Abstract

Deterioration of road infrastructure arises from aging and various other factors. Consequently, inspection and maintenance have been a serious worldwide problem. In the United States, degradation of concrete bridge decks is a widespread problem among several bridge components. In order to prevent the impending degradation of bridges, periodic inspection and proper maintenance are indispensable. However, the transportation system faces unprecedented challenges because the number of aging bridges is increasing under limited resources, both in terms of budget and personnel. Therefore, innovative technologies and processes that enable bridge owners to inspect and evaluate bridge conditions more effectively and efficiently with less human and monetary resources are desired. Traditionally, qualified engineers and inspectors implemented hammer sounding and/or chain drag, and visual inspection for concrete bridge deck evaluations, but these methods require substantial field labor, experience, and lane closures for bridge deck inspections. Under these circumstances, Non-Destructive Evaluation (NDE) techniques such as computer vision-based crack detection, impact echo (IE), ground-penetrating radar (GPR) and infrared thermography (IRT) have been developed to inspect and monitor aging and deteriorating structures rapidly and effectively. However, no single method can detect all kinds of defects in concrete structures as well as the traditional inspection combination of visual and sounding inspections; hence, there is still no international standard NDE methods for concrete bridges, although significant progress has been made up to the present. This research presents the potential to reduce a burden of bridge inspections, especially for bridge decks, in place of traditional chain drag and hammer sounding methods by IRT with the combination of computer vision-based technology. However, there were still several challenges and uncertainties in using IRT for bridge inspections. This study revealed those challenges and uncertainties, and explored those solutions, proper methods and ideal conditions for applying IRT in order to enhance the usability, reliability and accuracy of IRT for concrete bridge inspections. Throughout the study, detailed investigations of IRT are presented. Firstly, three different types of infrared (IR) cameras were compared under active IRT conditions in the laboratory to examine the effect of photography angle on IRT along with the specifications of cameras. The results showed that when IR images are taken from a certain angle, each camera shows different temperature readings. However, since each IR camera can capture temperature differences between sound and delaminated areas, they have a potential to detect delaminated areas under a given condition in spite of camera specifications even when they are utilized from a certain angle. Furthermore, a more objective data analysis method than just comparing IR images was explored to assess IR data. Secondly, coupled structural mechanics and heat transfer models of concrete blocks with artificial delaminations used for a field test were developed and analyzed to explore sensitive parameters for effective utilization of IRT. After these finite element (FE) models were validated, critical parameters and factors of delamination detectability such as the size of delamination (area, thickness and volume), ambient temperature and sun loading condition (different season), and the depth of delamination from the surface were explored. This study presents that the area of delamination is much more influential in the detectability of IRT than thickness and volume. It is also found that there is no significant difference depending on the season when IRT is employed. Then, FE model simulations were used to obtain the temperature differences between sound and delaminated areas in order to process IR data. By using this method, delaminated areas of concrete slabs could be detected more objectively than by judging the color contrast of IR images. However, it was also found that the boundary condition affects the accuracy of this method, and the effect varies depending on the data collection time. Even though there are some limitations, integrated use of FE model simulation with IRT showed that the combination can be reduce other pre-tests on bridges, reduce the need to have access to the bridge and also can help automate the IRT data analysis process for concrete bridge deck inspections. After that, the favorable time windows for concrete bridge deck inspections by IRT were explored through field experiment and FE model simulations. Based on the numerical simulations and experimental IRT results, higher temperature differences in the day were observed from both results around noontime and nighttime, although IRT is affected by sun loading during the daytime heating cycle resulting in possible misdetections. Furthermore, the numerical simulations show that the maximum effect occurs at night during the nighttime cooling cycle, and the temperature difference decreases gradually from that time to a few hours after sunrise of the next day. Thus, it can be concluded that the nighttime application of IRT is the most suitable time window for bridge decks. Furthermore, three IR cameras with different specifications were compared to explore several factors affecting the utilization of IRT in regards to subsurface damage detection in concrete structures, specifically when the IRT is utilized for high-speed bridge deck inspections at normal driving speeds under field laboratory conditions. The results show that IRT can detect up to 2.54 cm delamination from the concrete surface at any time period. This study revealed two important factors of camera specifications for high-speed inspection by IRT as shorter integration time and higher pixel resolution. Finally, a real bridge was scanned by three different types of IR cameras and the results were compared with other NDE technologies that were implemented by other researchers on the same bridge. When compared at fully documented locations with 8 concrete cores, a high-end IR camera with cooled detector distinguished sound and delaminated areas accurately. Furthermore, indicated location and shape of delaminations by three IR cameras were compared to other NDE methods from past research, and the result revealed that the cooled camera showed almost identical shapes to other NDE methods including chain drag. It should be noted that the data were collected at normal driving speed without any lane closures, making it a more practical and faster method than other NDE technologies. It was also presented that the factor most likely to affect high-speed application is integration time of IR camera as well as the conclusion of the field laboratory test. The notable contribution of this study for the improvement of IRT is that this study revealed the preferable conditions for IRT, specifically for high-speed scanning of concrete bridge decks. This study shows that IRT implementation under normal driving speeds has high potential to evaluate concrete bridge decks accurately without any lane closures much more quickly than other NDE methods, if a cooled camera equipped with higher pixel resolution is used during nighttime. Despite some limitations of IRT, the data collection speed is a great advantage for periodic bridge inspections compared to other NDE methods. Moreover, there is a high possibility to reduce inspection time, labor and budget drastically if high-speed bridge deck scanning by the combination of IRT and computer vision-based technology becomes a standard bridge deck inspection method. Therefore, the author recommends combined application of the high-speed scanning combination and other NDE methods to optimize bridge deck inspections.

Notes

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Graduation Date

2016

Semester

Summer

Advisor

Catbas, Necati

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental and Construction Engineering

Degree Program

Civil Engineering

Format

application/pdf

Identifier

CFE0006323

URL

http://purl.fcla.edu/fcla/etd/CFE0006323

Language

English

Release Date

August 2016

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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