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Abstract

This paper presents the capabilities and limitations of high-definition (HD) imaging and infrared thermography (IRT) using unmanned aerial vehicles (UAVs) for bridge inspections. Regarding HD imaging, this study shows the potential to detect the required minimum, 0.1 mm, width of cracks using simulated cracks at a distance of 1-3 m from the bridge. As for IRT application with UAVs, the effect of vibration caused by the drone was investigated. This paper concludes that the hovering flight of drones was extremely stable and IRT with UAVs can provide reliable data for bridge inspections. If IRT is used without hovering flight, the effect of flying speed must be investigated since it might also affect the IRT results. This paper shows great potential of image-based technologies with UAVs for bridge inspections without any traffic disturbance as a complimentary approach to current practices.

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... More specifically, 20 of the 53 studies explored the possibility of automating bridge inspection [5,7,10,18,[26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41], and three articles investigated the application of automation assisted technologies for highway inspection [42][43][44], [64]. These statistics reflect the importance of the timely maintenance and repair of bridge structures and highways, for they are among the most critical infrastructures supporting our communities. ...
... IR thermal imaging techniques were used in power line inspection and management to detect excessive heat buildup [52]. Further, this technology was also used in bridge inspection application [39] and general infrastructure application [25]. Additionally, long wavelength IR technology is used to detect and classify humidity [36]. ...
... It merely displays the images collected [38]. Similarly, the bridge inspection drone explained in Hiasa et al. [39] is also an example of level 2 system, because the drone is manually controlled and the data is manually analyzed. For Level 3, the automation selects one alternative and presents it to the inspector. ...
Article
Routine inspection and maintenance are critical for the proper functioning of civil infrastructures such as bridges, pavements and underground structures. Civil infrastructures are being inspected less frequently because of the high cost and long duration of current inspection procedures. Furthermore, conventional inspection procedures often interrupt the routine functioning of the infrastructure, are inspector-dependent and expose the inspectors to complex and unsafe working environments. Visual inspection technologies play a crucial role in the inspection and maintenance of civil infrastructures. Automation-assisted technologies such as drones and underwater vehicles equipped with multiple imaging and sensing systems have been developed to address some of these issues with the conventional visual inspection processes. This paper reviews peer-reviewed research publications investigating automated visual inspection technologies following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, 53 publications satisfying a set of inclusion criteria were reviewed, its results highlighting the application domain, the level of autonomy of the automated systems, the sensor technologies used for the inspection process and navigation, the navigation and control technologies and the algorithms used. The review of the articles revealed that the data collected by automation is used to augment the qualitative assessment. Several types of algorithms such as target detection and image enhancing have been developed to reduce the inspector bias in these automated technologies. Path planning algorithms reduce the workload on the inspector by automating the navigation and control tasks. Remotely operated systems reduce the risk to the inspectors by minimizing their exposure to the inspection environment. However, only a limited number of studies investigated the human factors aspects of the automation-assisted inspection process. It is important to understand the cognitive, physical, and temporal demands these technologies place on inspectors to improve the design of systems assisting in the inspection process. Moreover, factors such as automation bias, trust in the system and communication between the automation and the operator need to be investigated. Furthermore, it is important to incorporate appropriate decision aids that support adequate situation awareness in the interface design. Based on these findings this review proposes directions for future research. This review concludes by highlighting the need for human-centered research to develop better solutions for infrastructure inspection problems.
... Karaaslan (2018) designed a decision support framework to retrieve information from novel NDE techniques including vision-based technologies (e.g., infrared thermography, other imagery data) and perform network-level decision analysis using both NBI's inputs and automatically retrieved inspection data from NDE [15]. The framework implemented a condition prediction methodology introduced by Hiasa et al. (2018) [16]. The methodology used the infrared thermography (IRT) data from the deck surface collected over a period of 10 years to provide critical information on how local delamination can potentially impact the integrity of the overall structure, as described in Figure 2. It will be possible to conduct time history prediction on the data to determine the optimal timeline of the necessary maintenance/repair actions. ...
... Example utilization of NDE for bridge management[16]. ...
Article
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Developing a bridge management strategy at the network level with efficient use of capital is very important for optimal infrastructure remediation. This paper introduces a novel decision support system that considers many aspects of bridge management and successfully implements the investigated methodology in a web-based platform. The proposed decision support system uses advanced prediction models, decision trees, and incremental machine learning algorithms to generate an optimal decision strategy. The system aims to achieve adaptive and flexible decision making while entailing powerful utilization of nondestructive evaluation (NDE) methods. The NDE data integration and visualization allow automatic retrieval of inspection results and overlaying the defects on a 3D bridge model. Furthermore, a deep learning-based damage growth prediction model estimates the future condition of the bridge elements and utilizes this information in the decision-making process. The decision ranking takes into account a wide range of factors including structural safety, serviceability, rehabilitation cost, life cycle cost, and societal and political factors to generate optimal maintenance strategies with multiple decision alternatives. This study aims to bring a complementary solution to currently in-use systems with the utilization of advanced machine-learning models and NDE data integration while still equipped with main bridge management functions of bridge management systems and capable of transferring data to other systems.
... More recent loadtest implementations to concrete bridge can be found in the study of Omar and Nehdi (2018). On the other hand, some researchers also recently demonstrated that more effective bridge condition assessments could be done through other technologies (computer vision, image, thermal camera, etc.) (Agdas et al., 2016;Zaurin et al., 2016;Hiasa et al., 2018;Dong and Catbas, 2019). In these studies, these technologies were determined to be an effective complementary tool. ...
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In this article, dynamic and static load tests of a concrete highway bridge, which is a deteriorated and repaired, are presented depending on displacement and strain data for engineering decision making about the operation of a critical bridge. Static load test was carried out to determine the live load distribution factor (DF) and load-rating factor (RF) as well as serviceability by means of deflection limits. Modal characteristics in terms of structural frequencies and mode shapes and impact factor (IM) were identified from the dynamic load test for different truck-load and speed cases, and finite element (FE) model. The DF and rating factor (RF) were also compared with those calculated according to AASHTO standard and FE model. The results showed that the DF calculated by American Association of State Highway and Transportation Officials (AASHTO) standard gave more conservative results when compared with the experimental and FEM approaches. Similarly, the load-rating factor (RF) calculated by AASHTO standard yielded to more conservative results comparing with the experimental FEM approaches using practical DFs. Maximum deflections in static cases and dynamic cases were found to be within the limit calculated by (L/800) given in the AASHTO code. Impact factors among all the cases were obtained much smaller than the one recommended by AASHTO standard (33%). The modal properties were obtained to track changes in dynamic behavior due to stiffness and boundary effects as well as for finite element model calibration. The calibrated FE model of the bridge also indicated that the load carrying capacity of the bridge is adequate after repair. Finally, the results from the current study reveal that use of experimental data can be utilized to obtain load rating with minimum interruption to bridge operations through computer vision technology and methods.
... The American Association of State Highway and Transportation Officials March 2016 survey found that 17 state Departments of Transportation had studied or used drones, whereas 16 states were either exploring UAV usage, assisting in the development of drone policies, or supporting drone research [27][28][29]. UAVs themselves cannot perform inspections independently, but can be used as a tool for bridge inspectors to view and assess bridge element conditions following the National Bridge Inspection Standard [30][31][32]. We believe that to achieve profits resulting from the automation of the image analysis process, it is also essential to automate the process of data acquisition, which is the drone flight route. ...
Article
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The monitoring of a structural condition of steel bridges is an important issue. Good condition of infrastructure facilities ensures the safety and economic well-being of society. At the same time, due to the continuous development, rising wealth of the society and socio-economic integration of countries, the number of infrastructural objects is growing. Therefore, there is a need to introduce an easy-to-use and relatively low-cost method of bridge diagnostics. We can achieve these benefits by the use of Unmanned Aerial Vehicle-Based Remote Sensing and Digital Image Processing. In our study, we present a state-of-the-art framework for Structural Health Monitoring of steel bridges that involves literature review on steel bridges health monitoring, drone route planning, image acquisition, identification of visual markers that may indicate a poor condition of the structure and determining the scope of applicability. The presented framework of image processing procedure is suitable for diagnostics of steel truss riveted bridges. In our considerations, we used photographic documentation of the Fitzpatrick Bridge located in Tallassee, Alabama, USA.
... Seo, Duque and Wacker [9] designed a study where they used glued laminated (glulam) girder with a composite concrete deck bridge in South Dakota and for bridge inspection they employed (DJI) Phantom 4 drone. In [10] Hiasa at al. presented the capabilities and limitations of high-definition (HD) imaging and infrared thermography (IRT) using unmanned aerial vehicles (UAVs) for bridge inspections. Also, they investigated the effect of vibration caused by the drone and concluded that the hovering flight of drones was extremely stable and that IRT with UAVs can provide reliable data for bridge inspections. ...
Conference Paper
This paper presents a case of filming a bridge in Novi Sad during its construction for the purpose of inspection of construction works carried out by the Spanish - Italian consortium AZVI. Twice a month the photographs and high-resolution films shot by a drone were sent to AZVI. In this way, the contractors had up-to-date information on the progress of the works without the need for a permanent presence of the company's engineers. Drone shooting has enabled high-resolution images and movies which allow quality remote inspection, accompanied, naturally, with occasional arrivals at the site.
... The precise control below the ceiling is not discussed in these implementations. Furthermore, recent applications also explore the aerial photography for the crack [21][22][23] and delamination/corrosion [24,25] inspection as well as thermal images [26], these also require a stationary keeping in the air which can be troublesome when a proper control approach is not utilized. ...
Article
Today's modern cities depend on basic services such as water and sewer systems including tunnels, which are supported by a pervasive infrastructure. The structural condition of these places is naturally prone to deterioration. Therefore, regular inspections are required for early detection of damage and material failure. It is envisaged that robotic systems may provide an up-to-date solution for this inspection task. Considering the challenging conditions in the surrounding environments, unmanned aerial vehicles (UAVs) may be a potential candidate to meet the need for both purely visual (contactless) and subsurface (contact-based) inspection functionalities. When the interaction with an uncooperative target is required, the UAV needs to be aware of the forces arising during the contact phase. This stage brings additional challenges including a variable center of gravity as well as the moment of inertias due to the shift in the attached parts, sliding on the target because of the surface characteristics and reaching desired interaction levels out of the equilibrium point without crashing the dedicated sensors while creating counter forces (reactions) in a desired manner. At the same time, ultrasonic inspection requires uninterrupted contact in certain force ranges. In order to address these factors in a compact and feasible manner, an optimization algorithm consisting of nonlinear moving horizon estimation (NMHE), which is a part of nonlinear model predictive control (NMPC) is proposed. In this algorithm, the baseline model of the UAV is augmented by the external forces where uncertainties, modeling mismatches and disturbances are lumped. Therefore, the NMHE has estimated these values in an online manner. In a simultaneous fashion, the identified external forces are fed into the NMPC to physically interact with the ceiling during the contact phase. The experimental inspection data is collected while autonomously staying in predefined constraint limits. It is observed that the complete inspection data is obtained and streamed via Bluetooth when the force on the ultrasonic tool reached 5.5 N level. The external force update coming from the estimation allows the proposed approach to reach these interaction levels.
Article
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Reinforced concrete bridge decks deteriorate over time primarily due to increasing traffic loads, severe environmental conditions, especially in North America, and deferred maintenance. Condition monitoring of those structures in a timely manner is of a great importance for making informed decisions regarding maintenance, rehabilitation and replacement strategies to preserve their value, maintain their levels of service and safeguard against catastrophic failures. The objective of this paper is to introduce a comprehensive review of the current state of the art on condition monitoring of reinforced concrete bridge decks. Deterioration progression of bridge decks including different types of defects (i.e., cracks, corrosion, delamination, spalling, honeycomb and voids), their associated causes and effects are introduced. Commonly used non-invasive and non-destructive evaluation methods, including digital imaging, ground-penetrating radar, infrared thermography, half-cell potential, electrical resistivity, chain drag & hammer sounding, ultrasonic surface wave, ultrasonic pulse echo and impact echo, are presented with their capabilities and limitations. A bibliometric analysis was conducted to investigate the current trend in condition monitoring. The review also examines the frequency of methods used in condition monitoring and provide a classification of recent studies according to study type (e.g., field or laboratory experiment), joint use (i.e., whether study applied method as a stand-alone or hybridized it with another method) and performance (i.e., whether study investigated performance indicators of the applied methods or not). The key stages in monitoring condition states of bridge decks considering risks are discussed. Current practices, challenges and future perspectives are also highlighted.
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Routine visual inspection is essential to maintain adequate safety and serviceability of civil infrastructures. Computer vision and machine learning based software techniques are becoming recognized methods that can potentially help the inspectors analyze the physical and functional condition of infrastructures from images and/or videos of the region of interest. More recently, deep learning approaches have been shown robust in identifying damages; yet these methods require precisely labeled large amount of training data for high accuracy complementary to visual assessment of inspectors. Especially in image segmentation operations, in which damages are subtracted from the image background for further analysis, there is a strong need to localize the damaged region prior to segmentation operation. However, available segmentation methods mostly focus on the latter step (i.e., delineation), and mis-localization of damaged regions causes accuracy drops. Inspired by the superiority of human cognitive system, where recognizing objects is simpler and more efficient than machine learning algorithms, which are superior to human in local tasks, this paper describes a novel method to dramatically improve the accuracy of the damage quantification (detection + segmentation) using an attention-guided technique. In the proposed method, a fast object detection model, Single Shot Detector (SSD) trained on VGG-16 base classifier architecture, performs a real-time crack and spall detection while working interactively with the human inspector to ensure recognition of the region of interest is well-performed. Upon the inspector's verification, happening in real-time, the detected damage region is used for damage segmentation for further analysis. This initial region of interest selection drastically lowers the computational cost, required amount of training data and reduces number of outliers. For optimal performance, a modified version of SegNet architecture was used for damage segmentation. Based on various performance criteria, the proposed attention-guided infrastructure damage analysis technique provides 30% more precision with a very minor sacrifice in computational speed compared to analysis without using attention guide.
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Concrete bridge decks are vital components of bridges that provide the driving surface to bridge users. Partial or complete failure of this component has a significant impact on the overall performance of the bridge and, consequently, on the subsequent highway network(s). In this regard, periodical inspections are typically conducted to ensure the integrity of bridges and to determine the required maintenance, rehabilitation, and replacement work. Nevertheless, current practices in inspection (i.e., visual inspection) are time-consuming and suffer from several limitations, such as subjectivity and uncertainty. In addition, visual inspection provides limited defect-detection capabilities. Therefore, nondestructive technologies (NDTs), such as impact echo, ultrasonic surface wave, half-cell potential, ground-penetrating radar, infrared thermography, and image-based techniques, have been incorporated into inspection processes to tackle such limitations. However, none of these technologies can identify all types of defects, which reveals the critical need for a comprehensive inspection system. Accordingly, several studies have incorporated multitechnology systems in the inspection process to expand defect-detection capabilities and to ensure successful inspection outcomes. Previous studies also investigated the performance criteria of different NDTs, which provide beneficial information for choosing the most effective techniques for inspection purposes. The present research aims at assessing the capabilities of different NDTs, providing an overview of the developed multitechnology systems and reviewing the main criteria to measure the performance of NDTs. The review provides insight into recent developments in the inspection process, which helps in identifying future needs in this field.
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The vast number of deteriorated bridge infrastructure highlights the importance of implementing efficient and timely maintenance and rehabilitation strategies. Non-contact testing (NCT) technologies are potential tools for bridge condition assessment, facilitating fast and easy measurement with minimal traffic disruption, compared to conventional methods. The foremost objective of this study is to conduct a comprehensive review of the common NCT technologies for concrete bridge condition monitoring including defect detection, rebar corrosion, delamination, and cracking. Four NCT technologies, namely, ground penetrating radar, close-range photogrammetry, infrared thermography, and terrestrial laser scanning have been identified and analysed in depth. The review compiled one hundred and thirty-five laboratory and field studies, spanning from 2000 to 2019, on non-invasive assessment of bridge condition monitoring. In addition to comparison of stand-alone NCT methods, the study analysed integrated NCT systems, data acquisition tools, and processing and simulation platforms. Additionally, challenges and future perspectives of the reviewed NCT technologies are highlighted.
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This study aims to reveal the effect and correlation of delamination size and defect shape for using infrared thermography (IRT) through FE modeling to enhance the reliability and applicability of IRT for effective structural inspections. Regarding the effect of delamination size, it is observed that the temperature difference between sound and delaminated area (\(\Delta \)T) increases as the size of delamination increases; however, \(\Delta \)T converges to a certain value when the area is 40 \(\times \) 40 cm and the thickness is 1 cm. As for the shape of delamination, it can be assumed that if the aspect ratio which is the ratio of the length of the shorter side to the longer side of the delamination is more than 25%, \(\Delta \)T of any delaminations converges to \(\Delta \)T of the same area of a square/circular-shaped delamination. Furthermore, if the aspect ratio is 25% or smaller, \(\Delta \)T becomes smaller than the \(\Delta \)T of the same area of a square/circular-shaped delamination, and it is getting smaller as the ratio becomes smaller. Furthermore, this study attempts to estimate depths of delaminations by using IRT data. Based on the correlation between the size of delamination and the depth from the concrete surface in regard to \(\Delta \)T, it was assumed that it was possible to estimate the depth of delamination by comparing \(\Delta \)T from IRT data to \(\Delta \)T at several depths obtained from FE model simulations. Through the investigation using IRT data from real bridge deck scanning, this study concluded that this estimation method worked properly to provide delamination depth information by incorporating IRT with FE modeling.
Thesis
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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.
Article
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Infrared and visible cameras were mounted on an unmanned aerial vehicle (UAV) to image bridge deck surfaces and map likely concrete delaminations. The infrared sensor was first tested on laboratory validation experiments, to assess how well it could detect and map delaminations under controlled conditions. Field tests on two bridge deck surfaces further extend the validation dataset to real-world conditions for heterogeneous concrete surfaces. Performance of the mapping instrument and algorithms were evaluated through receiver operating characteristic (ROC) curves, giving acceptable results. To improve the performance of the mapping by reducing the rate of false positives, i.e., areas wrongly mapped as being affected by delamination, visible images were jointly analyzed with the infrared imagery. The potential for expanding the method to include other products derived from the visible camera data, including high density 3D point locations generated by photogrammetric methods, promises to further improve the performance of the method, potentially making it a viable and more effective option compared to other platforms and systems for imaging bridge decks for mapping delaminations.
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There is a need for rapid and objective assessment of concrete bridge decks for maintenance decision making. Infrared Thermography (IRT) has great potential to identify deck delaminations more objectively than routine visual inspections or chain drag tests. In addition, it is possible to collect reliable data rapidly with appropriate IRT cameras attached to vehicles and the data are analyzed effectively. This research compares three infrared cameras with different specifications at different times and speeds for data collection, and explores 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 inspection at normal driving speeds. These results show that IRT can detect up to 2.54 cm delamination from the concrete surface at any time period. It is observed that nighttime would be the most suitable time frame with less false detections and interferences from the sunlight and less adverse effect due to direct sunlight, making more " noise " for the IRT results. This study also revealed two important factors of camera specifications for high-speed inspection by IRT as shorter integration time and higher pixel resolution.
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A detailed investigation of infrared thermography (IRT) for civil structures is presented by considering different technologies, data analysis methods and experimental conditions in the laboratory and also in the field. Three different types of infrared (IR) camera were compared under active IRT conditions in the laboratory to examine the effect of photography angle on IRT along with the specifications of cameras. It is found 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 area 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, and it is much easier to detect delamination than raw IR images. Specially designed laboratory and field studies show the capabilities, opportunities and challenges of implementing IRT for civil structures.
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To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research.
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The main objective of this study is to comprehensively evaluate the utilization of infrared thermography (IRT) considering different technologies, critical environmental parameters, and uncertainties for bridge deck evaluation. For this purpose, a real bridge was scanned and the results were compared with other nondestructive evaluation (NDE) technologies that were implemented on the same bridge. There are a number of considerations and factors that affect the utilization of IRT, such as thermal contrasts, camera specifications, distance, and utilization speed, and these are evaluated by using three different infrared (IR) cameras with different specifications. These considerations are discussed and results are presented. When compared at fully documented locations with eight 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 with other NDE methods from past research, and the results revealed that the cooled camera showed shapes almost identical to other NDE methods including chain drag. It should be noted that these data were collected at a normal driving speed without any lane closures, making it a more practical and faster method than other NDE technologies. It is also presented that the factor most likely to affect high-speed application is integration time of the IR camera.
Article
The present study explores the potential application of unmanned aerial vehicle (UAV) Infrared Thermography for detecting subsurface delaminations in concrete bridge decks, which requires neither traffic interruption nor physical contact with the deck being inspected. A UAV-borne thermal imaging system was utilized to survey two in-service concrete bridge decks. The inspection process involved the acquisition of thermal images via low altitude flights using a high resolution thermal camera. The images were then enhanced and stitched together using custom developed codes to create a mosaic thermal image for the entire bridge deck. Image analysis based on the k-means clustering technique was utilized to segment the mosaic and identify objective thresholds. Hence, a condition map delineating different categories of delamination severity was created. The results were validated using data generated by other non-destructive testing technologies on the same bridge decks, namely hammer sounding and half-cell potential testing. The findings reveal that UAV with high-resolution thermal infrared imagery offers an efficient tool for precisely detecting subsurface anomalies in bridge decks. The proposed methodology allows more frequent and less costly bridge deck inspection without traffic interruption. This should enable rapid bridge condition assessment at various service live stages, thus effectively allocating maintenance and repair funds.
Article
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.
Article
Infrared thermography (IRT) has been used experimentally for concrete delamination detection. The past studies were conducted with limited experimental conditions, which make a difference in delamination detection. As a result, there are inconsistencies in the results reported in the literature. In this study, heat transfer models of concrete blocks with artificial delamination used for a previous test are developed and analyzed to explore sensitive parameters for effective utilization of IRT. After these FE models are validated, critical parameters and factors of delamination detectability such as the size of delamination (area, thickness and volume), ambient temperature and solar irradiance conditions (different seasons), and the depth of delamination from the surface are 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. This study shows a potential to bring significant improvement to IRT use in the field for subsurface damage detection for concrete structures.
Article
The rapid, cost-effective, and non-disruptive assessment of bridge deck condition has emerged as a critical challenge for bridge maintenance. Deck delaminations are a common form of deterioration which has been assessed, historically, through chain-drag techniques and more recently through nondestructive evaluation (NDE) including both acoustic and optical methods. Although NDE methods have proven to be capable to provide information related to the existence of delaminations in bridge decks, many of them are time-consuming, labor-intensive, expensive, while they further require significant disruptions to traffic. In this context, this article demonstrates the capability of unmanned aerial vehicles (UAVs) equipped with both color and infrared cameras to rapidly and effectively detect and estimate the size of regions where subsurface delaminations exist. To achieve this goal, a novel image post-processing algorithm was developed to use such multispectral imagery obtained by a UAV. To evaluate the capabilities of the presented approach, a bridge deck mockup with pre-manufactured defects was tested. The major advantages of the presented approach include its capability to rapidly identify locations where delaminations exist, as well as its potential to automate bridge-deck related damage detection procedures and further guide investigations using other higher accuracy and ground-based approaches.
Article
Infrared Thermography (IRT) is one of the nondestructive inspection techniques to detect delaminations in concrete bridge decks. These defects are identified by capturing the temperature gradient of concrete surfaces. In order for this technique to be effective in damage detection, IRT inspections should be conducted at certain time windows with favorable temperature conditions to get clear temperature gradients on inspected surfaces. This study is an experimental work examining the effects of ambient environmental conditions at different times of a day to locate subsurface delaminations and voids at a shallow depth, which is an additional influencing factor. This study also attempts to figure out a relationship between ambient environmental conditions and the temperature values of concrete surfaces to estimate the best time window with appropriate environmental conditions for IRT inspections. To this end, specially designed reusable concrete test plates with different thicknesses were manufactured to collect thermocouple sensor readings. Multiple regression analyses were employed to generate prediction models that seek a relationship between environmental conditions and temperature gradients on the test plates attached to a target bridge. Regression models also utilized sensor data collected at another location different than the target bridge location. It was found out that the most important aspect of sensor data collection was to accomplish a perfect contact of test plates with concrete bridge deck surfaces to get discernible temperature gradients. When this condition is not met, data analyses yield spurious results leading to futile conclusions. On the other hand, it was also observed that prediction models generated by regression analyses followed the same pattern as that of sensor readings. This makes it possible to have prediction equations based on sensor readings to determine suitable time window for conducting IRT inspections.
Article
Unmanned aerial vehicles (UAVs) allow remote imaging which can be useful in infrastructure condition evaluation. Furthermore, emerging noncontact sensing techniques such as digital imaging correlation (DIC) and other photogrammetric and visual approaches, including simultaneous localization and mapping (SLAM), can compute three-dimensional (3D) coordinates and perform deformation measurements as in the case of DIC/photogrammetry. A quantitative assessment of ways remote sensing in conjunction with UAVs could be implemented in practical applications is critically needed to leverage such capabilities in structural health monitoring (SHM). A comparative investigation of the remote sensing capabilities of a commercially availabl e UAV, as well as both an optical metrology system known by the acronym TRITOP and the X-Box Kinect, is presented in this paper. The evidence provided demonstrates that red-green-blue cameras on UAVs could detect, from varying distances, cracks of sizes comparable to those currently sought in visual inspections. In addition, mechanical tests were performed on representative bridge structural components to attempt, for the first time to the writers' best knowledge, deformation measurements using an aerial vehicle; displacements and corresponding accuracies were quantified in static and flying conditions. Finally, an outdoor feasibility test with the UAV was accomplished on a pedestrian bridge to test the marker identification algorithm.
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Weather History for Orlando, FL [KFLORLAN72
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2013 Status of the Nation's Highways, Bridges, and Transit: Conditions & Performance
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