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STRUCTURAL HEALTH MONITORING WITH EMPHASIS ON COMPUTER VISION, DAMAGE INDICES, AND STATISTICAL ANALYSIS
Title
STRUCTURAL HEALTH MONITORING WITH EMPHASIS ON COMPUTER VISION, DAMAGE INDICES, AND STATISTICAL ANALYSIS.
Creator
ZAURIN, RICARDO, CATBAS, F. NECATI, University of Central Florida
Abstract / Description
Structural Health Monitoring (SHM) is the sensing and analysis of a structure to detect abnormal behavior, damage and deterioration during regular operations as well as under extreme loadings. SHM is designed to provide objective information for decision-making on safety and serviceability. This research focuses on the SHM of bridges by developing and integrating novel methods and techniques using sensor networks, computer vision, modeling for damage indices and statistical approaches....
Show moreStructural Health Monitoring (SHM) is the sensing and analysis of a structure to detect abnormal behavior, damage and deterioration during regular operations as well as under extreme loadings. SHM is designed to provide objective information for decision-making on safety and serviceability. This research focuses on the SHM of bridges by developing and integrating novel methods and techniques using sensor networks, computer vision, modeling for damage indices and statistical approaches. Effective use of traffic video synchronized with sensor measurements for decision-making is demonstrated. First, some of the computer vision methods and how they can be used for bridge monitoring are presented along with the most common issues and some practical solutions. Second, a conceptual damage index (Unit Influence Line) is formulated using synchronized computer images and sensor data for tracking the structural response under various load conditions. Third, a new index, Nd , is formulated and demonstrated to more effectively identify, localize and quantify damage. Commonly observed damage conditions on real bridges are simulated on a laboratory model for the demonstration of the computer vision method, UIL and the new index. This new method and the index, which are based on outlier detection from the UIL population, can very effectively handle large sets of monitoring data. The methods and techniques are demonstrated on the laboratory model for damage detection and all damage scenarios are identified successfully. Finally, the application of the proposed methods on a real life structure, which has a monitoring system, is presented. It is shown that these methods can be used efficiently for applications such as damage detection and load rating for decision-making. The results from this monitoring project on a movable bridge are demonstrated and presented along with the conclusions and recommendations for future work.
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Date Issued
2009
Identifier
CFE0002890, ucf:48039
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002890
Point Cloud Technology for Analysis of Existing Structures
Title
Point Cloud Technology for Analysis of Existing Structures.
Creator
Cano, Jacob, Catbas, Necati, Apostolakis, Georgios, Zaurin, Ricardo, Walters, Lori, University of Central Florida
Abstract / Description
For this thesis, a study was completed on two different structures on the UCF Orlando campus through the use of both structural plans and point cloud technology. The results sought to understand the viability of point cloud technology as an accurate tool for the static and dynamic modal analysis of existing structures. For static analysis, a portion of the framing of Spectrum Stadium was rendered, modeled, analyzed and compared to a previous case study. The results emphasized how different...
Show moreFor this thesis, a study was completed on two different structures on the UCF Orlando campus through the use of both structural plans and point cloud technology. The results sought to understand the viability of point cloud technology as an accurate tool for the static and dynamic modal analysis of existing structures. For static analysis, a portion of the framing of Spectrum Stadium was rendered, modeled, analyzed and compared to a previous case study. The results emphasized how different users can render dissimilar member sizes and lengths due to human judgment on point cloud visuals. The study also found that structural plans cannot always be relied upon as the most accurate source for analysis as the new point cloud produced more accurate results than the structural plans when compared to the control model. For the pedestrian bridge, the structure was scanned, rendered and modeled for both static and dynamic modal analysis. The point cloud produced from scanning the bridge was modified twice in order to have three distinct point clouds with varying densities: fine, medium and coarse. These three cases were compared to structural plans in a static analysis. The fine point cloud produced the most accurate displacement results with an accuracy above 96%. The data sources were also compared to experimental data under dynamic modal analysis to discover how lessening the density of point clouds affect the accuracy of results. The analysis showed that point cloud technology can give you an accuracy of 88% and above for frequency while also producing MAC values exceeding 0.9 consistently. Also, changes in density were found to change the accuracy of results but the numeric values stayed within close proximity by not differing more than 10%. This thesis shines a light on the accuracy point cloud technology can ascertain and the potential it has within engineering.
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Date Issued
2019
Identifier
CFE0007438, ucf:52724
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007438
Fluid Dynamics Modeling and Sound Analysis of a Bileaflet Mechanical Heart Valve
Title
Fluid Dynamics Modeling and Sound Analysis of a Bileaflet Mechanical Heart Valve.
Creator
Khalili, Fardin, Mansy, Hansen, Kassab, Alain, Steward, Robert, Zaurin, Ricardo, University of Central Florida
Abstract / Description
Cardiovascular disease (CVD) is one of the main causes of death in the world. Some CVD involve severe heart valve disease that require valve replacement. There are more than 300,000 heart valves implanted worldwide, and about 85,000 heart valve replacements in the US. Approximately half of these valves are mechanical. Artificial valves may dysfunction leading to adverse hemodynamic conditions. Understanding the normal and abnormal valve function is important as it help improve valve designs....
Show moreCardiovascular disease (CVD) is one of the main causes of death in the world. Some CVD involve severe heart valve disease that require valve replacement. There are more than 300,000 heart valves implanted worldwide, and about 85,000 heart valve replacements in the US. Approximately half of these valves are mechanical. Artificial valves may dysfunction leading to adverse hemodynamic conditions. Understanding the normal and abnormal valve function is important as it help improve valve designs. Modeling of heart valve hemodynamics using computational fluid dynamics (CFD) provides a comprehensive analysis of flow, which can potentially help explain clinical observations and support therapeutic decision-making. This detailed information might not be accessible with in-vivo measurements. On the other hand, finite element analysis (FEA), is an efficient way to analyze the interactions of blood flow with blood vessel and tissue layers. In this project both CFD and FEA simulations were performed to investigate the flow-induced sound generation and propagation of sound waves through a tissue-like material. This method is based on mapping the transient pressure (force) fluctuations on the vessel wall and solving for the structural vibrations in the frequency domain. These vibrations would then be detected as sound on the epidermal surface. Advantages of the methods used in the current study include: (a) capability of providing accurate solution with a faster solution time; (b) inclusion of the fluid(-)structure interaction between blood flow and the arterial wall; and (c) accurately capturing some of the spectral features of the velocity fluctuation measured over the epidermal surface.
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Date Issued
2018
Identifier
CFE0007029, ucf:52038
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007029
Investigation of Damage Detection Methodologies For Structural Health Monitoring of Thin-Walled Pressure Vessels
Title
Investigation of Damage Detection Methodologies For Structural Health Monitoring of Thin-Walled Pressure Vessels.
Creator
Modesto, Arturo, Catbas, Necati, Chopra, Manoj, Zaurin, Ricardo, University of Central Florida
Abstract / Description
There is a need in exploring structural health monitoring technologies for the composite structures particularly aged Composite Overwrapped Pressure Vessels (COPVs) for the current and future implementation of COPVs for space missions. In this study, the research was conducted in collaboration with NASA Kennedy Space Center and also NASA Marshall Space and Flight Center engineers. COPVs have been used to store inert gases like helium (for propulsion) and nitrogen (for life support) under...
Show moreThere is a need in exploring structural health monitoring technologies for the composite structures particularly aged Composite Overwrapped Pressure Vessels (COPVs) for the current and future implementation of COPVs for space missions. In this study, the research was conducted in collaboration with NASA Kennedy Space Center and also NASA Marshall Space and Flight Center engineers. COPVs have been used to store inert gases like helium (for propulsion) and nitrogen (for life support) under varying degrees of pressure onboard the orbiter since the beginning of the Space Shuttle Program. After the Columbia accident, the COPVs were re-examined and different studies (e.g. Laser profilometry inspection, NDE utilizing Raman Spectroscopy) have been conducted and can be found in the literature. To explore some of the unique in-house developed hardware and algorithms for monitoring COPVs, this project is carried out with the following general objectives:1) Investigate the obtaining indices/features related to the performance and/or condition of pressure vessels2) Explore different sensing technologies and Structural Health Monitoring (SHM) systems3) Explore different types of data analysis methodologies to detect damage with particular emphasis on statistical analysis, cross-correlation analysis and Auto Regressive model with eXogeneous input (ARX) models4) Compare differences in various types of pressure vesselsFirst an introduction to theoretical pressure vessels, which are used to compare to actual test specimens, is presented. Next, a background review of the test specimens including their applications and importance is discussed. Subsequently, a review of related SHM applications to this study is presented. The theoretical background of the data analysis methodologies used to detect damage in this study are provided and these methodologies are applied in the laboratory using Composite Overwrapped Pressure Vessels (COPVs) to determine the effectiveness of these techniques. Next another study on the Air Force Research Laboratory (AFRL) Tank that is carried out in collaboration with NASA KSC and NASA MSFC is presented with preliminary results. Finally the results and interpretations of both studies are summarized and discussed.
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Date Issued
2015
Identifier
CFE0005978, ucf:50770
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005978
INVESTIGATION OF THE EFFECT OF EDGE-OXIDIZED GRAPHENE OXIDE (EOGO) ON THE PROPERTIES OF CEMENT COMPOSITES
Title
INVESTIGATION OF THE EFFECT OF EDGE-OXIDIZED GRAPHENE OXIDE (EOGO) ON THE PROPERTIES OF CEMENT COMPOSITES.
Creator
Alharbi, Yousef, Nam, Boo Hyun, Chopra, Manoj, Zaurin, Ricardo, Kwok, Kawai, University of Central Florida
Abstract / Description
The use of edge-oxidized graphene oxide (EOGO), produced by a mechanochemical ?process that allow to deliver a product suitable for large-scale production at affordable cost, as ?an additive in cement composites was investigated. Comprehensive experimental tests were ?conducted to investigate the effect of EOGO on the properties of cement composites. The ?experimental tests were designed for three subtasks: (1) investigation of the performance of ?EOGO and its mixing method on the strength,...
Show moreThe use of edge-oxidized graphene oxide (EOGO), produced by a mechanochemical ?process that allow to deliver a product suitable for large-scale production at affordable cost, as ?an additive in cement composites was investigated. Comprehensive experimental tests were ?conducted to investigate the effect of EOGO on the properties of cement composites. The ?experimental tests were designed for three subtasks: (1) investigation of the performance of ?EOGO and its mixing method on the strength, pore structure and microstructure of EOGO-?cement composites, (2) evaluation of the rheological and fluidity behavior of EOGO-cement ?paste and mortar, and (3) investigation of the mechanism of the enhanced workability of ?EOGO-concrete. EOGO content ranged from 0.01% to 1% and two mix design methods were ?employed for cement paste and mortar to explore an optimum and feasible mix design of ?EOGO. Compressive and flexural strength tests were conducted to investigate the mechanical ?performance of EOGO-cement composites. Total porosity and water sorptivity were performed ?to investigate the pore structure of EOGO-cement paste and mortar. Furthermore, petrographic ?analyses were conducted to characterize the microstructure of EOGO-cement composites. ?Imaged based-mini-slump and flow table tests were performed to measure the fluidity of ?EOGO-cement paste and mortar. The rheological properties of EOGO-cement paste were ?measured through viscometer test. The mechanism of the enhanced workability of EOGO-?concrete was investigated by performing slump and water absorption of aggregate in cement ?paste tests. The key findings are (1) the addition of EOGO into cement composites improves the ?compressive and flexural strength, (2) 0.05% of EOGO is the optimum content to improve the ?strength and pore structure of EOGO-cement composites, (3) the addition of EOGO reduces the ?fluidity and increases the viscosity of EOGO-cement composites, (4) the addition of EOGO ?improves the workability of concrete, and (5) dry-mix design is feasible and more practical for ?large-scale production.?
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Date Issued
2019
Identifier
CFE0007425, ucf:52721
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007425
Mechanical Study on Edge-Oxidized Graphene Oxide (EOGO) Reinforced Concrete
Title
Mechanical Study on Edge-Oxidized Graphene Oxide (EOGO) Reinforced Concrete.
Creator
Khawaji, Mohammad, Nam, Boo Hyun, Chopra, Manoj, Zaurin, Ricardo, Kwok, Kawai, University of Central Florida
Abstract / Description
It is known that graphene oxide (GO) has superior mechanical properties and can enhance mechanical properties of cement composites. However, Hummer produced conventional GOs have been limited to small-scale specimens (e.g., cement paste and mortar) and applications to concrete have not been implemented due to their high cost and large volume of concrete. Edge-oxidized graphene oxide (EOGO) is a low-cost, carbon-based nanomaterial produced by a mechanochemical process with ball milling and a...
Show moreIt is known that graphene oxide (GO) has superior mechanical properties and can enhance mechanical properties of cement composites. However, Hummer produced conventional GOs have been limited to small-scale specimens (e.g., cement paste and mortar) and applications to concrete have not been implemented due to their high cost and large volume of concrete. Edge-oxidized graphene oxide (EOGO) is a low-cost, carbon-based nanomaterial produced by a mechanochemical process with ball milling and a non-toxic oxidizing agent. The low cost (less than $50/kg) of EOGO enables its use in bulk-scale concrete materials/structures, which is a prerequisite for the field implementation. In this study, EOGO was applied to macroscopic concrete to investigate mechanical and workability performance of EOGO reinforced concrete. Interestingly, it was observed that the addition of EOGO to normal concrete increases concrete slump, which opposes the conventional GO study on cement paste. To maximize the benefits of the improved workability, EOGO was then applied to fiber reinforced concretes (FRCs) to compensate their low workability. Two different types of fibers were used, including basalt and steel fibers. The results indicated that EOGO is not effective in basalt fiber reinforced concrete (BFRC) perhaps due to the high absorption of basalt fibers. However, adding EOGO to steel fiber reinforced concrete (SFRC) exhibited significant enhancement in workability and strength compared with control specimens. Subsequently, the effect of EOGO on flexural fatigue behavior of cement composite mixtures (cement mortar and concrete) was investigated. The flexural fatigue results show that adding EOGO to cement composites enhances flexural fatigue performance. Lastly, the impact of EOGO on pavement structure was investigated based on Mechanistic-Empirical Design Guide (MEPDG). The results show EOGO significantly extends service life and minimizes the required thickness of surface layer.
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Date Issued
2019
Identifier
CFE0007826, ucf:52821
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007826
Investigation of infrared thermography for subsurface damage detection of concrete structures
Title
Investigation of infrared thermography for subsurface damage detection of concrete structures.
Creator
Hiasa, Shuhei, Catbas, Necati, Tatari, Omer, Nam, Boo Hyun, Zaurin, Ricardo, Xanthopoulos, Petros, University of Central Florida
Abstract / Description
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...
Show moreDeterioration 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.
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Date Issued
2016
Identifier
CFE0006323, ucf:51575
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006323
Computer Vision Based Structural Identification Framework for Bridge Health Mornitoring
Title
Computer Vision Based Structural Identification Framework for Bridge Health Mornitoring.
Creator
Khuc, Tung, Catbas, Necati, Oloufa, Amr, Mackie, Kevin, Zaurin, Ricardo, Shah, Mubarak, University of Central Florida
Abstract / Description
The objective of this dissertation is to develop a comprehensive Structural Identification (St-Id) framework with damage for bridge type structures by using cameras and computer vision technologies. The traditional St-Id frameworks rely on using conventional sensors. In this study, the collected input and output data employed in the St-Id system are acquired by series of vision-based measurements. The following novelties are proposed, developed and demonstrated in this project: a) vehicle...
Show moreThe objective of this dissertation is to develop a comprehensive Structural Identification (St-Id) framework with damage for bridge type structures by using cameras and computer vision technologies. The traditional St-Id frameworks rely on using conventional sensors. In this study, the collected input and output data employed in the St-Id system are acquired by series of vision-based measurements. The following novelties are proposed, developed and demonstrated in this project: a) vehicle load (input) modeling using computer vision, b) bridge response (output) using full non-contact approach using video/image processing, c) image-based structural identification using input-output measurements and new damage indicators. The input (loading) data due vehicles such as vehicle weights and vehicle locations on the bridges, are estimated by employing computer vision algorithms (detection, classification, and localization of objects) based on the video images of vehicles. Meanwhile, the output data as structural displacements are also obtained by defining and tracking image key-points of measurement locations. Subsequently, the input and output data sets are analyzed to construct novel types of damage indicators, named Unit Influence Surface (UIS). Finally, the new damage detection and localization framework is introduced that does not require a network of sensors, but much less number of sensors.The main research significance is the first time development of algorithms that transform the measured video images into a form that is highly damage-sensitive/change-sensitive for bridge assessment within the context of Structural Identification with input and output characterization. The study exploits the unique attributes of computer vision systems, where the signal is continuous in space. This requires new adaptations and transformations that can handle computer vision data/signals for structural engineering applications. This research will significantly advance current sensor-based structural health monitoring with computer-vision techniques, leading to practical applications for damage detection of complex structures with a novel approach. By using computer vision algorithms and cameras as special sensors for structural health monitoring, this study proposes an advance approach in bridge monitoring through which certain type of data that could not be collected by conventional sensors such as vehicle loads and location, can be obtained practically and accurately.
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Date Issued
2016
Identifier
CFE0006127, ucf:51174
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006127