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Title: NARRATIVE BASED FEAR APPEALS: MANIPULATING GRAMMATICAL PERSON AND MESSAGE FRAME TO PROMOTE HPV AWARENESS AND RESPONSIBLE SEXUAL CONDUCT.
Creator: Spear, Jennifer, Miller, Ann, University of Central Florida
Abstract / Description: The utility of narrative as a persuasive mechanism has been increasingly investigated in recent years especially within the context of health behaviors. Although many studies have noted the effectiveness of narrative-based persuasive appeals, conceptual inconsistencies have made it difficult to determine what specific aspects of narrative messages lead to the most effective persuasive outcomes. In the present study, 145 female college students were randomly assigned to read one of four...
Show moreThe utility of narrative as a persuasive mechanism has been increasingly investigated in recent years especially within the context of health behaviors. Although many studies have noted the effectiveness of narrative-based persuasive appeals, conceptual inconsistencies have made it difficult to determine what specific aspects of narrative messages lead to the most effective persuasive outcomes. In the present study, 145 female college students were randomly assigned to read one of four narrative health messages about a female freshman college students experiences with the human papillomavirus (HPV). Two elements of the narrative message structure were manipulated: the message frame (gain framed vs. loss framed), and the grammatical person of the text (first-person vs. third-person).The messages were presented via the medium of an online blog. After reading a narrative participants responded to a brief questionnaire designed to measure perceptions of threat regarding HPV contraction, perceptions of efficacy regarding HPV prevention, and intentions to get the Gardasil vaccine. Participants exposed to loss framed messages reported higher levels of perceived threat (susceptibility and severity) than participants exposed to gain framed messages although participants in the gain framed message conditions reported higher levels of perceived self-efficacy. Significant correlations were also found between levels of reported character identification and the two threat variables. No effects were found for grammatical person.
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Date Issued: 2011
Identifier: CFE0003997, ucf:48673
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003997

Title: IMPROVING TRAFFIC SAFETY AND DRIVERS' BEHAVIOR IN REDUCED VISIBILITY CONDITIONS.
Creator: Hassan, Hany, Abde-Aty, Mohamed, University of Central Florida
Abstract / Description: This study is concerned with the safety risk of reduced visibility on roadways. Inclement weather events such as fog/smoke (FS), heavy rain (HR), high winds, etc, do affect every road by impacting pavement conditions, vehicle performance, visibility distance, and drivers' behavior. Moreover, they affect travel demand, traffic safety, and traffic flow characteristics. Visibility in particular is critical to the task of driving and reduction in visibility due FS or other weather events such as...
Show moreThis study is concerned with the safety risk of reduced visibility on roadways. Inclement weather events such as fog/smoke (FS), heavy rain (HR), high winds, etc, do affect every road by impacting pavement conditions, vehicle performance, visibility distance, and drivers' behavior. Moreover, they affect travel demand, traffic safety, and traffic flow characteristics. Visibility in particular is critical to the task of driving and reduction in visibility due FS or other weather events such as HR is a major factor that affects safety and proper traffic operation. A real-time measurement of visibility and understanding drivers' responses, when the visibility falls below certain acceptable level, may be helpful in reducing the chances of visibility-related crashes. In this regard, one way to improve safety under reduced visibility conditions (i.e., reduce the risk of visibility related crashes) is to improve drivers' behavior under such adverse weather conditions. Therefore, one of objectives of this research was to investigate the factors affecting drivers' stated behavior in adverse visibility conditions, and examine whether drivers rely on and follow advisory or warning messages displayed on portable changeable message signs (CMS) and/or variable speed limit (VSL) signs in different visibility, traffic conditions, and on two types of roadways; freeways and two-lane roads. The data used for the analyses were obtained from a self-reported questionnaire survey carried out among 566 drivers in Central Florida, USA. Several categorical data analysis techniques such as conditional distribution, odds' ratio, and Chi-Square tests were applied. In addition, two modeling approaches; bivariate and multivariate probit models were estimated. The results revealed that gender, age, road type, visibility condition, and familiarity with VSL signs were the significant factors affecting the likelihood of reducing speed following CMS/VSL instructions in reduced visibility conditions. Other objectives of this survey study were to determine the content of messages that would achieve the best perceived safety and drivers' compliance and to examine the best way to improve safety during these adverse visibility conditions. The results indicated that "Caution-fog ahead-reduce speed" was the best message and using CMS and VSL signs together was the best way to improve safety during such inclement weather situations. In addition, this research aimed to thoroughly examine drivers' responses under low visibility conditions and quantify the impacts and values of various factors found to be related to drivers' compliance and drivers' satisfaction with VSL and CMS instructions in different visibility and traffic conditions. To achieve these goals, Explanatory Factor Analysis (EFA) and Structural Equation Modeling (SEM) approaches were adopted. The results revealed that drivers' satisfaction with VSL/CMS was the most significant factor that positively affected drivers' compliance with advice or warning messages displayed on VSL/CMS signs under different fog conditions followed by driver factors. Moreover, it was found that roadway type affected drivers' compliance to VSL instructions under medium and heavy fog conditions. Furthermore, drivers' familiarity with VSL signs and driver factors were the significant factors affecting drivers' satisfaction with VSL/CMS advice under reduced visibility conditions. Based on the findings of the survey-based study, several recommendations are suggested as guidelines to improve drivers' behavior in such reduced visibility conditions by enhancing drivers' compliance with VSL/CMS instructions. Underground loop detectors (LDs) are the most common freeway traffic surveillance technologies used for various intelligent transportation system (ITS) applications such as travel time estimation and crash detection. Recently, the emphasis in freeway management has been shifting towards using LDs data to develop real-time crash-risk assessment models. Numerous studies have established statistical links between freeway crash risk and traffic flow characteristics. However, there is a lack of good understanding of the relationship between traffic flow variables (i.e. speed, volume and occupancy) and crashes that occur under reduced visibility (VR crashes). Thus, another objective of this research was to explore the occurrence of reduced visibility related (VR) crashes on freeways using real-time traffic surveillance data collected from loop detectors (LDs) and radar sensors. In addition, it examines the difference between VR crashes to those occurring at clear visibility conditions (CV crashes). To achieve these objectives, Random Forests (RF) and matched case-control logistic regression model were estimated. The results indicated that traffic flow variables leading to VR crashes are slightly different from those variables leading to CV crashes. It was found that, higher occupancy observed about half a mile between the nearest upstream and downstream stations increases the risk for both VR and CV crashes. Moreover, an increase of the average speed observed on the same half a mile increases the probability of VR crash. On the other hand, high speed variation coupled with lower average speed observed on the same half a mile increase the likelihood of CV crashes. Moreover, two issues that have not explicitly been addressed in prior studies are; (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors installed on Expressways and (2) which traffic data is advantageous for predicting VR crashes; LDs or AVIs. Thus, this research attempts to examine the relationships between VR crash risk and real-time traffic data collected from LDs installed on two Freeways in Central Florida (I-4 and I-95) and from AVI sensors installed on two Expressways (SR 408 and SR 417). Also, it investigates which data is better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression using the historical VR crashes, LDs and AVI data. Regarding models estimated based on LDs data, the average speed observed at the nearest downstream station along with the coefficient of variation in speed observed at the nearest upstream station, all at 5-10 minute prior to the crash time, were found to have significant effect on VR crash risk. However, for the model developed based on AVI data, the coefficient of variation in speed observed at the crash segment, at 5-10 minute prior to the crash time, affected the likelihood of VR crash occurrence. Argument concerning which traffic data (LDs or AVI) is better for predicting VR crashes is also provided and discussed.
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Date Issued: 2011
Identifier: CFE0003946, ucf:48693
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003946

Title: THE RELATIVE RECOVERABILITY OF DNA AND RNA PROFILES FROM FORENSICALLY RELEVANT BODY FLUID STAINS.
Creator: Parker, Charly, Ballantyne, Jack, University of Central Florida
Abstract / Description: Biological material (fluids or tissues) whether from the victim or suspect is often collected as forensic evidence, and methods to obtain and analyze the DNA found in that material have been well established. The type of body fluid (i.e. blood, saliva, semen, vaginal secretions, and menstrual blood) from which the DNA originated is also of interest, and messenger RNA typing provides a specific and sensitive means of body fluid identification. In order for mRNA profiling to be utilized in...
Show moreBiological material (fluids or tissues) whether from the victim or suspect is often collected as forensic evidence, and methods to obtain and analyze the DNA found in that material have been well established. The type of body fluid (i.e. blood, saliva, semen, vaginal secretions, and menstrual blood) from which the DNA originated is also of interest, and messenger RNA typing provides a specific and sensitive means of body fluid identification. In order for mRNA profiling to be utilized in routine forensic casework, RNA of sufficient quantity and quality must be obtained from biological fluid stains and the methods used for RNA analysis must be fully compatible with current DNA analysis methodologies. Several DNA/RNA co-extraction methods were evaluated based on the quantity and quality of DNA and RNA recovered and were also compared to standard non-co-extraction methods. The two most promising methods, the in-house developed NCFS co-extraction and the commercially available AllPrep DNA/RNA Mini kit, were then optimized by improving nucleic acid recovery and consistency of CE (capillary electrophoresis) detection results. The sensitivity of the two methods was also evaluated, and DNA and RNA profiles could be obtained for the lowest amount of blood (0.2 µL) and saliva and semen (1 µL) tested. Both extraction methods were found to be acceptable for use with forensic samples, and the ability to obtain full DNA profiles was not hindered by the co-extraction of RNA. It is generally believed that RNA is less stable than DNA which may prevent its use in forensic casework. However, the degradation rates of DNA and RNA in the same biological fluid stain have not been directly compared. To determine the relative stability of DNA and RNA, the optimized NCFS co-extraction protocol was used to isolate DNA and RNA from environmentally compromised stains. Dried blood, saliva, and semen stains and vaginal secretions swabs were incubated at set temperatures and outside for up to 1 year. Even at 56°C, DNA and RNA were both stable out to 1 year in the blood and semen stains, out to 3 months (DNA) and 1 year (RNA) in the saliva stains, and out to 6 months (DNA) and 3 months (RNA) in the vaginal secretions swabs. The recoverability of both nucleic acids was reduced when the samples were exposed to increased humidity, sunlight, and rain. In general, DNA and RNA stability was found to be similar with a loss in ability to obtain a DNA or RNA profile occurring at the same time point; however, there were instances where RNA body fluid markers were detected when a poor/no DNA profile was obtained, indicating that RNA in dried stains is sufficiently stable for mRNA body fluid typing to be used in forensic casework.
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Date Issued: 2011
Identifier: CFE0003596, ucf:48849
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003596

Title: EFFECTS OF 3D STEREOSCOPY, VISUO-SPATIAL WORKING MEMORY, AND PERCEPTIONS OF SIMULATION EXPERIENCE ON THE MEMORIZATION OF CONFUSABLE OBJECTS.
Creator: Keebler, Joseph, Jentsch, Florian, University of Central Florida
Abstract / Description: This dissertation investigated the impact of active stereoscopic 3-dimensional (3D) imagery equipment and individual differences in visuo-spatial working memory (VSWM) capacity on retention of a set of similar, novel objects (i.e., armored military vehicles). Seventy-one participants were assessed on their visuo-spatial working memory using the Visual Patterns Test (Della Sala, Gray, Baddeley, & Wilson, 1997). They were then assigned to one of four different conditions (3D high VSWM, 3D low...
Show moreThis dissertation investigated the impact of active stereoscopic 3-dimensional (3D) imagery equipment and individual differences in visuo-spatial working memory (VSWM) capacity on retention of a set of similar, novel objects (i.e., armored military vehicles). Seventy-one participants were assessed on their visuo-spatial working memory using the Visual Patterns Test (Della Sala, Gray, Baddeley, & Wilson, 1997). They were then assigned to one of four different conditions (3D high VSWM, 3D low VSWM, 2D high VSWM, 2D low VSWM) based upon their visuo-spatial working memory. Participants were then trained to identify military vehicles using a simulation that presented the training stimuli in one of two dimensionalities, i.e. two dimensional (2D) or active stereoscopic three-dimensional (3D). Testing consisted of a vehicle memory training assessment, which challenged participants to choose the correct components of each vehicle immediately after studying; a measure of retention for military vehicles which asked participants to categorize the alliance and identify previously studied vehicles; and a transfer measure using video footage of actual military vehicles. The latter measures depicted military vehicles in an array of combat situations, and participants were asked to decide on whether or not to shoot each vehicle, as well as identify the vehicles. Testing occurred immediately after training. The moderating, as well as main effects, of VSWM were assessed. The mediating/moderating effects of several experiential factors were measured as well, including: immersion, presence, engagement, flow state, and technology acceptance. Findings indicate that perceptions of the simulation experience and VSWM are strong positive predictors of performance, while 3D was not predictive, and in some instances, significantly worse than the 2D condition. These findings indicate that individual differences in visual memory and user experiences during the SBT both are predictive factors in memory tasks for confusable objects. The SBT designed in this study also led to robust prediction of training outcomes on the final transfer task.
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Date Issued: 2011
Identifier: CFE0003939, ucf:48702
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003939

Title: UNCERTAINTY, IDENTIFICATION, AND PRIVACY: EXPERIMENTS IN INDIVIDUAL DECISION-MAKING.
Creator: Rivenbark, David, Harrison, Glenn, University of Central Florida
Abstract / Description: The alleged privacy paradox states that individuals report high values for personal privacy, while at the same time they report behavior that contradicts a high privacy value. This is a misconception. Reported privacy behaviors are explained by asymmetric subjective beliefs. Beliefs may or may not be uncertain, and non-neutral attitudes towards uncertainty are not necessary to explain behavior. This research was conducted in three related parts. Part one presents an experiment in individual...
Show moreThe alleged privacy paradox states that individuals report high values for personal privacy, while at the same time they report behavior that contradicts a high privacy value. This is a misconception. Reported privacy behaviors are explained by asymmetric subjective beliefs. Beliefs may or may not be uncertain, and non-neutral attitudes towards uncertainty are not necessary to explain behavior. This research was conducted in three related parts. Part one presents an experiment in individual decision making under uncertainty. EllsbergÃ‚Â's canonical two-color choice problem was used to estimate attitudes towards uncertainty. Subjects believed bets on the color ball drawn from EllsbergÃ‚Â's ambiguous urn were equally likely to pay. Estimated attitudes towards uncertainty were insignificant. Subjective expected utility explained subjectsÃ‚Â' choices better than uncertainty aversion and the uncertain priors model. A second treatment tested Vernon SmithÃ‚Â's conjecture that preferences in EllsbergÃ‚Â's problem would be unchanged when the ambiguous lottery is replaced by a compound objective lottery. The use of an objective compound lottery to induce uncertainty did not affect subjectsÃ‚Â' choices. The second part of this dissertation extended the concept of uncertainty to commodities where quality and accuracy of a quality report were potentially ambiguous. The uncertain priors model is naturally extended to allow for potentially different attitudes towards these two sources of uncertainty, quality and accuracy. As they relate to privacy, quality and accuracy of a quality report are seen as metaphors for online security and consumer trust in e-commerce, respectively. The results of parametric structural tests were mixed. Subjects made choices consistent with neutral attitudes towards uncertainty in both the quality and accuracy domains. However, allowing for uncertainty aversion in the quality domain and not the accuracy domain outperformed the alternative which only allowed for uncertainty aversion in the accuracy domain. Finally, part three integrated a public-goods game and punishment opportunities with the Becker-DeGroot-Marschak mechanism to elicit privacy values, replicating previously reported privacy behaviors. The procedures developed elicited punishment (consequence) beliefs and information confidentiality beliefs in the context of individual privacy decisions. Three contributions are made to the literature. First, by using cash rewards as a mechanism to map actions to consequences, the study eliminated hypothetical bias as a confounding behavioral factor which is pervasive in the privacy literature. Econometric results support the Ã‚Â"privacy paradoxÃ‚Â" at levels greater than 10 percent. Second, the roles of asymmetric beliefs and attitudes towards uncertainty were identified using parametric structural likelihood methods. Subjects were, in general, uncertainty neutral and believed Ã‚Â"badÃ‚Â" events were more likely to occur when their private information was not confidential. A third contribution is a partial test to determine which uncertain process, loss of privacy or the resolution of consequences, is of primary importance to individual decision-makers. Choices were consistent with uncertainty neutral preferences in both the privacy and consequences domains.
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Date Issued: 2010
Identifier: CFE0003251, ucf:48539
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003251

Title: Environmentalism and Environmental Constitutional Ballot Initiatives in Florida: The Elements of Support for Amendment One in 2014 in the Context of Current Environmental Attitudes.
Creator: Jones, Michael, Jacques, Peter, Knuckey, Jonathan, Jewett, Aubrey, University of Central Florida
Abstract / Description: Americans express support for (")the environment(") with environmental support cutting across political and demographic differences and cleavages. In the past 15 years, however, period effects, political sorting, and the emergence of a powerful anti-environmental movement have lessened the generalized levels of environmental support. Using the 2012 CCES survey, the expressed attitudes regarding multiple environmental issues found significant differences in levels of environmental support...
Show moreAmericans express support for (")the environment(") with environmental support cutting across political and demographic differences and cleavages. In the past 15 years, however, period effects, political sorting, and the emergence of a powerful anti-environmental movement have lessened the generalized levels of environmental support. Using the 2012 CCES survey, the expressed attitudes regarding multiple environmental issues found significant differences in levels of environmental support nationally by party, Tea Party attitudes, ideology, and certain demographic characteristics. For Floridians, the differences between the most pro-environmental respondents and the most anti-environmental are narrower; partisan identification itself is not significant in environmental attitudes; but ideology, Tea party support, and to a lesser degree, gender and race are associated in explaining variances in environmental attitudes. Voting decision behavior previously observed only for certain environmental issues appears to be influenced by multiple environmental positions. The significance of age on environmental attitudes remains perplexing with evidence for both younger and older respondents' support for environmentalism, as compared to the support expressed by persons aged 40-59. Support and opposition for a specific Florida constitutional ballot proposition on environmental land conservative acquisition reflect partisan and gender divides, and the impact of attitudes regarding an unpopular elected national official. Environmentalism appears to be further evidence of the (")Big Sort(") in American politics, increasingly likely to be used as an interparty wedge issue and for intraparty base mobilizations. The need for further research and the implications for environmental activists conclude this thesis.
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Date Issued: 2015
Identifier: CFE0005960, ucf:50795
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005960

Title: Visual Scanpath Training for Facial Affect Recognition in a Psychiatric Sample.
Creator: Chan, Chi, Bedwell, Jeffrey, Cassisi, Jeffrey, Sims, Valerie, University of Central Florida
Abstract / Description: Social cognition is essential for functional outcome and quality of life in psychiatric patients. Facial affect recognition (FAR), a domain of social cognition, is impaired in many patients with schizophrenia and bipolar disorder. There is evidence that abnormal visual scanpath patterns may underlie FAR deficits, and metacognitive factors may impact task performance. The present study aimed to develop a brief, individually-administered, computerized training program to normalize scanpath...
Show moreSocial cognition is essential for functional outcome and quality of life in psychiatric patients. Facial affect recognition (FAR), a domain of social cognition, is impaired in many patients with schizophrenia and bipolar disorder. There is evidence that abnormal visual scanpath patterns may underlie FAR deficits, and metacognitive factors may impact task performance. The present study aimed to develop a brief, individually-administered, computerized training program to normalize scanpath patterns in order to improve FAR in patient with a psychosis history or bipolar I disorder. The program was developed using scanpath data from 19 nonpsychiatric controls (NC) while they completed a FAR tasks that involved identification of mild or extreme intensity happy, sad, angry, and fearful faces, and a neutral expression. Patients were randomized to a waitlist (WG; n = 16) or training group (TG; n = 18). Both patient groups completed a baseline FAR task (T0), the training (or a repeated FAR task as a control for WG; T1), and a post-training FAR task (T2). Patients evaluated their own performance and eyetracking data were recorded. Results indicated that the patient groups did not differ from NC on FAR performance, metacognitive accuracy, or scanpath patterns at T0. TG was compliant with the training program and showed changes in scanpath patterns during T1, but returned to baseline scanpath patterns at T2. WG and TG did not differ at T2 on FAR performance, metacognitive accuracy, or scanpath patterns. Across both patient groups, FAR performance for mild intensity emotions were more sensitive to the effect of time than for extreme intensity emotions. Exploratory analysis showed that at baseline, greater severity of negative symptoms was associated with poorer metacognitive accuracy (i.e., accuracy in their evaluation of their performance). Limitations to the study and future directions are discussed.
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Date Issued: 2016
Identifier: CFE0006280, ucf:51613
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006280

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: Preliminary Validation of Handheld X-Ray Fluorescence (HHXRF) Spectrometry: Distinguishing Osseous and Dental Tissue from Non-Bone Material of Similar Chemical Composition.
Creator: Zimmerman, Heather, Schultz, John, Toyne, Jennifer, Sigman, Michael, University of Central Florida
Abstract / Description: Forensic anthropologists normally examine bone from a variety of medicolegal contexts. The skeletal remains may in some cases be highly fragmented or taphonomically modified, making it difficult to sort bone from non-bone material. In these cases, the forensic anthropologist may rely on microscopic or destructive chemical analyses to sort the material. However, these techniques are costly and time-intensive, prompting the use of nondestructive analytical methods in distinguishing bone and...
Show moreForensic anthropologists normally examine bone from a variety of medicolegal contexts. The skeletal remains may in some cases be highly fragmented or taphonomically modified, making it difficult to sort bone from non-bone material. In these cases, the forensic anthropologist may rely on microscopic or destructive chemical analyses to sort the material. However, these techniques are costly and time-intensive, prompting the use of nondestructive analytical methods in distinguishing bone and teeth from non-bone materials in a limited number of cases. The proposed analytical techniques are limited in that they rely on an examination of the major elements in the material, and do not sort out all materials with a similar chemical composition to bone/teeth. To date, no methods have been proposed for the use of handheld X-ray fluorescence (HHXRF) spectrometry in discriminating human and nonhuman bone/teeth from non-bone materials. The purpose of this research was to develop a method for the use of HHXRF spectrometry in forensic anthropology specifically related to distinguishing human and nonhuman bone and teeth from non-bone materials of a similar chemical composition using multivariate statistical analyses: principal components analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and hierarchical cluster analysis (HCA). This was accomplished in two phases. Phase 1 consisted of a Reliability Test and involved sampling a single human long bone in thirty locations. Multiple spectra were collected at each location to examine the reliability of the instrument in detecting the elements both within a single site and between multiple sites. The results of the Reliability Test indicated that HHXRF consistently detected the major and minor elements found on the surface of a human bone. These results were used for Phase 2, designated the Accuracy Test, which involved analyzing a set of materials compiled from the literature to test the accuracy of the technique in discriminating bone (human and nonhuman) and non-bone samples (other biological and non-biological). The results of the Accuracy Test indicate that osseous and dental tissue can be distinguished from non-bone material of similar chemical composition with a high degree of accuracy (94%) when data is collected from several locations on a sample and analyzed separately during multivariate statistical analyses. Overall, it was not possible to discriminate rock apatite and synthetic hydroxyapatite (synthetic bone) from bone. However, this technique successfully discriminated other non-bone materials that are chemically similar to bone, such as ivory and octocoral, which previous methods focusing on only a comparison of Ca/P ratios were unable to distinguish from bone.
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Date Issued: 2013
Identifier: CFE0004801, ucf:49736
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004801

Title: Consumer Engagement in Travel-related Social Media.
Creator: Li, Xu, Wang, Youcheng, Robinson, Edward, Kwun, David, Nusair, Khaldoon, He, Xin, University of Central Florida
Abstract / Description: The term of (")consumer engagement(") is extensively used in the digital era. It is believed that engaged consumers play an important role in products/services referral and recommendation, new product/service development and experience/value co-creation. Although the notion of consumer engagement sounds compelling, it is not fully developed in theory. Different interpretations coexist, resulting in confusion and misuse of the concept. This study attempts to define consumer engagement and...
Show moreThe term of (")consumer engagement(") is extensively used in the digital era. It is believed that engaged consumers play an important role in products/services referral and recommendation, new product/service development and experience/value co-creation. Although the notion of consumer engagement sounds compelling, it is not fully developed in theory. Different interpretations coexist, resulting in confusion and misuse of the concept. This study attempts to define consumer engagement and develop a conceptual framework of consumer engagement, addressing antecedents of consumer engagement in online context. Moreover, some situational and social media usage-related factors are incorporated into the framework. A set of propositions are presented based on literature review and the conceptual framework to illustrate the relationship between consumer engagement and related factors. To provide empirical evidence for the conceptual model, an online survey is conducted. Participants complete the self-administered survey by answering questions concerning their online experience with the travel-related social media website they visit most. Two-step structural equation modeling is employed to analyze the data. The results show that both community experience and community identification have significant and positive relationship with consumer engagement. Community experience is also a strong predictor of community identification. Attitude toward using social media and travel involvement influence the relationship between consumer engagement and its antecedents.With focus on the interactive and experiential nature of consumer engagement, this study expands current understanding of consumer engagement and provides insights for hospitality and tourism businesses regarding how to engage consumers through travel-related social media.
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Date Issued: 2013
Identifier: CFE0004878, ucf:49657
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004878

Title: Taming Wild Faces: Web-Scale, Open-Universe Face Identification in Still and Video Imagery.
Creator: Ortiz, Enrique, Shah, Mubarak, Sukthankar, Rahul, Da Vitoria Lobo, Niels, Wang, Jun, Li, Xin, University of Central Florida
Abstract / Description: With the increasing pervasiveness of digital cameras, the Internet, and social networking, there is a growing need to catalog and analyze large collections of photos and videos. In this dissertation, we explore unconstrained still-image and video-based face recognition in real-world scenarios, e.g. social photo sharing and movie trailers, where people of interest are recognized and all others are ignored. In such a scenario, we must obtain high precision in recognizing the known identities,...
Show moreWith the increasing pervasiveness of digital cameras, the Internet, and social networking, there is a growing need to catalog and analyze large collections of photos and videos. In this dissertation, we explore unconstrained still-image and video-based face recognition in real-world scenarios, e.g. social photo sharing and movie trailers, where people of interest are recognized and all others are ignored. In such a scenario, we must obtain high precision in recognizing the known identities, while accurately rejecting those of no interest.Recent advancements in face recognition research has seen Sparse Representation-based Classification (SRC) advance to the forefront of competing methods. However, its drawbacks, slow speed and sensitivity to variations in pose, illumination, and occlusion, have hindered its wide-spread applicability. The contributions of this dissertation are three-fold: 1. For still-image data, we propose a novel Linearly Approximated Sparse Representation-based Classification (LASRC) algorithm that uses linear regression to perform sample selection for l1-minimization, thus harnessing the speed of least-squares and the robustness of SRC. On our large dataset collected from Facebook, LASRC performs equally to standard SRC with a speedup of 100-250x.2. For video, applying the popular l1-minimization for face recognition on a frame-by-frame basis is prohibitively expensive computationally, so we propose a new algorithm Mean Sequence SRC (MSSRC) that performs video face recognition using a joint optimization leveraging all of the available video data and employing the knowledge that the face track frames belong to the same individual. Employing MSSRC results in a speedup of 5x on average over SRC on a frame-by-frame basis.3. Finally, we make the observation that MSSRC sometimes assigns inconsistent identities to the same individual in a scene that could be corrected based on their visual similarity. Therefore, we construct a probabilistic affinity graph combining appearance and co-occurrence similarities to model the relationship between face tracks in a video. Using this relationship graph, we employ random walk analysis to propagate strong class predictions among similar face tracks, while dampening weak predictions. Our method results in a performance gain of 15.8% in average precision over using MSSRC alone.
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Date Issued: 2014
Identifier: CFE0005536, ucf:50313
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005536

Title: The Phenomenological Experience of Narrative Transportation.
Creator: Buchanan, William, Fiore, Stephen, Weger, Harry, Miller, Ann, University of Central Florida
Abstract / Description: Previous research has attempted to identify consequences of mental transportation into narrative worlds. While scales have been developed and validated to measure readers' levels of transportation, the objective quantification has left researchers at a descriptive disadvantage for the full range of qualitative responses to this phenomenon. This study presents a qualitative method of inquiry designed to get at the experience of narrative transportation as it is lived: the phenomenological...
Show morePrevious research has attempted to identify consequences of mental transportation into narrative worlds. While scales have been developed and validated to measure readers' levels of transportation, the objective quantification has left researchers at a descriptive disadvantage for the full range of qualitative responses to this phenomenon. This study presents a qualitative method of inquiry designed to get at the experience of narrative transportation as it is lived: the phenomenological interview. Interview transcripts were inductively analyzed for common themes that indicate intersubjective features of narrative experience. Four main themes were identified, which were composed of 22 base-level experiences reported by participants. These findings corroborated the extant literature and provided a nuanced understanding of the phenomenon as it is lived.
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Date Issued: 2013
Identifier: CFE0004657, ucf:49883
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004657

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: 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: Improving the performance of data-intensive computing on Cloud platforms.
Creator: Dai, Wei, Bassiouni, Mostafa, Zou, Changchun, Wang, Jun, Lin, Mingjie, Bai, Yuanli, University of Central Florida
Abstract / Description: Big Data such as Terabyte and Petabyte datasets are rapidly becoming the new norm for various organizations across a wide range of industries. The widespread data-intensive computing needs have inspired innovations in parallel and distributed computing, which has been the effective way to tackle massive computing workload for decades. One significant example is MapReduce, which is a programming model for expressing distributed computations on huge datasets, and an execution framework for data...
Show moreBig Data such as Terabyte and Petabyte datasets are rapidly becoming the new norm for various organizations across a wide range of industries. The widespread data-intensive computing needs have inspired innovations in parallel and distributed computing, which has been the effective way to tackle massive computing workload for decades. One significant example is MapReduce, which is a programming model for expressing distributed computations on huge datasets, and an execution framework for data-intensive computing on commodity clusters as well. Since it was originally proposed by Google, MapReduce has become the most popular technology for data-intensive computing. While Google owns its proprietary implementation of MapReduce, an open source implementation called Hadoop has gained wide adoption in the rest of the world. The combination of Hadoop and Cloud platforms has made data-intensive computing much more accessible and affordable than ever before.This dissertation addresses the performance issue of data-intensive computing on Cloud platforms from three different aspects: task assignment, replica placement, and straggler identification. Both task assignment and replica placement are subjects closely related to load balancing, which is one of the key issues that can significantly affect the performance of parallel and distributed applications. While task assignment schemes strive to balance data processing load among cluster nodes to achieve minimum job completion time, replica placement policies aim to assign block replicas to cluster nodes according to their processing capabilities to exploit data locality to the maximum extent. Straggler identification is also one of the crucial issues data-intensive computing has to deal with, as the overall performance of parallel and distributed applications is often determined by the node with the lowest performance. The results of extensive evaluation tests confirm that the schemes/policies proposed in this dissertation can improve the performance of data-intensive applications running on Cloud platforms.
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Date Issued: 2017
Identifier: CFE0006731, ucf:51896
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006731

Title: Understanding images and videos using context.
Creator: Vaca Castano, Gonzalo, Da Vitoria Lobo, Niels, Shah, Mubarak, Mikhael, Wasfy, Jones, W Linwood, Wiegand, Rudolf, University of Central Florida
Abstract / Description: In computer vision, context refers to any information that may influence how visual media are understood.(&)nbsp; Traditionally, researchers have studied the influence of several sources of context in relation to the object detection problem in images. In this dissertation, we present a multifaceted review of the problem of context.(&)nbsp; Context is analyzed as a source of improvement in the object detection problem, not only in images but also in videos. In the case of images, we also...
Show moreIn computer vision, context refers to any information that may influence how visual media are understood.(&)nbsp; Traditionally, researchers have studied the influence of several sources of context in relation to the object detection problem in images. In this dissertation, we present a multifaceted review of the problem of context.(&)nbsp; Context is analyzed as a source of improvement in the object detection problem, not only in images but also in videos. In the case of images, we also investigate the influence of the semantic context, determined by objects, relationships, locations, and global composition, to achieve a general understanding of the image content as a whole. In our research, we also attempt to solve the related problem of finding the context associated with visual media. Given a set of visual elements (images), we want to extract the context that can be commonly associated with these images in order to remove ambiguity. The first part of this dissertation concentrates on achieving image understanding using semantic context.(&)nbsp; In spite of the recent success in tasks such as image classi?cation, object detection, image segmentation, and the progress on scene understanding, researchers still lack clarity about computer comprehension of the content of the image as a whole. Hence, we propose a Top-Down Visual Tree (TDVT) image representation that allows the encoding of the content of the image as a hierarchy of objects capturing their importance, co-occurrences, and type of relations. A novel Top-Down Tree LSTM network is presented to learn about the image composition from the training images and their TDVT representations. Given a test image, our algorithm detects objects and determine the hierarchical structure that they form, encoded as a TDVT representation of the image.A single image could have multiple interpretations that may lead to ambiguity about the intentionality of an image.(&)nbsp; What if instead of having only a single image to be interpreted, we have multiple images that represent the same topic. The second part of this dissertation covers how to extract the context information shared by multiple images. We present a method to determine the topic that these images represent. We accomplish this task by transferring tags from an image retrieval database, and by performing operations in the textual space of these tags. As an application, we also present a new image retrieval method that uses multiple images as input. Unlike earlier works that focus either on using just a single query image or using multiple query images with views of the same instance, the new image search paradigm retrieves images based on the underlying concepts that the input images represent.Finally, in the third part of this dissertation, we analyze the influence of context in videos. In this case, the temporal context is utilized to improve scene identification and object detection. We focus on egocentric videos, where agents require some time to change from one location to another. Therefore, we propose a Conditional Random Field (CRF) formulation, which penalizes short-term changes of the scene identity to improve the scene identity accuracy.(&)nbsp; We also show how to improve the object detection outcome by re-scoring the results based on the scene identity of the tested frame. We present a Support Vector Regression (SVR) formulation in the case that explicit knowledge of the scene identity is available during training time. In the case that explicit scene labeling is not available, we propose an LSTM formulation that considers the general appearance of the frame to re-score the object detectors.
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Date Issued: 2017
Identifier: CFE0006922, ucf:51703
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006922

Title: Ok, Ladies, Now Let's Get Information: Recognizing Moments of Rhetorical Identification in Beyonc(&)#233;'s Digital Activism.
Creator: Arban, Garrett, Jones, Natasha, Vie, Stephanie, Wheeler, Stephanie, University of Central Florida
Abstract / Description: This research seeks to understand how activists are encouraging audiences to identify with their work in digital spaces through a case study of Beyonc(&)#233; Knowles-Carter's activism. The current scholarship surrounding digital activism is extensive and has offered a detailed look at individual tools used in activist movements, but there is a lack of research that recognizes the complex network of tools that are often used by an activist or activist group. To address this gap in the...
Show moreThis research seeks to understand how activists are encouraging audiences to identify with their work in digital spaces through a case study of Beyonc(&)#233; Knowles-Carter's activism. The current scholarship surrounding digital activism is extensive and has offered a detailed look at individual tools used in activist movements, but there is a lack of research that recognizes the complex network of tools that are often used by an activist or activist group. To address this gap in the research, this thesis offers an analysis of three specific activist tools used by Beyonc(&)#233; to encourage her fans and other audiences to identify with and participate in her activism. This study investigates the methods Beyonc(&)#233; employs to get her multiple audiences informed and engaged through an analysis of her activist blog, the (")Formation(") music video, and her live performance during the 2016 Super Bowl halftime show. Specifically, the purpose of this study is to assess, from a rhetorical standpoint, how Beyonc(&)#233; is inviting her audiences to respond and become engaged.The analysis of these three activist tools utilizes qualitative data analysis, focusing on Burke's (1969) concept of rhetorical identification to understand how her activist messages are presented across mediums. To expand on the findings of this analysis, a reception study on Beyonc(&)#233;'s (")Formation(") music video and 2016 Super Bowl performance was conducted to gauge the success of her rhetorical methods. The findings of this study recognize the need to continue looking at the multiple tools used by activists to understand the complexity of their rhetorical work online. This study also provides methods for analyzing the intertextual nature of digital activism so that further research can be done. While this study begins to address the gap in the current scholarship, more research needs to be done to study the current rhetorical practices of digital activists.
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Date Issued: 2017
Identifier: CFE0006557, ucf:51344
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006557

Title: Examining the Role of Music Streaming Motives, Social Identification, and Technological Engagement in Digital Music Streaming Service Use.
Creator: Bolduc, Heidi, Kinnally, William, Neuberger, Lindsay, Rubenking, Bridget, University of Central Florida
Abstract / Description: According to the Nielsen Music 360 Research Report, 67% of all music consumers in the United States used digital music streaming services to listen, discover, and share music online in 2014 (The Nielsen Company, 2014). As such, communications scholars and music industry professionals are beginning to recognize the importance of understanding the factors that influence digital music listener behavior. Therefore, this study proposes an expanded theory of planned behavior model (TPB) by...
Show moreAccording to the Nielsen Music 360 Research Report, 67% of all music consumers in the United States used digital music streaming services to listen, discover, and share music online in 2014 (The Nielsen Company, 2014). As such, communications scholars and music industry professionals are beginning to recognize the importance of understanding the factors that influence digital music listener behavior. Therefore, this study proposes an expanded theory of planned behavior model (TPB) by incorporating music streaming motives, social identification, and technological engagement into the original TPB model framework in an effort to gain a better understanding of people's intentions to use digital music streaming services as well as the amount of time spent listening to them. Results suggest that both the original TPB and expanded TPB models can be successfully applied within the context of digital music streaming service use. Specifically, attitudes as well as convenience emerged as positive contributors to intention to use digital music streaming services, while entertainment along with social identification, technological engagement, and behavioral intention emerged as positive contributors to streaming behavior. Additionally, information seeking and pass time emerged as negative contributors to these two behavioral outcomes. However, adding these additional components only improved the overall ability of the expanded model to predict streaming behavior. Both models also explained a larger percentage of intention to use digital music streaming services as compared to total time spent listening. As a result, this study implies the practical importance of understanding the fundamental differences between what drives listener intentions to use digital music streaming services as compared to what drives the actual amount of time listeners spend using digital music streaming services.
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Date Issued: 2016
Identifier: CFE0006266, ucf:51037
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006266

Title: A SOCIAL COGNITIVE APPROACH TOWARDS UNDERSTANDING THE EFFECTS OF POPULAR POKER TELEVISION SHOWS ON COLLEGE STUDENTS.
Creator: Londo, Marc, Shaver, Dan, University of Central Florida
Abstract / Description: Tournament poker shows have become a leading ratings draw on American television. Since ESPN and the Travel Channel began airing their innovative poker shows in 2003, the game has reached a new following, particularly among college students. There are unique and psychologically significant factors that characterize the college population that make students particularly receptive to popular characterizations in media. This study investigates the potential exacerbating effect that these widely...
Show moreTournament poker shows have become a leading ratings draw on American television. Since ESPN and the Travel Channel began airing their innovative poker shows in 2003, the game has reached a new following, particularly among college students. There are unique and psychologically significant factors that characterize the college population that make students particularly receptive to popular characterizations in media. This study investigates the potential exacerbating effect that these widely popular poker television shows have on the gambling behavior of college students. 444 college students completed a survey designed to assess gratifications sought through media along with measures of attitudes, gambling behavior, and social systems. Using Social Cognitive Theory as a framework of influence, exposure to these shows – ranging from the individual student to the overall college environment – was assessed and evaluated. Results indicated that student gambling is strongly correlated to viewership of poker shows, particularly among younger students. This was especially seen among students who utilized the online gambling option. Gambling behavior of peers wasn't shown to be a strong influence for student gambling. However, excitement was shown to be a strong variable that should be looked at closer.
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Date Issued: 2006
Identifier: CFE0001087, ucf:46781
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0001087

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

identification (10)	+ -
Structural Health Monitoring (5)	+ -
Structural Identification (3)	+ -
System Identification (3)	+ -
rhetoric (3)	+ -

Body Fluid Identification (2)	+ -
Computer Vision (2)	+ -
Damage Detection (2)	+ -
Experimental Modal Analysis (2)	+ -
Forensic Science (2)	+ -
Identification (2)	+ -
narrative (2)	+ -
qualitative (2)	+ -
transportation (2)	+ -
"Big Sort" (1)	+ -
3D (1)	+ -
3D stereoscopy (1)	+ -
Academically Disengaged Students (1)	+ -
Activities of Daily Living (1)	+ -
Adam Lambert (1)	+ -
Alcohol Use Disorders Identification Test (1)	+ -
Alcohol-Related Negative Consequences (1)	+ -
American Idol (1)	+ -
Analysis (1)	+ -
Anthropology (1)	+ -
Applications (1)	+ -
Art (1)	+ -
At-Risk Students (1)	+ -
Bayesian inference (1)	+ -
Bias (1)	+ -

ETD Collection (42)	+ -
Honors Collection (5)	+ -

University of Central Florida (47)	+ -
Mackie, Kevin (4)	+ -
Yun, Hae-Bum (4)	+ -
Catbas, Fikret (3)	+ -
Miller, Ann (3)	+ -

Shah, Mubarak (3)	+ -
Wheeler, Stephanie (3)	+ -
Abdel-Aty, Mohamed (2)	+ -
Ballantyne, Jack (2)	+ -
Catbas, Necati (2)	+ -
Chopra, Manoj (2)	+ -
Da Vitoria Lobo, Niels (2)	+ -
Jentsch, Florian (2)	+ -
Lee, JaeYoung (2)	+ -
Nam, Boo Hyun (2)	+ -
Schultz, John (2)	+ -
Sims, Valerie (2)	+ -
Wang, Jun (2)	+ -
Wiegand, Rudolf (2)	+ -
Abde-Aty, Mohamed (1)	+ -
Al Amili, Samer (1)	+ -
Al Rumaithi, Ayad (1)	+ -
Ambrose, Maureen (1)	+ -
Applen, John (1)	+ -
Arban, Garrett (1)	+ -
Bai, Yuanli (1)	+ -
Baldwin, Lee (1)	+ -
Ballantyne, John (1)	+ -
Barmak, Katayun (1)	+ -
Bassiouni, Mostafa (1)	+ -

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