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Title: COMPRESSIVE AND CODED CHANGE DETECTION: THEORY AND APPLICATION TO STRUCTURAL HEALTH MONITORING.
Creator: Sarayanibafghi, Omid, Atia, George, Vosoughi, Azadeh, Rahnavard, Nazanin, University of Central Florida
Abstract / Description: In traditional sparse recovery problems, the goal is to identify the support of compressible signals using a small number of measurements. In contrast, in this thesis the problem of identification of a sparse number of statistical changes in stochastic phenomena is considered when decision makers only have access to compressed measurements, i.e., each measurement is derived by a subset of features. Herein, we propose a new framework that is termed Compressed Change Detection. The main...
Show moreIn traditional sparse recovery problems, the goal is to identify the support of compressible signals using a small number of measurements. In contrast, in this thesis the problem of identification of a sparse number of statistical changes in stochastic phenomena is considered when decision makers only have access to compressed measurements, i.e., each measurement is derived by a subset of features. Herein, we propose a new framework that is termed Compressed Change Detection. The main approach relies on integrating ideas from the theory of identifying codes with change point detection in sequential analysis. If the stochastic properties of certain features change, then the changes can be detected by examining the covering set of an identifying code of measurements. In particular, given a large number N of features, the goal is to detect a small set of features that undergoes a statistical change using a small number of measurements. Sufficient conditions are derived for the probability of false alarm and isolation to approach zero in the asymptotic regime where N is large.As an application of compressed change detection, the problem of detection of a sparse number of damages in a structure for Structural Health Monitoring (SHM) is considered. Since only a small number of damage scenarios can occur simultaneously, change detection is applied to responses of pairs of sensors that form an identifying code over a learned damage-sensing graph. Generalizations of the proposed framework with multiple concurrent changes and for arbitrary graph topologies are presented.
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Date Issued: 2016
Identifier: CFE0006387, ucf:51507
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006387

Title: Structural Health Monitoring using Novel Sensing Technologies and Data Analysis Methods.
Creator: Malekzadeh, Seyedmasoud, Catbas, Fikret, Yun, Hae-Bum, Tatari, Mehmet, Moslehy, Faissal, Gul, Mustafa, University of Central Florida
Abstract / Description: The main objective of this research is to explore, investigate and develop the new data analysis techniques along with novel sensing technologies for structural health monitoring applications. The study has three main parts. First, a systematic comparative evaluation of some of the most common and promising methods is carried out along with a combined method proposed in this study for mitigating drawbacks of some of the techniques. Secondly, non-parametric methods are evaluated on a real life...
Show moreThe main objective of this research is to explore, investigate and develop the new data analysis techniques along with novel sensing technologies for structural health monitoring applications. The study has three main parts. First, a systematic comparative evaluation of some of the most common and promising methods is carried out along with a combined method proposed in this study for mitigating drawbacks of some of the techniques. Secondly, non-parametric methods are evaluated on a real life movable bridge. Finally, a hybrid approach for non-parametric and parametric method is proposed and demonstrated for more in depth understanding of the structural performance. In view of that, it is shown in the literature that four efficient non-parametric algorithms including, Cross Correlation Analysis (CCA), Robust Regression Analysis (RRA), Moving Cross Correlation Analysis (MCCA) and Moving Principal Component Analysis (MPCA) have shown promise with respect to the conducted numerical studies. As a result, these methods are selected for further systematic and comparative evaluation using experimental data. A comprehensive experimental test is designed utilizing Fiber Bragg Grating (FBG) sensors simulating some of the most critical and common damage scenarios on a unique experimental structure in the laboratory. Subsequently the SHM data, that is generated and collected under different damage scenarios, are employed for comparative study of the selected techniques based on critical criteria such as detectability, time to detection, effect of noise, computational time and size of the window. The observations indicate that while MPCA has the best detectability, it does not perform very reliable results in terms of time to detection. As a result, a machine-learning based algorithm is explored that not only reduces the associated delay with MPCA but further improves the detectability performance. Accordingly, the MPCA and MCCA are combined to introduce an improved algorithm named MPCA-CCA. The new algorithm is evaluated through both experimental and real-life studies. It is realized that while the methods identified above have failed to detect the simulated damage on a movable bridge, the MPCA-CCA algorithm successfully identified the induced damage. An investigative study for automated data processing method is developed using non-parametric data analysis methods for real-time condition maintenance monitoring of critical mechanical components of a movable bridge. A maintenance condition index is defined for identifying and tracking the critical maintenance issues. The efficiency of the maintenance condition index is then investigated and demonstrated against some of the corresponding maintenance problems that have been visually and independently identified for the bridge.Finally, a hybrid data interpretation framework is designed taking advantage of the benefits of both parametric and non-parametric approaches and mitigating their shortcomings. The proposed approach can then be employed not only to detect the damage but also to assess the identified abnormal behavior. This approach is also employed for optimized sensor number and locations on the structure.
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Date Issued: 2014
Identifier: CFE0005207, ucf:50648
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005207

Title: STRUCTURAL HEALTH MONITORING OF COMPOSITE OVERWRAPPED PRESSURE VESSELS.
Creator: Letizia, Luca, Catbas, F. Necati, University of Central Florida
Abstract / Description: This work is focusing to study the structural behavior of Composite Overwrapped Pressure Vessels (COPVs). These COPVs are found in many engineering applications. In the aerospace field, they are installed onto spaceships and aid the reorientation of the spacecraft in very far and airless, therefore frictionless, orbits to save energy and fuel. The intent of this research is to analyze the difference in performance of both perfectly intact and purposely damaged tanks. Understanding both the...
Show moreThis work is focusing to study the structural behavior of Composite Overwrapped Pressure Vessels (COPVs). These COPVs are found in many engineering applications. In the aerospace field, they are installed onto spaceships and aid the reorientation of the spacecraft in very far and airless, therefore frictionless, orbits to save energy and fuel. The intent of this research is to analyze the difference in performance of both perfectly intact and purposely damaged tanks. Understanding both the source and location of a structural fault will help NASA engineers predict the performance of COPVs subject to similar conditions, which could prevent failures of important missions. The structural behavior of six tanks is investigated by means of experimental modal analysis. Knowledge of statistical signal processing methods allows to sort out and extract meaningful features from the data as to gain understanding of the performance of the structures. Structural identification is carried out using Narrow Band and Broad Band algorithms. A comparison through correlation tables and figures presents the differences in natural frequencies, mode shapes and damping ratios of all structures. A careful analysis displays the deviation of these modal parameters in the damaged tanks, highlighting the evident structural defects.
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Date Issued: 2016
Identifier: CFH2000069, ucf:45514
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFH2000069

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

Title: INVESTIGATION OF DAMAGE DETECTION METHODOLOGIES FOR STRUCTURAL HEALTH MONITORING.
Creator: Gul, Mustafa, Catbas, F. Necati, University of Central Florida
Abstract / Description: Structural Health Monitoring (SHM) is employed to track and evaluate damage and deterioration during regular operation as well as after extreme events for aerospace, mechanical and civil structures. A complete SHM system incorporates performance metrics, sensing, signal processing, data analysis, transmission and management for decision-making purposes. Damage detection in the context of SHM can be successful by employing a collection of robust and practical damage detection methodologies...
Show moreStructural Health Monitoring (SHM) is employed to track and evaluate damage and deterioration during regular operation as well as after extreme events for aerospace, mechanical and civil structures. A complete SHM system incorporates performance metrics, sensing, signal processing, data analysis, transmission and management for decision-making purposes. Damage detection in the context of SHM can be successful by employing a collection of robust and practical damage detection methodologies that can be used to identify, locate and quantify damage or, in general terms, changes in observable behavior. In this study, different damage detection methods are investigated for global condition assessment of structures. First, different parametric and non-parametric approaches are re-visited and further improved for damage detection using vibration data. Modal flexibility, modal curvature and un-scaled flexibility based on the dynamic properties that are obtained using Complex Mode Indicator Function (CMIF) are used as parametric damage features. Second, statistical pattern recognition approaches using time series modeling in conjunction with outlier detection are investigated as a non-parametric damage detection technique. Third, a novel methodology using ARX models (Auto-Regressive models with eXogenous output) is proposed for damage identification. By using this new methodology, it is shown that damage can be detected, located and quantified without the need of external loading information. Next, laboratory studies are conducted on different test structures with a number of different damage scenarios for the evaluation of the techniques in a comparative fashion. Finally, application of the methodologies to real life data is also presented along with the capabilities and limitations of each approach in light of analysis results of the laboratory and real life data.
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Date Issued: 2009
Identifier: CFE0002830, ucf:48069
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0002830

Title: STRUCTURAL HEALTH MONITORING FOR DAMAGE DETECTION USING WIRED AND WIRELESS SENSOR CLUSTERS.
Creator: Terrell, Thomas, Catbas, Necati, University of Central Florida
Abstract / Description: Sensing and analysis of a structure for the purpose of detecting, tracking, and evaluating damage and deterioration, during both regular operation and extreme events, is referred to as Structural Health Monitoring (SHM). SHM is a multi-disciplinary field, with a complete system incorporating sensing technology, hardware, signal processing, networking, data analysis, and management for interpretation and decision making. However, many of these processes and subsequent integration into a...
Show moreSensing and analysis of a structure for the purpose of detecting, tracking, and evaluating damage and deterioration, during both regular operation and extreme events, is referred to as Structural Health Monitoring (SHM). SHM is a multi-disciplinary field, with a complete system incorporating sensing technology, hardware, signal processing, networking, data analysis, and management for interpretation and decision making. However, many of these processes and subsequent integration into a practical SHM framework are in need of development. In this study, various components of an SHM system will be investigated. A particular focus is paid to the investigation of a previously developed damage detection methodology for global condition assessment of a laboratory structure with a decking system. First, a review of some of the current SHM applications, which relate to a current UCF Structures SHM study monitoring a full-scale movable bridge, will be presented in conjunction with a summary of the critical components for that project. Studies for structural condition assessment of a 4-span bridge-type steel structure using the SHM data collected from laboratory based experiments will then be presented. For this purpose, a time series analysis method using ARX models (Auto-Regressive models with eXogeneous input) for damage detection with free response vibration data will be expanded upon using both wired and wireless acceleration data. Analysis using wireless accelerometers will implement a sensor roaming technique to maintain a dense sensor field, yet require fewer sensors. Using both data types, this ARX based time series analysis method was shown to be effective for damage detection and localization for this relatively complex laboratory structure. Finally, application of the proposed methodologies on a real-life structure will be discussed, along with conclusions and recommendations for future work.
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Date Issued: 2011
Identifier: CFE0003694, ucf:48837
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003694

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

Title: Analytical And Experimental Study Of Monitoring For Chain-Like Nonlinear Dynamic Systems.
Creator: Paul, Bryan, Yun, Hae-Bum, Catbas, Fikret, Chopra, Manoj, University of Central Florida
Abstract / Description: Inverse analysis of nonlinear dynamic systems is an important area of research in the ?eld of structural health monitoring for civil engineering structures. Structural damage usually involves localized nonlinear behaviors of dynamic systems that evolve into different classes of nonlinearity as well as change system parameter values. Numerous parametric modal analysis techniques (e.g., eigensystem realization algorithm and subspace identification method) have been developed for system...
Show moreInverse analysis of nonlinear dynamic systems is an important area of research in the ?eld of structural health monitoring for civil engineering structures. Structural damage usually involves localized nonlinear behaviors of dynamic systems that evolve into different classes of nonlinearity as well as change system parameter values. Numerous parametric modal analysis techniques (e.g., eigensystem realization algorithm and subspace identification method) have been developed for system identification of multi-degree-of-freedom dynamic systems. However, those methods are usually limited to linear systems and known for poor sensitivity to localized damage. On the other hand, non-parametric identification methods (e.g., artificial neural networks) are advantageous to identify time-varying nonlinear systems due to unpredictable damage. However, physical interpretation of non-parametric identification results is not as straightforward as those of the parametric methods. In this study, the Multidegree-of-Freedom Restoring Force Method (MRFM) is employed as a semi-parametric nonlinear identification method to take the advantages of both the parametric and non-parametric identification methods.The MRFM is validated using two realistic experimental nonlinear dynamic tests: (i) large-scale shake table tests using building models with different foundation types, and (ii) impact test using wind blades. The large-scale shake table test was conducted at Tongji University using 1:10 scale 12-story reinforced concrete building models tested on three different foundations, including pile, box and fixed foundation. The nonlinear dynamic signatures of the building models collected from the shake table tests were processed using MRFM (i) to investigate the effects of foundation types on nonlinear behavior of the superstructure and (ii) to detect localized damage during the shake table tests. Secondly, the MRFM was applied to investigate the applicability of this method to wind turbine blades. Results are promising, showing a high level of nonlinearity of the system and how the MRFM can be applied to wind-turbine blades. Future studies were planned for the comparison of physical characteristic of this blade with blades created made of other material.
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Date Issued: 2013
Identifier: CFE0004734, ucf:49818
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004734

Title: Spatial and Temporal Compressive Sensing for Vibration-based Monitoring: Fundamental Studies with Beam Vibrations.
Creator: Ganesan, Vaahini, Das, Tuhin, Kauffman, Jeffrey L., Raghavan, Seetha, University of Central Florida
Abstract / Description: Vibration data from mechanical systems carry important information that is useful for characterization and diagnosis. Standard approaches rely on continually streaming data at a fixed sampling frequency. For applications involving continuous monitoring, such as Structural Health Monitoring (SHM), such approaches result in high data volume and require powering sensors for prolonged duration. Furthermore, adequate spatial resolution, typically involves instrumenting structures with a large...
Show moreVibration data from mechanical systems carry important information that is useful for characterization and diagnosis. Standard approaches rely on continually streaming data at a fixed sampling frequency. For applications involving continuous monitoring, such as Structural Health Monitoring (SHM), such approaches result in high data volume and require powering sensors for prolonged duration. Furthermore, adequate spatial resolution, typically involves instrumenting structures with a large array of sensors. This research shows that applying Compressive Sensing (CS) can significantly reduce both the volume of data and number of sensors in vibration monitoring applications. Random sampling and the inherent sparsity of vibration signals in the frequency domain enables this reduction. Additionally, by exploiting the sparsity of mode shapes, CS can also enable efficient spatial reconstruction using fewer spatially distributed sensors than a traditional approach. CS can thereby reduce the cost and power requirement of sensing as well as streamline data storage and processing in monitoring applications. In well-instrumented structures, CS can enable continuous monitoring in case of sensor or computational failures. The scope of this research was to establish CS as a viable method for SHM with application to beam vibrations. Finite element based simulations demonstrated CS-based frequency recovery from free vibration response of simply supported, fixed-fixed and cantilever beams. Specifically, CS was used to detect shift in natural frequencies of vibration due to structural change using considerably less data than required by traditional sampling. Experimental results using a cantilever beam provided further insight into this approach. In the experimental study, impulse response of the beam was used to recover natural frequencies of vibration with CS. It was shown that CS could discern changes in natural frequencies under modified beam parameters. When the basis functions were modified to accommodate the effect of damping, the performance of CS-based recovery further improved. Effect of noise in CS-based frequency recovery was also studied. In addition to incorporating damping, formulating noise-handling as a part of the CS algorithm for beam vibrations facilitated detecting shift in frequencies from even fewer samples. In the spatial domain, CS was primarily developed to focus on image processing applications, where the signals and basis functions are very different from those required for mechanical beam vibrations. Therefore, it mandated reformulation of the CS problem that would handle related challenges and enable the reconstruction of spatial beam response using very few sensor data. Specifically, this research addresses CS-based reconstruction of deflection shape of beams with fixed boundary conditions. Presence of a fixed end makes hyperbolic terms indispensable in the basis, which in turn causes numerical inconsistencies. Two approaches are discussed to mitigate this problem. The first approach is to restrict the hyperbolic terms in the basis to lower frequencies to ensure well conditioning. The second, a more systematic approach, is to generate an augmented basis function that will combine harmonic and hyperbolic terms. At higher frequencies, the combined hyperbolic terms will limit each other's magnitude, thus ensuring boundedness. This research thus lays the foundation for formulating the CS problem for the field of mechanical vibrations. It presents fundamental studies and discusses open-ended challenges while implementing CS to this field that will pave way for further research.
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Date Issued: 2017
Identifier: CFE0007120, ucf:51954
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007120

Title: A study of Compressive Sensing for application to Structural Health Monitoring.
Creator: Ganesan, Vaahini, Das, Tuhin, Kauffman, Jeffrey, Raghavan, Seetha, University of Central Florida
Abstract / Description: One of the key areas that have attracted attention in the construction industry today is Structural Health Monitoring, more commonly known as SHM. It is a concept developed to monitor the quality and longevity of various engineering structures. The incorporation of such a system would help to continuously track health of the structure, indicate the occurrence/presence of any damage in real time and give us an idea of the number of useful years for the same. Being a recently conceived idea,...
Show moreOne of the key areas that have attracted attention in the construction industry today is Structural Health Monitoring, more commonly known as SHM. It is a concept developed to monitor the quality and longevity of various engineering structures. The incorporation of such a system would help to continuously track health of the structure, indicate the occurrence/presence of any damage in real time and give us an idea of the number of useful years for the same. Being a recently conceived idea, the state of the art technique in the field is straight forward - populating a given structure with sensors and extracting information from them. In this regard, instrumenting with too many sensors may be inefficient as this could lead to superfluous data that is expensive to capture and process.This research aims to explore an alternate SHM technique that optimizes the data acquisition process by eliminating the amount of redundant data that is sensed and uses this sufficient data to detect and locate the fault present in the structure. Efficient data acquisition requires a mechanism that senses just the necessary amount of data for detection and location of fault. For this reason Compressive Sensing (CS) is explored as a plausible idea. CS claims that signals can be reconstructed from what was previously believed to be incomplete information by Shannon's theorem, taking only a small amount of random and linear non - adaptive measurements. As responses of many physical systems contain a finite basis, CS exploits this feature and determines the sparse solution instead of the traditional least - squares type solution. As a first step, CS is demonstrated by successfully recovering the frequency components of a simple sinusoid. Next, the question of how CS compares with the conventional Fourier transform is analyzed. For this, recovery of temporal frequencies and signal reconstruction is performed using the same number of samples for both the approaches and the errors are compared. On the other hand, the FT error is gradually minimized to match that of CS by increasing the number of regularly placed samples. Once the advantages are established, feasibility of using CS to detect damage in a single degree of freedom system is tested under unforced and forced conditions. In the former scenario, damage is indicated when there is a change in natural frequency of vibration of the system after an impact. In the latter, the system is excited harmonically and damage is detected by a change in amplitude of the system's vibration. As systems in real world applications are predominantly multi-DOF, CS is tested on a 2-DOF system excited with a harmonic forcing. Here again, damage detection is achieved by observing the change in the amplitude of vibration of the system. In order to employ CS for detecting either a change in frequency or amplitude of vibration of a structure subjected to realistic forcing conditions, it would be prudent to explore the reconstruction of a signal which contains multiple frequencies. This is accomplished using CS on a chirp signal.Damage detection is clearly a spatio-temporal problem. Hence it is important to additionally explore the extension of CS to spatial reconstruction. For this reason, mode shape reconstruction of a beam with standard boundary conditions is performed and validated with standard/analytical results from literature. As the final step, the operation deflection shapes (ODS) are reconstructed for a simply supported beam using CS to establish that it is indeed a plausible approach for a less expensive SHM. While experimenting with the idea of spatio-temporal domain, the mode shape as well as the ODS of the given beam are examined under two conditions - undamaged and damaged. Damage in the beam is simulated as a decrease in the stiffness coefficient over a certain number of elements. Although the range of modes to be examined heavily depends on the structure in question, literature suggests that for most practical applications, lower modes are more dominant in indicating damage. For ODS on the other hand, damage is indicated by observing the shift in the recovered spatial frequencies and it is confirmed by the reconstructed response.
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Date Issued: 2014
Identifier: CFE0005334, ucf:50520
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005334

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

Title: Structural Identification through Monitoring, Modeling and Predictive Analysis under Uncertainty.
Creator: Gokce, Hasan, Catbas, Fikret, Chopra, Manoj, Mackie, Kevin, Yun, Hae-Bum, DeMara, Ronald, University of Central Florida
Abstract / Description: Bridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior...
Show moreBridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior based on structural health monitoring (SHM) data. In a recent study by the ASCE St-Id Committee, St-Id framework is given in six steps, including modeling, experimentation and ultimately decision making for estimating the performance and vulnerability of structural systems reliably through the improved simulations using monitoring data. In some St-Id applications, there can be challenges and considerations related to this six-step framework. For instance not all of the steps can be employed; thereby a subset of the six steps can be adapted for some cases based on the various limitations. In addition, each step has its own characteristics, challenges, and uncertainties due to the considerations such as time varying nature of civil structures, modeling and measurements. It is often discussed that even a calibrated model has limitations in fully representing an existing structure; therefore, a family of models may be well suited to represent the structure's response and performance in a probabilistic manner.The principle objective of this dissertation is to investigate nonparametric and parametric St-Id approaches by considering uncertainties coming from different sources to better assess the structural condition for decision making. In the first part of the dissertation, a nonparametric St-Id approach is employed without the use of an analytical model. The new methodology, which is successfully demonstrated on both lab and real-life structures, can identify and locate the damage by tracking correlation coefficients between strain time histories and can locate the damage from the generated correlation matrices of different strain time histories. This methodology is found to be load independent, computationally efficient, easy to use, especially for handling large amounts of monitoring data, and capable of identifying the effectiveness of the maintenance. In the second part, a parametric St-Id approach is introduced by developing a family of models using Monte Carlo simulations and finite element analyses to explore the uncertainty effects on performance predictions in terms of load rating and structural reliability. The family of models is developed from a parent model, which is calibrated using monitoring data. In this dissertation, the calibration is carried out using artificial neural networks (ANNs) and the approach and results are demonstrated on a laboratory structure and a real-life movable bridge, where predictive analyses are carried out for performance decrease due to deterioration, damage, and traffic increase over time. In addition, a long-span bridge is investigated using the same approach when the bridge is retrofitted. The family of models for these structures is employed to determine the component and system reliability, as well as the load rating, with a distribution that incorporates various uncertainties that were defined and characterized. It is observed that the uncertainties play a considerable role even when compared to calibrated model-based predictions for reliability and load rating, especially when the structure is complex, deteriorated and aged, and subjected to variable environmental and operational conditions. It is recommended that a family-of-models approach is suitable for structures that have less redundancy, high operational importance, are deteriorated, and are performing under close capacity and demand levels.
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Date Issued: 2012
Identifier: CFE0004232, ucf:48997
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004232

Title: STRUCTURAL CONDITION ASSESSMENT OF PRESTRESSED CONCRETE TRANSIT GUIDEWAYS.
Creator: Shmerling, Robert, Catbas, F. Necati, University of Central Florida
Abstract / Description: Objective condition assessment is essential to make better decisions for safety and serviceability of existing civil infrastructure systems. This study explores the condition of an existing transit guideway system that has been in service for thirty-five years. The structural system is composed of six-span continuous prestressed concrete bridge segments. The overall transit system incorporates a number of continuous bridges which share common design details, geometries, and loading conditions...
Show moreObjective condition assessment is essential to make better decisions for safety and serviceability of existing civil infrastructure systems. This study explores the condition of an existing transit guideway system that has been in service for thirty-five years. The structural system is composed of six-span continuous prestressed concrete bridge segments. The overall transit system incorporates a number of continuous bridges which share common design details, geometries, and loading conditions. The original analysis is based on certain simplifying assumptions such as rigid behavior over supports and simplified tendon/concrete/steel plate interaction. The current objective is to conduct a representative study for a more accurate understanding of the structural system and its behavior. The scope of the study is to generate finite element models (FEMs) to be used in static and dynamic parameter sensitivity studies, as well load rating and reliability analysis of the structure. The FEMs are used for eigenvalue analysis and simulations. Parameter sensitivity studies consider the effect of changing critical parameters, including material properties, prestress loss, and boundary and continuity conditions, on the static and dynamic structural response. Load ratings are developed using an American Association for State Highway Transportation Officials Load and Resistance Factor Rating (AASHTO LRFR) approach. The reliability of the structural system is evaluated based on the data obtained from various finite element models. Recommendations for experimental validation of the FEM are presented. This study is expected to provide information to make better decisions for operations, maintenance and safety requirements; to be a benchmark for future studies, to establish a procedure and methodology for structural condition assessment, and to contribute to the general research body of knowledge in condition assessment and structural health monitoring.
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Date Issued: 2005
Identifier: CFE0000658, ucf:46520
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0000658

Structural Health Monitoring (9)	+ -
Damage Detection (5)	+ -
Structural Identification (3)	+ -
SHM (2)	+ -
Structural health monitoring (2)	+ -

compressive sensing (2)	+ -
damage detection (2)	+ -
structural health monitoring (2)	+ -
AFRL Tank (1)	+ -
ARX (1)	+ -
Bridges (1)	+ -
COPV (1)	+ -
Change detection (1)	+ -
Composite Overwrapped Pressure Vessels (1)	+ -
Computer Vision (1)	+ -
Conceptual Indices (1)	+ -
Correlation (1)	+ -
Damage Identification (1)	+ -
Data Driven (1)	+ -
Experimental Modal Analysis (1)	+ -
Family of Models (1)	+ -
Fiber Optic Sensing (1)	+ -
Finite Element Model (1)	+ -
Identifying codes (1)	+ -
Influence Line (1)	+ -
L1 minimization (1)	+ -
Load Rating (1)	+ -
Long Span Bridge (1)	+ -
Modal Analysis (1)	+ -
Movable Bridge (1)	+ -

ETD Collection (12)	+ -
Honors Collection (1)	+ -

University of Central Florida (13)	+ -
Catbas, F. Necati (3)	+ -
Catbas, Fikret (3)	+ -
Catbas, Necati (3)	+ -
Chopra, Manoj (3)	+ -

Yun, Hae-Bum (3)	+ -
Das, Tuhin (2)	+ -
Ganesan, Vaahini (2)	+ -
Gul, Mustafa (2)	+ -
Mackie, Kevin (2)	+ -
Raghavan, Seetha (2)	+ -
Zaurin, Ricardo (2)	+ -
Atia, George (1)	+ -
CATBAS, F. NECATI (1)	+ -
DeMara, Ronald (1)	+ -
Gokce, Hasan (1)	+ -
Kauffman, Jeffrey (1)	+ -
Kauffman, Jeffrey L. (1)	+ -
Khuc, Tung (1)	+ -
Letizia, Luca (1)	+ -
Malekzadeh, Seyedmasoud (1)	+ -
Modesto, Arturo (1)	+ -
Moslehy, Faissal (1)	+ -
Oloufa, Amr (1)	+ -
Paul, Bryan (1)	+ -
Rahnavard, Nazanin (1)	+ -
Sarayanibafghi, Omid (1)	+ -
Shah, Mubarak (1)	+ -
Shmerling, Robert (1)	+ -
Tatari, Mehmet (1)	+ -

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