A data processing methodology for infrared thermography images of concrete bridges

doi:10.1016/j.compstruc.2017.05.011

Computers & Structures

Volume 190, 1 October 2017, Pages 205-218

https://doi.org/10.1016/j.compstruc.2017.05.011 Get rights and content

Highlights

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This study aims to detect invisible delaminations objectively.
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A more objective infrared thermography data processing method was developed.
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IRT data was processed mathematically.
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Finite element model simulation was used to obtain thresholds for data processing.
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Integrated use of FE modeling can improve the efficiency of IRT data analysis.

Abstract

This study presents a methodology to improve the usability and efficiency of infrared thermography (IRT) for subsurface damage detection in concrete structures. A practical and more objective approach to obtain a threshold for IRT data processing was developed by incorporating finite element (FE) model simulations. Regarding the temperature thresholds of sound and delaminated areas, the temperature of the sound part was obtained from the IR image, and the temperature of the delaminated area was defined by FE model simulation. With this methodology, delaminated areas of concrete slabs at 1.27 cm and 2.54 cm depths could be detected more objectively than by visually judging the color contrast of IR images. However, it was also found that the boundary condition affects the accuracy of the method, and the effect varies depending on the data collection time. On the other hand, it can be assumed that the influential area of the boundary condition is much smaller than the area of a bridge deck in real structures; thus, it might be ignorable on real concrete bridge decks. Even though there are some limitations, this methodology performed successfully paving the way towards automated IRT data analysis for concrete bridge deck inspections.

Introduction

Degradation of reinforced concrete bridge decks is a widespread problem in the United States. Concrete bridge decks deteriorate faster than other bridge components due to direct exposure to traffic. Moreover, the Federal Highway Administration’s (FHWA’s) Long Term Bridge Performance (LTBP) Program identified that the most important bridge performance issue was the performance of concrete bridge decks [1]. Cracking, spalling, and delaminations were common defects requiring maintenance and rehabilitation. Most State Departments of Transportation (DOTs) noted that repair, rehabilitation and replacement of concrete bridge decks account for 50–80% of their budgets for maintenance of bridges. These State DOTs are seeking methods to detect defects and quantify the extent and severity of bridge decks early, accurately, and rapidly with minimal traffic impact, ideally, without lane closure for inspections [1], [2]. Furthermore, FHWA [3] requires biennial inspection of every highway bridge in the National Highway System (NHS). Therefore, Non-Destructive Evaluation (NDE) methods are desired to inspect bridge decks efficiently and effectively. Consequently, NDE techniques are being developed to examine and monitor deteriorating structures rapidly and effectively [4]. Most NDE methods aim to achieve the highest quality of visual imaging of the relevant internal features of structures [5], [6]. However, there is still no international standard NDE methods for concrete bridges, although significant progress has been made towards an internationally common approach to NDE inspection [6]. As Gucunski et al. [1] reports, one of the limitations of NDE methods for bridge inspection is the speed of data collection.

Infrared thermography (IRT) is one of the NDE methods and has been developed to detect invisible deteriorations including delaminations and voids in concrete structures with reasonable accuracy; it also helps avoid the time and expense of gaining immediate access to the concrete surface to conduct traditional sounding tests. IRT is a suitable approach for inspection of civil infrastructures since it is a non-contact method and infrared (IR) images can instantly portray a wide range of concrete structures at one time [7], [8], [9], [10]. Therefore, IRT can be the fastest and easiest NDE methodology regarding data collection among the other NDE methods, even though there are some limitations and uncertainties in using IRT for bridge inspections such as data collection time, size of delamination, camera specifications, data collection speed and data interpretation.

The objective of this study is to develop a methodology of how to objectively interpret and detect delaminations from IR images since it becomes very subjective judging whether or not the color contrast of the image is a damage indication. As Washer et al. [11] argued, if the temperature span for IR images is setup too high or too low, it appears in the IR image as if there is no anomaly even though there are some defects. Therefore, they recommended adjusting the temperature span of IR images continuously throughout inspections. However, it might require a lot of work during or after the bridge inspection. This study consequently explores a more objective method than just comparing IR images to assess IRT data. Kee et al. [12] and Oh et al. [13] processed IRT data mathematically by using MATLAB with certain thresholds defined by iterative trials until the operator obtained the clearest contrast between the sound and delaminated regions within each IR image. However, this procedure is very subjective since the operator has to determine whether the contrast depicts damaged or sound regions, even though these regions are usually unknown areas in terms of existing defects. Processing IRT data mathematically is more objective than judging the data from the color contrast since it does not require a temperature span setting as mentioned above. However, how to determine the thresholds, in other words, how to obtain the information of temperature difference between sound and delaminated areas becomes a challenge for their methodology to process mathematically without subjective trials.

Hiasa et al. [14] proposed a data processing method; however, it was also discussed how obtaining the information of temperature difference between sound and delaminated areas becomes a challenge. In this study, an easier and more objective method to obtain the threshold for IRT data processing is explored by incorporating finite element (FE) model simulations. The use of FE model simulation has been increasing recently to simulate the temperature distribution of the object’s surface [7], [15], [16], [17], [18], [19]. In the past study, Hiasa et al. [20] employed the FE model simulation to explore sensitive parameters for effective utilization of IRT without a large number of experiments, which require extremely time-consuming work. This study uses the FE model developed in the past study. The aim of this study is twofold: to obtain information regarding the temperature difference between sound and delaminated areas from FE modeling, and to process IRT data in order to objectively detect invisible subsurface delaminations. In this study, the IRT data obtained from a field laboratory experiment under passive IRT conditions [21] are used to develop a more objective data processing method with the combination of FE model simulation.

Section snippets

Current practice and future potential of infrared thermography for bridge inspection

Through literature reviews, several factors that might affect the performance of IRT can be excerpted such as data collection time, size of delamination, data collection speed and IR camera specifications. In terms of data collection time, Washer et al. [22], [23] recommended daytime measurements of 5–9 h after sunrise to detect subsurface delaminations for the solar loading part. On the other hand, Gucunski et al. [24] mentioned that a thermal image recorded 40 min after sunrise yielded a much

Summary of past studies

The present study aims to develop an IRT data processing methodology to objectively detect delaminations using FE model simulation. For that purpose, this study uses past experimental data and FE models developed in the previous study. This section summarizes these past studies.

Application of FE model simulation to define thresholds for data processing

The present study utilizes FE model simulations to obtain thresholds for IRT data processing more easily and objectively than the method Kee et al. [12] and Oh et al. [13] conducted with iterative trials. In this study, the same FE model and conditions explained in Section 3 were used to obtain temperature differences between sound and delaminated areas. Fig. 5 shows some simulation results at 9 AM, 3 PM, 8 PM and 12 AM (midnight). Since the surface temperatures are not homogeneous due to

Results of the IRT data processing methodology

Based on the temperature spans defined in Table 4, each IRT data was specified into the temperature range. In this study, every image was smoothened by Gaussian filter as described in Eq. (5) to reduce noise [37]. $F (x, y) = \frac{1}{\sqrt{2 π} σ^{2}} \exp (- \frac{x^{2} + y^{2}}{2 σ^{2}})$

In this study, 9 × 9 matrix with σ = 1.5 was used for the filter. Fig. 10 shows IR raw images and Fig. 11 displays those specified and filtered images. Furthermore, these images were converted into binary images as described in Eq. (3) to reduce noise, and Fig. 12

Discussion

As can be seen in Fig. 11, there are a lot of gray level indications, making it is ambiguous to judge whether those are sound or delaminated areas. A different evaluation might be obtained depending on the person who judges those images. On the other hand, processed images converted into binary images indicate only two colors, white or black as shown in Fig. 12; thus, the judgment becomes objective regardless of the person who evaluates processed images.

In terms of the damage detection

Advantage of FE model simulation

Even though there are some limitations regarding boundary conditions in this method, combined use of FE model simulation with IRT showed that the combination can improve efficiency of IRT for concrete bridge inspections to detect subsurface delaminations. Fig. 13 displays scaled original IRT data image at 8 PM for 1.27 cm deep delamination (left) and specified range image of the same condition with data smoothing (right). Obviously, the specified range image shows much clearer indications of

Conclusions

In this study, a methodology that combines numerical modeling and IRT data is presented to improve the usability and efficiency of data analysis, possibly leading to automated analysis and evaluation. Kee et al. [12] and Oh et al. [13] processed IRT data mathematically by using MATLAB with certain thresholds defined by iterative trials until the operator obtained the clearest contrast between the sound and delaminated regions within each IR image. Processing IRT data mathematically is more

Acknowledgments

This work was supported mainly by West Nippon Expressway Company Limited (NEXCO-West) and also by the Scientific and Technological Research Council of Turkey (TÜBİTAK) and National Science Foundation (NSF CMMI #1463493). The authors would like to express their sincere gratitude to Dr. Koji Mitani, Mr. Masato Matsumoto, Mr. Shinji Nagayasu and Mr. Kyle Ruske of NEXCO-West USA for feedback and support throughout the studies presented in this manuscript. Without their contributions, this project

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¹: Formerly, Visiting Scholar at the University of Central Florida, 12800 Pegasus Drive, Suite 211, Orlando, FL, USA.

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