative displacement at each point along the crack path, Δi, was then divided by the maximum relative displacement, Δmax. A convergence value was defined by subtracting this ratio of relative displacements from 100%, calculated as in Eq. (1) and plotted in Fig. 1b. Theoretically, convergence values should approach 100% near the crack tip as crack openings go to zero. However, known crack tip locations on the C(T) specimens typically corresponded with convergence values between 90% and 95%. At higher load cases convergence values typically decreased, indicating relative displacement continued beyond the actual crack tip location. This is likely due to larger amounts of crack tip plasticity occurring at higher applied stress intensities. 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 = 100% −
∆𝑖𝑖
∆𝑚𝑚𝑚𝑚𝑚𝑚
(1)
2.3. Application to Distortion-Induced Fatigue Loading The crack characterization methodology based on in-plane C(T) specimens was applied to data obtained from a half-scale girderto-cross-frame subassembly. This bridge component specimen allowed for loading with a realistic distortion-induced fatigue
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689
Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
4
685
mechanism. Loading out-of-plane for 21,000 cycles produced a vertical fatigue crack at the stiffener to web weld. The 44.5 mm (1.75 in.) crack was composed of three linear sections, with two vertical segments connected by small diagonal segment. Similar to the inplane C(T) tests, multiple load levels were defined based on realistic fatigue loading. A stereo-camera setup was used to obtain threedimensional displacement data, and convergence was calculated based on displacement in the direction perpendicular to the girder web. Using convergence values of 90% and 95%, crack lengths were calculated using the DIC crack characterization methodology. For all load cases, the average total crack length corresponding to 90% convergence was 40.6 mm (1.59 in.), while 95% convergence predicted an average crack length of 45.0 mm (1.77 in.). These predictions under-predict and over-predict the crack length by 9% and 1%, respectively, indicating the 90% to 95% convergence range is appropriate for distortion-induced fatigue cracks subjected to outof-plane loading. However, it should be noted that the although more geometrically-complex than the linear crack on the in-plane C(T) specimen, the out-of-plane fatigue crack was fairly simple for a distortion-induced fatigue crack. Often cracks on steel highway bridges can be extremely complex, with multiple cracks located in the same region and bifurcation creating two or more crack tips on individual cracks.
Fig. 1. a) Relative displacement along length of crack path and b) Convergence along length of crack path
3. Objective The objective of the study discussed herein was to evaluate the ability of a DIC-based fatigue crack characterization methodology to quantify a complex, bifurcated out-of-plane crack subjected to distortion-induced fatigue. A branching crack was tested under outof-plane loading, and DIC data was collected at various load levels. The efficacy of the DIC crack characterization methodology was examined for two bifurcated crack tips emanating from a single crack. 4. Experimental Approach 4.1. Girder Test Setup and Loading A girder-to-cross-frame subassembly, shown in Fig. 2a, was used for distortion-induced fatigue testing. The half-scale girder, fabricated from A36 steel, had a length of 2845 mm (112 in.), a depth of 917 mm (36.1 in.), and a web thickness of 10 mm (0.375 in.). The bottom flange of the girder subassembly was connected to the reaction floor of the laboratory, restraining it from out-of-plane motion. This restraint approximates how the axial stiffness of a concrete deck restrains out-of-plane motion of a bridge flange. At the girder mid-span a cross-frame was installed and attached to a connection plate. The connection plate was welded to the girder web only, creating the web gap region found on older structures. Load was vertically applied to the cross-frame through the use of a hydraulic actuator, the attachment of which can be seen in the top left corner of Fig. 2a. Vertical displacements at the end of the crossframe produce realistic distortion-induced fatigue loading in the web gap region, resulting in load applied vertically and out-of-plane with respect to the girder web.
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689
686
Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
5
4.2. Complex Crack Geometry The previously existing fatigue crack was loaded cyclically at a range from 2.2 to 25.5 kN (0.5 to 5.75 kips) for 73,000 additional cycles, causing crack propagation and bifurcation. This is highlighted in Fig. 2b, where black lines have been drawn over the fatigue crack. One branch of the bifurcated crack, segment B-C, propagated vertically up the stiffener-to-web weld, while segment B-D grew horizontally into the girder web. For the purposes of crack characterization, these will be presented as two separate cracks with a shared initiation site: the vertical branch (A-B-C) and the horizontal branch (A-B-D). Inspection of the web gap revealed the total length of the vertical branch was 90.3 mm (3.55 in.) and the total length of the horizontal branch was 75.1 mm (2.95 in.).
Fig. 2. a) Distortion-induced fatigue subassembly and b) bifurcated crack in the web gap region
4.3. Loading Protocol Finite element analysis was used to determine appropriate levels of load for the distortion-induced fatigue subassembly. An analytical model of a steel girder bridge was evaluated with the AASHTO fatigue truck. Differential girder displacements determined in the model were scaled for the test specimen resulting in a target deflection of 1.25 mm (0.05 in.). This deflection corresponded to an applied actuator load of 6.6 kN (1.5 kips), and seven load cases were defined both above and below this threshold. Each load case, presented in Table 1, had a minimum load of 0.89 kN (0.2 kips) simulating dead load. Table 1. Loading Protocol for Distortion-Induced Fatigue Testing Load Case
Load Range, kN (kips)
LC1
0.89-2.2 (0.2-0.5)
LC2
0.89-4.4 (0.2-1.0)
LC3
0.89-6.7 (0.2-1.5)
LC4
0.89-8.9 (0.2-2.0)
LC5
0.89-11.1 (0.2-2.5)
LC6
0.89-13.3 (0.2-3.0)
LC7
0.89-15.6 (0.2-3.5)
5. Results 5.1. DIC Results DIC data was collected for the bifurcated out-of-plane crack for each load case. Typical DIC results, visualized for both strain and displacement, are shown in Fig. 3. The majority of the crack is clearly visible in both the strain and displacement images, although the
6
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689
Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
687
vertical branch is difficult to see. Due to the complex geometry of the crack and multi-axial fatigue loading, displacements were examined in all three principal directions, and resultant differential displacements were calculated for use with the crack characterization methodology. As described above, differential displacements were taken orthogonal to the crack path, and convergence was calculated along the length of the crack path.
Fig. 3. Typical DIC results of the distortion-induced fatigue specimen visualized in terms of a) strain and b) displacement
5.2. Horizontal Branch Characterization Results Horizontal branch relative displacement along the crack path is presented in Fig. 4a for all load cases. Calculated convergence values are shown in Fig. 4b. In the figures the actual crack length of 75.1 mm (2.95 in.) is represented by a vertical dotted line. It should be noted that displacements caused by load case 1 were extremely small, causing extreme variation in convergence values. Due to this, load case 1 convergence values are not presented. Using 90% and 95% convergence, crack lengths were predicted and compared with the optically measured length. The predicted crack lengths and error, expressed as a percentage, are presented in Table 2. Using a value of 90% convergence under-predicted crack lengths by an average of 27%. The 95% convergence performed much better for the horizontal branch, under-predicting crack length by an average of only 10%.
Fig. 4. Horizontal branch a) Relative displacement and b) Convergence of relative displacement
5.3. Vertical Branch Characterization Results Relative displacement along the vertical branch crack path for all load cases is shown in Fig. 5a, and convergence values are presented in Fig. 5b. The optically measured crack length of 90.3 mm (3.55 in.) is indicated by the vertical dotted lines. Again, load case 1 convergence values are not presented as small displacements cause extreme variability. It can be seen that for most load cases relative displacement values approach zero well before the tip of the crack causing convergence values to greatly under-predict crack length.
688
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689 Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
7
Table 2. Horizontal branch length characterization 90% Convergence Load Case
Crack Length, mm (in.)
Error, %
LC1
N/A
LC2
50.2 (1.98)
LC3
50.2 (1.98)
95% Convergence Crack Length, mm (in.)
Error, %
N/A
N/A
N/A
-33
65.5 (2.58)
-13
-33
64.7 (2.55)
-14
LC4
51.2 (2.02)
-32
64.3 (2.53)
-14
LC5
51.8 (2.04)
-31
64.4 (2.54)
-14
LC6
60.9 (2.40)
-19
71.9 (2.83)
-4
LC7
63.2 (2.49)
-16
73.8 (2.90)
-2
Average
54.6 (2.15)
-27
67.4 (2.65)
-10
Fig. 5. Vertical branch a) Relative displacement and b) Convergence of relative displacement
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. Table 3. Vertical branch length characterization 90% Convergence Load Case
Crack Length, mm (in.)
Error, %
LC1
N/A
LC2
51.0 (2.01)
LC3
50.2 (1.98)
95% Convergence Crack Length, mm (in.)
Error, %
N/A
N/A
N/A
-44
56.3 (2.22)
-38
-44
54.3 (2.14)
-40
LC4
51.3 (2.02)
-43
54.7 (2.15)
-39
LC5
51.9 (2.04)
-43
55.0 (2.17)
-39
LC6
53.7 (2.11)
-40
57.0 (2.24)
-37
LC7
54.3 (2.14)
-40
57.5 (2.26)
-36
Average
53.0 (2.05)
-42
55.8 (2.20)
-38
8
Hayder Al-Salih et al. / Procedia Structural Integrity 17 (2019) 682–689
Al-Salih/ Structural Integrity Procedia 00 (2019) 000–000
689
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. 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 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), 10011007. 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.