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Title: A NOVEL EXPERIMENTAL APPROACH USING A RECONFIGURABLE TEST SETUP FOR COMPLEX NONLINEAR DYNAMIC SYSTEMS.
Creator: Rank, Aaron, Yun, Hae-Bum, University of Central Florida
Abstract / Description: Experimental nonlinear dynamics is an important area of study in the modern engineering field, with engineering applications in structural dynamics, structural control, and structural health monitoring. As a result, the discipline has experienced a great influx of research efforts to develop a versatile and reliable experimental methodology. A technical challenge in many experimental studies is the procurement of a device that exhibits the desired nonlinear behavior. As a result, many...
Show moreExperimental nonlinear dynamics is an important area of study in the modern engineering field, with engineering applications in structural dynamics, structural control, and structural health monitoring. As a result, the discipline has experienced a great influx of research efforts to develop a versatile and reliable experimental methodology. A technical challenge in many experimental studies is the procurement of a device that exhibits the desired nonlinear behavior. As a result, many researchers have longed for a versatile, but accurate, testing methodology that has complete freedom to simulate a wide range of nonlinearities and stochastic behaviors. The objective of this study is to develop a reconfigurable test setup as a tool to be used in a wide range of nonlinear dynamic studies. The main components include a moving mass whose restoring force can accurately be controlled and reprogrammed (with software) based upon measured displacement and velocity readings at each time step. The device offers control over nonlinear characteristics and the equation of dynamic motion. The advantage of having such an experimental setup is the ability to simulate various types of nonlinearities with the same test setup. As a result, the data collected can be used to help validate nonlinear modeling, system identification, and stochastic analysis studies. A physical test apparatus was developed, and various mechanical, electrical, and programming calibrations were performed for reliable experimental studies. To display potential uses for the reconfigurable approach, examples are presented where the device has been used to create physical data for use in change detection and deterioration studies. In addition, a demonstration is presented of the device's ability to physically simulate a large-scale orifice viscous damper, devices commonly used for vibration mitigation in bridges and buildings. For a large-scale viscous damper, physical testing is required to ensure structural design properties. However, due to the large scale of the dampers, expensive dynamic loading tests can be carried out at a very limited number of facilities. Using the reconfigurable test setup, the dynamic signature of the large-scale viscous damper can accurately be simulated with pre-collected data. The development of a system capable of emulating the restoring force of a nonlinear device with software is a novel approach and requires further calibration for increased reliability and accuracy. A discussion regarding the challenges faced when developing the methodology is presented and possible solutions are recommended. The methodology introduced by this apparatus is very promising. The device is a valuable experimental tool for researchers and designers, allowing for physical data collection, modeling, analysis, and validation of a wide class of nonlinear phenomena that commonly occur in a wide variety of engineering applications.
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Date Issued: 2011
Identifier: CFE0003982, ucf:48654
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0003982

Title: A Decision Support Model for Autonomous Trucks Strategies.
Creator: Mohamed, Ahmad Saeid Ammar, Yun, Hae-Bum, Chopra, Manoj, Sallam, Amr, University of Central Florida
Abstract / Description: We examined the potential to improve the movement of freight using Truck PlatooningLane strategies on limited access highways in the State of Florida. In the First part of thisresearch, we investigated the potential benefits from dedicating one lane from existinglanes for autonomous trucks only. In this regard, a general framework tool was developedto evaluate and compare different measurements (e.g., travel tim and emissions) to betterassist decision makers to determine the most effective...
Show moreWe examined the potential to improve the movement of freight using Truck PlatooningLane strategies on limited access highways in the State of Florida. In the First part of thisresearch, we investigated the potential benefits from dedicating one lane from existinglanes for autonomous trucks only. In this regard, a general framework tool was developedto evaluate and compare different measurements (e.g., travel tim and emissions) to betterassist decision makers to determine the most effective freight transportation strategy.Additionally, the travel time, level of service and emissions on Florida Strategic IntermodalSystem (SIS) were systematically analyzed using a VISSIM and MOVES simulation todetermine if it can be improved. For the scenarios simulated in this investigation, the inputincluded different patterns with a variety of peak hour volumes, truck percentages, speeds,and number of lanes. Additionally, the various total values of the resultant travel time,emissions and level of service for each SIS corridor were determined and calculated usinga General Linear Model and then tabulated to reveal input patterns. The results showed thata truck platooning lane can significantly reduce the travel time and emissions of trucks. Inthe second part, we proposed using a The Analytic Hierarchy Process (AHP) method toevaluate the potential benefits of building a new lane for autonomous trucks. The AHPmethod was developed to include all possible measurements that can assist decision makersto select the best autonomous truck policy. The results of the AHP model showed that thesafety criterion was significantly the most influential perspective per experts' opinions. Theresults showed that experts were more concerned about safety and environmentalconsiderations than the initial cost associated with building a new lane.
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Date Issued: 2018
Identifier: CFE0007056, ucf:51983
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007056

Title: Failure Analysis of Impact-Damaged Metallic Poles Repaired With Fiber Reinforced Polymer Composites.
Creator: Slade, Robert, Mackie, Kevin, Yun, Hae-Bum, Gou, Jihua, University of Central Florida
Abstract / Description: Metallic utility poles, light poles, and mast arms are intermittently damaged by vehicle collision. In many cases the vehicular impact does not cause immediate failure of the structure, but induces localized damage that may result in failure under extreme service loadings or can promote degradation and corrosion within the damaged region. Replacement of these poles is costly and often involves prolonged lane closures, service interruption, and temporary loss of functionality. Therefore, an in...
Show moreMetallic utility poles, light poles, and mast arms are intermittently damaged by vehicle collision. In many cases the vehicular impact does not cause immediate failure of the structure, but induces localized damage that may result in failure under extreme service loadings or can promote degradation and corrosion within the damaged region. Replacement of these poles is costly and often involves prolonged lane closures, service interruption, and temporary loss of functionality. Therefore, an in situ repair of these structures is required.This thesis examines the failure modes of damaged metallic poles reinforced with externally-bonded fiber reinforced polymer (FRP) composites. Several FRP repair systems were selected for comparison, and a set of medium and full-scale tests were conducted to identify the critical failure modes. The material properties of each component of the repair were experimentally determined, and then combined into a numerical model capable of predicting global response.Four possible failure modes are discussed: yielding of the unreinforced substrate, tensile rupture of the FRP, compressive buckling of the FRP, and debonding of the FRP from the substrate. It was found that simple linear, bilinear, and trilinear stress-strain relationships accurately describe the response of the composite and substrate components, whereas a more complex bond-slip relationship is required to characterize debonding. These constitutive properties were then incorporated into MSC.Marc, a versatile nonlinear finite element program. The output of the FEM analysis showed good agreement with the results of the experimental bond-slip tests.
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Date Issued: 2012
Identifier: CFE0004262, ucf:49514
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004262

Title: Characterization of Dynamic Structures Using Parametric and Non-parametric System Identification Methods.
Creator: Al Rumaithi, Ayad, Yun, Hae-Bum, Catbas, Necati, Mackie, Kevin, University of Central Florida
Abstract / Description: The effects of soil-foundation-structure (SFS) interaction and extreme loading on structural behaviors are important issues instructural dynamics. System identification is an important technique to characterize linear and nonlinear dynamic structures.The identification methods are usually classified into the parametric and non-parametric approaches based on how to modeldynamic systems. The objective of this study is to characterize the dynamic behaviors of two realistic civil...
Show moreThe effects of soil-foundation-structure (SFS) interaction and extreme loading on structural behaviors are important issues instructural dynamics. System identification is an important technique to characterize linear and nonlinear dynamic structures.The identification methods are usually classified into the parametric and non-parametric approaches based on how to modeldynamic systems. The objective of this study is to characterize the dynamic behaviors of two realistic civil engineeringstructures in SFS configuration and subjected to impact loading by comparing different parametric and non-parametricidentification results. First, SFS building models were studied to investigate the effects of the foundation types on the structural behaviors underseismic excitation. Three foundation types were tested including the fixed, pile and box foundations on a hydraulic shaketable, and the dynamic responses of the SFS systems were measured with the instrumented sensing devices.Parametric modal analysis methods, including NExT-ERA, DSSI, and SSI, were studied as linear identification methodswhose governing equations were modeled based on linear equations of motion. NExT-ERA, DSSI, and SSI were used toanalyze earthquake-induced damage effects on the global behavior of the superstructures for different foundation types.MRFM was also studied to characterize the nonlinear behavior of the superstructure during the seismic events. MRFM is anonlinear non-parametric identification method which has advantages to characterized local nonlinear behaviors using theinterstory stiffness and damping phase diagrams. The major findings from the SFS study are: *The investigated modal analysis methods identified the linearized version of the model behavior. The change of globalstructural behavior induced by the seismic damage could be quantified through the modal parameter identification. Thefoundation types also affected the identification results due to different SFS interactions. The identification accuracy wasreduced as the nonlinear effects due to damage increased. *MRFM could characterize the nonlinear behavior of the interstory restoring forces. The localized damage could bequantified by measuring dissipated energy of each floor. The most severe damage in the superstructure was observed withthe fixed foundation. Second, the responses of a full-scale suspension bridge in a ship-bridge collision accident were analyzed to characterizethe dynamic properties of the bridge. Three parametric and non-parametric identification methods, NExT-ERA, PCA andICA were used to process the bridge response data to evaluate the performance of mode decomposition of these methodsfor traffic, no-traffic, and collision loading conditions. The PCA and ICA identification results were compared with those ofNExT-ERA method for different excitation, response types, system damping and sensor spatial resolution. The major findings from the ship-bridge collision study include: *PCA was able to characterize the mode shapes and modal coordinates for velocity and displacement responses. Theresults using the acceleration were less accurate. The inter-channel correlation and sensor spatial resolution had significanteffects on the mode decomposition accuracy. *ICA showed the lowest performance in this mode decomposition study. It was observed that the excitation type andsystem characteristics significantly affected the ICA accuracy.
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Date Issued: 2014
Identifier: CFE0005567, ucf:50295
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005567

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: Monitoring for Underdetermined Underground Structures during Excavation Using Limited Sensor Data.
Creator: Mehdawi, Nader, Yun, Hae-Bum, Sallam, Amr, Chopra, Manoj, University of Central Florida
Abstract / Description: A realistic field monitoring application to evaluate close proximity tunneling effects of a new tunnel on an existing tunnel is presented. A blind source separation (BSS)-based monitoring framework was developed using sensor data collected from the existing tunnel while the new tunnel was excavated. The developed monitoring framework is particularly useful to analyze underdetermined systems due to insufficient sensor data for explicit input force-output deformation relations. The analysis...
Show moreA realistic field monitoring application to evaluate close proximity tunneling effects of a new tunnel on an existing tunnel is presented. A blind source separation (BSS)-based monitoring framework was developed using sensor data collected from the existing tunnel while the new tunnel was excavated. The developed monitoring framework is particularly useful to analyze underdetermined systems due to insufficient sensor data for explicit input force-output deformation relations. The analysis results show that the eigen-parameters obtained from the correlation matrix of raw sensor data can be used as excellent indicators to assess the tunnel structural behaviors during the excavation with powerful visualization capability of tunnel lining deformation. Since the presented methodology is data-driven and not limited to a specific sensor type, it can be employed in various proximity excavation monitoring applications.
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Date Issued: 2013
Identifier: CFE0004718, ucf:49834
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004718

Title: Characterization of Waste-To-Energy (WTE) Bottom and Fly Ashes in Cementitious Materials.
Creator: An, Jin Woo, Nam, Boo Hyun, Yun, Hae-Bum, Chopra, Manoj, An, Linan, University of Central Florida
Abstract / Description: Waste-to-Energy (WTE) ashes (or called as municipal solid waste incineration ashes) have been recycled in the areas of road bed, asphalt paving, and concrete products in many European and Asian countries. In those countries, recycling programs (including required physical properties and environmental criteria) of ash residue management have been developed so as to encourage and enforce the reuse for WTE ashes instead of landfill disposal. However, the U.S. has shown a lack of consistent and...
Show moreWaste-to-Energy (WTE) ashes (or called as municipal solid waste incineration ashes) have been recycled in the areas of road bed, asphalt paving, and concrete products in many European and Asian countries. In those countries, recycling programs (including required physical properties and environmental criteria) of ash residue management have been developed so as to encourage and enforce the reuse for WTE ashes instead of landfill disposal. However, the U.S. has shown a lack of consistent and effective management plans as well as environmental regulations for the use of WTE ashes. Many previous studies demonstrated the potential beneficial use of WTE ash as an engineering material with minimum environmental impacts. Due to persistent uncertainty of engineering properties and inconsistency in the Federal and State regulations in the U.S., the recycling of WTE ash has been hindered, and they are mostly disposed of in landfills. The goal of this study is to identify beneficial use of WTE ashes as construction materials; thus, the recycling program of WTE ashes will become more active in the U.S. One of potential applications for the WTE ashes can be cement-based materials because the ashes contain good chemical components such as calcium and silicon. Moreover, toxics (heavy metals) can be bound or encapsulated in cement matrix; thus, the leaching potential can be reduced. The specific objectives are: (1) to understand the current practice of the reuse of WTE ashes as construction materials, (2) to physically and chemically characterize WTE bottom and fly ashes, (3) to investigate the effects of WTE bottom and fly ashes in cementitious materials (e.g. cement paste and concrete) as replacement of either cement of fine aggregate with emphasis on cement hydration, and (4) to investigate the environmental impacts of WTE bottom ash on leaching when used in cement-based materials. Fundamental properties of MSWI bottom ash and fly ash were studied by conducting physical, microstructural, and chemical tests. Petrographic examinations, such as scanning electron microscopy (SEM), energy dispersive x-ray (EDX), and x-ray diffraction (XRD) were performed in order to identify chemical composition of the ash and to determine their contents. To evaluate the main side effect of ash when used in concrete, the creation of a network of bubbles due to the presence of aluminum, ashes and aluminum powder were submerged in high pH solution, and the evolution of hydrogen gas was measured. Efforts were made to investigate the influence of WTE ashes on engineering properties of cement paste and concrete specimens when part of Portland cement and fine aggregate are replaced with ground and sieved WTE ashes. Cement paste and concrete cylinders were cast with various amounts of mineral and fine aggregate additions, respectively, and their strength and durability were investigated. Subsequently, optimum mix proportioning of the WTE ashes was investigated when they are used in cement paste and concrete specimens. In addition, the leaching characteristics of major alkaline and trace elements from concrete containing varied amounts (10%-50%) of BA were investigated by Synthetic Precipitation Leaching Procedure (SPLP) batch testing.
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Date Issued: 2015
Identifier: CFE0006251, ucf:51070
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006251

Title: A study of holistic strategies for the recognition of characters in natural scene images.
Creator: Ali, Muhammad, Foroosh, Hassan, Hughes, Charles, Sukthankar, Gita, Wiegand, Rudolf, Yun, Hae-Bum, University of Central Florida
Abstract / Description: Recognition and understanding of text in scene images is an important and challenging task. The importance can be seen in the context of tasks such as assisted navigation for the blind, providing directions to driverless cars, e.g. Google car, etc. Other applications include automated document archival services, mining text from images, and so on. The challenge comes from a variety of factors, like variable typefaces, uncontrolled imaging conditions, and various sources of noise corrupting...
Show moreRecognition and understanding of text in scene images is an important and challenging task. The importance can be seen in the context of tasks such as assisted navigation for the blind, providing directions to driverless cars, e.g. Google car, etc. Other applications include automated document archival services, mining text from images, and so on. The challenge comes from a variety of factors, like variable typefaces, uncontrolled imaging conditions, and various sources of noise corrupting the captured images. In this work, we study and address the fundamental problem of recognition of characters extracted from natural scene images, and contribute three holistic strategies to deal with this challenging task. Scene text recognition (STR) has been a known problem in computer vision and pattern recognition community for over two decades, and is still an active area of research owing to the fact that the recognition performance has still got a lot of room for improvement. Recognition of characters lies at the heart of STR and is a crucial component for a reliable STR system. Most of the current methods heavily rely on discriminative power of local features, such as histograms of oriented gradient (HoG), scale invariant feature transform (SIFT), shape contexts (SC), geometric blur (GB), etc. One of the problems with such methods is that the local features are rasterized in an ad hoc manner to get a single vector for subsequent use in recognition. This rearrangement of features clearly perturbs the spatial correlations that may carry crucial information vis-(&)#224;-vis recognition. Moreover, such approaches, in general, do not take into account the rotational invariance property that often leads to failed recognition in cases where characters in scene images do not occur in upright position. To eliminate this local feature dependency and the associated problems, we propose the following three holistic solutions: The first one is based on modelling character images of a class as a 3-mode tensor and then factoring it into a set of rank-1 matrices and the associated mixing coefficients. Each set of rank-1 matrices spans the solution subspace of a specific image class and enables us to capture the required holistic signature for each character class along with the mixing coefficients associated with each character image. During recognition, we project each test image onto the candidate subspaces to derive its mixing coefficients, which are eventually used for final classification.The second approach we study in this work lets us form a novel holistic feature for character recognition based on active contour model, also known as snakes. Our feature vector is based on two variables, direction and distance, cumulatively traversed by each point as the initial circular contour evolves under the force field induced by the character image. The initial contour design in conjunction with cross-correlation based similarity metric enables us to account for rotational variance in the character image. Our third approach is based on modelling a 3-mode tensor via rotation of a single image. This is different from our tensor based approach described above in that we form the tensor using a single image instead of collecting a specific number of samples of a particular class. In this case, to generate a 3D image cube, we rotate an image through a predefined range of angles. This enables us to explicitly capture rotational variance and leads to better performance than various local approaches.Finally, as an application, we use our holistic model to recognize word images extracted from natural scenes. Here we first use our novel word segmentation method based on image seam analysis to split a scene word into individual character images. We then apply our holistic model to recognize individual letters and use a spell-checker module to get the final word prediction. Throughout our work, we employ popular scene text datasets, like Chars74K-Font, Chars74K-Image, SVT, and ICDAR03, which include synthetic and natural image sets, to test the performance of our strategies. We compare results of our recognition models with several baseline methods and show comparable or better performance than several local feature-based methods justifying thus the importance of holistic strategies.
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Date Issued: 2016
Identifier: CFE0006247, ucf:51076
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006247

Title: Data-Driven Simulation Modeling of Construction and Infrastructure Operations Using Process Knowledge Discovery.
Creator: Akhavian, Reza, Behzadan, Amir, Oloufa, Amr, Yun, Hae-Bum, Sukthankar, Gita, Zheng, Qipeng, University of Central Florida
Abstract / Description: Within the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and...
Show moreWithin the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and resource allocation. Compared to other industries, this is particularly important in construction and infrastructure projects due to the high resource costs and the societal impacts of these projects. Discrete event simulation (DES) is a decision making tool that can benefit the process of design, control, and management of construction operations. Despite recent advancements, most DES models used in construction are created during the early planning and design stage when the lack of factual information from the project prohibits the use of realistic data in simulation modeling. The resulting models, therefore, are often built using rigid (subjective) assumptions and design parameters (e.g. precedence logic, activity durations). In all such cases and in the absence of an inclusive methodology to incorporate real field data as the project evolves, modelers rely on information from previous projects (a.k.a. secondary data), expert judgments, and subjective assumptions to generate simulations to predict future performance. These and similar shortcomings have to a large extent limited the use of traditional DES tools to preliminary studies and long-term planning of construction projects.In the realm of the business process management, process mining as a relatively new research domain seeks to automatically discover a process model by observing activity records and extracting information about processes. The research presented in this Ph.D. Dissertation was in part inspired by the prospect of construction process mining using sensory data collected from field agents. This enabled the extraction of operational knowledge necessary to generate and maintain the fidelity of simulation models. A preliminary study was conducted to demonstrate the feasibility and applicability of data-driven knowledge-based simulation modeling with focus on data collection using wireless sensor network (WSN) and rule-based taxonomy of activities. The resulting knowledge-based simulation models performed very well in properly predicting key performance measures of real construction systems. Next, a pervasive mobile data collection and mining technique was adopted and an activity recognition framework for construction equipment and worker tasks was developed. Data was collected using smartphone accelerometers and gyroscopes from construction entities to generate significant statistical time- and frequency-domain features. The extracted features served as the input of different types of machine learning algorithms that were applied to various construction activities. The trained predictive algorithms were then used to extract activity durations and calculate probability distributions to be fused into corresponding DES models. Results indicated that the generated data-driven knowledge-based simulation models outperform static models created based upon engineering assumptions and estimations with regard to compatibility of performance measure outputs to reality.
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Date Issued: 2015
Identifier: CFE0006023, ucf:51014
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006023

Title: Spatiotemporal Graphs for Object Segmentation and Human Pose Estimation in Videos.
Creator: Zhang, Dong, Shah, Mubarak, Qi, GuoJun, Bagci, Ulas, Yun, Hae-Bum, University of Central Florida
Abstract / Description: Images and videos can be naturally represented by graphs, with spatial graphs for images and spatiotemporal graphs for videos. However, for different applications, there are usually different formulations of the graphs, and algorithms for each formulation have different complexities. Therefore, wisely formulating the problem to ensure an accurate and efficient solution is one of the core issues in Computer Vision research. We explore three problems in this domain to demonstrate how to...
Show moreImages and videos can be naturally represented by graphs, with spatial graphs for images and spatiotemporal graphs for videos. However, for different applications, there are usually different formulations of the graphs, and algorithms for each formulation have different complexities. Therefore, wisely formulating the problem to ensure an accurate and efficient solution is one of the core issues in Computer Vision research. We explore three problems in this domain to demonstrate how to formulate all of these problems in terms of spatiotemporal graphs and obtain good and efficient solutions.The first problem we explore is video object segmentation. The goal is to segment the primary moving objects in the videos. This problem is important for many applications, such as content based video retrieval, video summarization, activity understanding and targeted content replacement. In our framework, we use object proposals, which are object-like regions obtained by low-level visual cues. Each object proposal has an object-ness score associated with it, which indicates how likely this object proposal corresponds to an object. The problem is formulated as a directed acyclic graph, for which nodes represent the object proposals and edges represent the spatiotemporal relationship between nodes. A dynamic programming solution is employed to select one object proposal from each video frame, while ensuring their consistency throughout the video frames. Gaussian mixture models (GMMs) are used for modeling the background and foreground, and Markov Random Fields (MRFs) are employed to smooth the pixel-level segmentation.In the above spatiotemporal graph formulation, we consider the object segmentation in only single video. Next, we consider multiple videos and model the video co-segmentation problem as a spatiotemporal graph. The goal here is to simultaneously segment the moving objects from multiple videos and assign common objects the same labels. The problem is formulated as a regulated maximum clique problem using object proposals. The object proposals are tracked in adjacent frames to generate a pool of candidate tracklets. Then an undirected graph is built with the nodes corresponding to the tracklets from all the videos and edges representing the similarities between the tracklets. A modified Bron-Kerbosch Algorithm is applied to the graph in order to select the prominent objects contained in these videos, hence relate the segmentation of each object in different videos.In online and surveillance videos, the most important object class is the human. In contrast to generic video object segmentation and co-segmentation, specific knowledge about humans, which is defined by a pose (i.e. human skeleton), can be employed to help the segmentation and tracking of people in the videos. We formulate the problem of human pose estimation in videos using the spatiotemporal graph. In this formulation, the nodes represent different body parts in the video frames and edges represent the spatiotemporal relationship between body parts in adjacent frames. The graph is carefully designed to ensure an exact and efficient solution. The overall objective for the new formulation is to remove the simple cycles from the traditional graph-based formulations. Dynamic programming is employed in different stages in the method to select the best tracklets and human pose configurations
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Date Issued: 2016
Identifier: CFE0006429, ucf:51488
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006429

Title: Weighting Policies for Robust Unsupervised Ensemble Learning.
Creator: Unlu, Ramazan, Xanthopoulos, Petros, Zheng, Qipeng, Rabelo, Luis, Yun, Hae-Bum, University of Central Florida
Abstract / Description: The unsupervised ensemble learning, or consensus clustering, consists of finding the optimal com- bination strategy of individual partitions that is robust in comparison to the selection of an algorithmic clustering pool. Despite its strong properties, this approach assigns the same weight to the contribution of each clustering to the final solution. We propose a weighting policy for this problem that is based on internal clustering quality measures and compare against other modern approaches...
Show moreThe unsupervised ensemble learning, or consensus clustering, consists of finding the optimal com- bination strategy of individual partitions that is robust in comparison to the selection of an algorithmic clustering pool. Despite its strong properties, this approach assigns the same weight to the contribution of each clustering to the final solution. We propose a weighting policy for this problem that is based on internal clustering quality measures and compare against other modern approaches. Results on publicly available datasets show that weights can significantly improve the accuracy performance while retaining the robust properties. Since the issue of determining an appropriate number of clusters, which is a primary input for many clustering methods is one of the significant challenges, we have used the same methodology to predict correct or the most suitable number of clusters as well. Among various methods, using internal validity indexes in conjunction with a suitable algorithm is one of the most popular way to determine the appropriate number of cluster. Thus, we use weighted consensus clustering along with four different indexes which are Silhouette (SH), Calinski-Harabasz (CH), Davies-Bouldin (DB), and Consensus (CI) indexes. Our experiment indicates that weighted consensus clustering together with chosen indexes is a useful method to determine right or the most appropriate number of clusters in comparison to individual clustering methods (e.g., k-means) and consensus clustering. Lastly, to decrease the variance of proposed weighted consensus clustering, we borrow the idea of Markowitz portfolio theory and implement its core idea to clustering domain. We aim to optimize the combination of individual clustering methods to minimize the variance of clustering accuracy. This is a new weighting policy to produce partition with a lower variance which might be crucial for a decision maker. Our study shows that using the idea of Markowitz portfolio theory will create a partition with a less variation in comparison to traditional consensus clustering and proposed weighted consensus clustering.
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Date Issued: 2017
Identifier: CFE0006813, ucf:51786
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006813

Title: Load Estimation, Structural Identification and Human Comfort Assessment of Flexible Structures.
Creator: Celik, Ozan, Catbas, Necati, Yun, Hae-Bum, Makris, Nicos, Kauffman, Jeffrey L., University of Central Florida
Abstract / Description: Stadiums, pedestrian bridges, dance floors, and concert halls are distinct from other civil engineering structures due to several challenges in their design and dynamic behavior. These challenges originate from the flexible inherent nature of these structures coupled with human interactions in the form of loading. The investigations in past literature on this topic clearly state that the design of flexible structures can be improved with better load modeling strategies acquired with reliable...
Show moreStadiums, pedestrian bridges, dance floors, and concert halls are distinct from other civil engineering structures due to several challenges in their design and dynamic behavior. These challenges originate from the flexible inherent nature of these structures coupled with human interactions in the form of loading. The investigations in past literature on this topic clearly state that the design of flexible structures can be improved with better load modeling strategies acquired with reliable load quantification, a deeper understanding of structural response, generation of simple and efficient human-structure interaction models and new measurement and assessment criteria for acceptable vibration levels. In contribution to these possible improvements, this dissertation taps into three specific areas: the load quantification of lively individuals or crowds, the structural identification under non-stationary and narrowband disturbances and the measurement of excessive vibration levels for human comfort. For load quantification, a computer vision based approach capable of tracking both individual and crowd motion is used. For structural identification, a noise-assisted Multivariate Empirical Mode Decomposition (MEMD) algorithm is incorporated into the operational modal analysis. The measurement of excessive vibration levels and the assessment of human comfort are accomplished through computer vision based human and object tracking, which provides a more convenient means for measurement and computation. All the proposed methods are tested in the laboratory environment utilizing a grandstand simulator and in the field on a pedestrian bridge and on a football stadium. Findings and interpretations from the experimental results are presented. The dissertation is concluded by highlighting the critical findings and the possible future work that may be conducted.
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Date Issued: 2017
Identifier: CFE0006863, ucf:51752
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006863

Title: Weakly Labeled Action Recognition and Detection.
Creator: Sultani, Waqas, Shah, Mubarak, Bagci, Ulas, Qi, GuoJun, Yun, Hae-Bum, University of Central Florida
Abstract / Description: Research in human action recognition strives to develop increasingly generalized methods thatare robust to intra-class variability and inter-class ambiguity. Recent years have seen tremendousstrides in improving recognition accuracy on ever larger and complex benchmark datasets, comprisingrealistic actions (")in the wild(") videos. Unfortunately, the all-encompassing, dense, globalrepresentations that bring about such improvements often benefit from the inherent characteristics,specific to...
Show moreResearch in human action recognition strives to develop increasingly generalized methods thatare robust to intra-class variability and inter-class ambiguity. Recent years have seen tremendousstrides in improving recognition accuracy on ever larger and complex benchmark datasets, comprisingrealistic actions (")in the wild(") videos. Unfortunately, the all-encompassing, dense, globalrepresentations that bring about such improvements often benefit from the inherent characteristics,specific to datasets and classes, that do not necessarily reflect knowledge about the entity to berecognized. This results in specific models that perform well within datasets but generalize poorly.Furthermore, training of supervised action recognition and detection methods need several precisespatio-temporal manual annotations to achieve good recognition and detection accuracy. For instance,current deep learning architectures require millions of accurately annotated videos to learnrobust action classifiers. However, these annotations are quite difficult to achieve.In the first part of this dissertation, we explore the reasons for poor classifier performance whentested on novel datasets, and quantify the effect of scene backgrounds on action representationsand recognition. We attempt to address the problem of recognizing human actions while trainingand testing on distinct datasets when test videos are neither labeled nor available during training. Inthis scenario, learning of a joint vocabulary, or domain transfer techniques are not applicable. Weperform different types of partitioning of the GIST feature space for several datasets and computemeasures of background scene complexity, as well as, for the extent to which scenes are helpfulin action classification. We then propose a new process to obtain a measure of confidence in eachpixel of the video being a foreground region using motion, appearance, and saliency together in a3D-Markov Random Field (MRF) based framework. We also propose multiple ways to exploit theforeground confidence: to improve bag-of-words vocabulary, histogram representation of a video,and a novel histogram decomposition based representation and kernel.iiiThe above-mentioned work provides probability of each pixel being belonging to the actor, however,it does not give the precise spatio-temporal location of the actor. Furthermore, above frameworkwould require precise spatio-temporal manual annotations to train an action detector. However,manual annotations in videos are laborious, require several annotators and contain humanbiases. Therefore, in the second part of this dissertation, we propose a weakly labeled approachto automatically obtain spatio-temporal annotations of actors in action videos. We first obtain alarge number of action proposals in each video. To capture a few most representative action proposalsin each video and evade processing thousands of them, we rank them using optical flow andsaliency in a 3D-MRF based framework and select a few proposals using MAP based proposal subsetselection method. We demonstrate that this ranking preserves the high-quality action proposals.Several such proposals are generated for each video of the same action. Our next challenge is toiteratively select one proposal from each video so that all proposals are globally consistent. Weformulate this as Generalized Maximum Clique Graph problem (GMCP) using shape, global andfine-grained similarity of proposals across the videos. The output of our method is the most actionrepresentative proposals from each video. Using our method can also annotate multiple instancesof the same action in a video can also be annotated. Moreover, action detection experiments usingannotations obtained by our method and several baselines demonstrate the superiority of ourapproach.The above-mentioned annotation method uses multiple videos of the same action. Therefore, inthe third part of this dissertation, we tackle the problem of spatio-temporal action localization in avideo, without assuming the availability of multiple videos or any prior annotations. The action islocalized by employing images downloaded from the Internet using action label. Given web images,we first dampen image noise using random walk and evade distracting backgrounds withinimages using image action proposals. Then, given a video, we generate multiple spatio-temporalaction proposals. We suppress camera and background generated proposals by exploiting opticalivflow gradients within proposals. To obtain the most action representative proposals, we propose toreconstruct action proposals in the video by leveraging the action proposals in images. Moreover,we preserve the temporal smoothness of the video and reconstruct all proposal bounding boxesjointly using the constraints that push the coefficients for each bounding box toward a commonconsensus, thus enforcing the coefficient similarity across multiple frames. We solve this optimizationproblem using the variant of two-metric projection algorithm. Finally, the video proposalthat has the lowest reconstruction cost and is motion salient is used to localize the action. Ourmethod is not only applicable to the trimmed videos, but it can also be used for action localizationin untrimmed videos, which is a very challenging problem.Finally, in the third part of this dissertation, we propose a novel approach to generate a few properlyranked action proposals from a large number of noisy proposals. The proposed approach beginswith dividing each proposal into sub-proposals. We assume that the quality of proposal remainsthe same within each sub-proposal. We, then employ a graph optimization method to recombinethe sub-proposals in all action proposals in a single video in order to optimally build new actionproposals and rank them by the combined node and edge scores. For an untrimmed video, we firstdivide the video into shots and then make the above-mentioned graph within each shot. Our methodgenerates a few ranked proposals that can be better than all the existing underlying proposals. Ourexperimental results validated that the properly ranked action proposals can significantly boostaction detection results.Our extensive experimental results on different challenging and realistic action datasets, comparisonswith several competitive baselines and detailed analysis of each step of proposed methodsvalidate the proposed ideas and frameworks.
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Date Issued: 2017
Identifier: CFE0006801, ucf:51809
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006801

Title: Sinkhole Monitoring Using Groundwater Table Data.
Creator: Tu, Ton, Yun, Hae-Bum, Nam, Boo Hyun, Wang, Dingbao, University of Central Florida
Abstract / Description: Florida might be one of the most sinkhole-active areas on the earth. Due to its unpredictability and significance of occurrence, the development of sinkhole monitoring techniques is imperative to minimize sinkhole-induced hazards. Several methods have been used to evaluate sinkhole risks, including destructive methods, such as Standard Penetrating Tests (SPT) and Cone Penetrating Tests (CPT), geophysical method, and sensor-based groundwater monitoring method. However, few studies are...
Show moreFlorida might be one of the most sinkhole-active areas on the earth. Due to its unpredictability and significance of occurrence, the development of sinkhole monitoring techniques is imperative to minimize sinkhole-induced hazards. Several methods have been used to evaluate sinkhole risks, including destructive methods, such as Standard Penetrating Tests (SPT) and Cone Penetrating Tests (CPT), geophysical method, and sensor-based groundwater monitoring method. However, few studies are available for comprehensive understanding of spatiotemporal sinkhole mechanism by combining different exploration methods under realistic experimental conditions. The objective of this study is to understand spatiotemporal sinkhole mechanism, using SPT, CPT, ground penetrating radar (GPR), and piezo pressure sensors tested at actual sinkhole sites. A small-scale test was conducted prior to the field test to validate data analysis technique using piezo pressure sensors, developed in this study. Eight piezo pressure sensors were used located at different distances from the sinkhole center to measure the ground water levels (GWLs) during artificially made sinkhole events. A total of 24 scaled tests was conducted with different sinkhole soil thickness and initial GWL. The cone of water depression was observed during the tests, which indicates there are strong relationship between sinkhole and sinkhole occurrence. A novel peak-counting method was developed and validated to estimate spatiotemporal relations of the relations between GWLs and sinkhole collapse patterns.The field test was conducted at an active sinkhole site in Lake county, Florida to determine locations of points of breach and to monitor fluctuation GWL over time. Twenty piezometer sensors were installed, and the GWLs were monitored for three months at 30-min sampling rate. The daily moving average of GWL was calculated and visualized in ArcGIS map to understand spatiotemporal behavior of GWL at different locations from sinkhole positions. The monitoring results were compared with CPT, SPT and GPR results that were conducted prior to the piezo sensor installations. Strong correlations were observed between CPT, SPT, GPR and GWL results. From the results, it can be concluded that size and shape of the cone of water depression depend on dimensions of point discharges and properties of surrounding soil.
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Date Issued: 2016
Identifier: CFE0006511, ucf:51383
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006511

Title: Online, Supervised and Unsupervised Action Localization in Videos.
Creator: Soomro, Khurram, Shah, Mubarak, Heinrich, Mark, Hu, Haiyan, Bagci, Ulas, Yun, Hae-Bum, University of Central Florida
Abstract / Description: Action recognition classifies a given video among a set of action labels, whereas action localization determines the location of an action in addition to its class. The overall aim of this dissertation is action localization. Many of the existing action localization approaches exhaustively search (spatially and temporally) for an action in a video. However, as the search space increases with high resolution and longer duration videos, it becomes impractical to use such sliding window...
Show moreAction recognition classifies a given video among a set of action labels, whereas action localization determines the location of an action in addition to its class. The overall aim of this dissertation is action localization. Many of the existing action localization approaches exhaustively search (spatially and temporally) for an action in a video. However, as the search space increases with high resolution and longer duration videos, it becomes impractical to use such sliding window techniques. The first part of this dissertation presents an efficient approach for localizing actions by learning contextual relations between different video regions in training. In testing, we use the context information to estimate the probability of each supervoxel belonging to the foreground action and use Conditional Random Field (CRF) to localize actions. In the above method and typical approaches to this problem, localization is performed in an offline manner where all the video frames are processed together. This prevents timely localization and prediction of actions/interactions - an important consideration for many tasks including surveillance and human-machine interaction. Therefore, in the second part of this dissertation we propose an online approach to the challenging problem of localization and prediction of actions/interactions in videos. In this approach, we use human poses and superpixels in each frame to train discriminative appearance models and perform online prediction of actions/interactions with Structural SVM. Above two approaches rely on human supervision in the form of assigning action class labels to videos and annotating actor bounding boxes in each frame of training videos. Therefore, in the third part of this dissertation we address the problem of unsupervised action localization. Given unlabeled videos without annotations, this approach aims at: 1) Discovering action classes using a discriminative clustering approach, and 2) Localizing actions using a variant of Knapsack problem.
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Date Issued: 2017
Identifier: CFE0006917, ucf:51685
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006917

Title: Experimental Study of Sinkhole Failure Related to Groundwater Level Drops.
Creator: Alrowaimi, Mohamed, Chopra, Manoj, Nam, Boo Hyun, Yun, Hae-Bum, Sallam, Amr, University of Central Florida
Abstract / Description: Sinkholes are natural geohazard phenomena that cause damage to property and may lead to loss of life. They can also cause added pollution to the aquifer by draining unfiltered water from streams, wetland, and lakes into the aquifer. Sinkholes occur in a very distinctive karst geology where carbonate, limestone, dolomite, or gypsum, are encountered as the bedrock that can naturally be dissolved by groundwater circulating through them. Sinkholes can occur gradually or suddenly with catastrophic...
Show moreSinkholes are natural geohazard phenomena that cause damage to property and may lead to loss of life. They can also cause added pollution to the aquifer by draining unfiltered water from streams, wetland, and lakes into the aquifer. Sinkholes occur in a very distinctive karst geology where carbonate, limestone, dolomite, or gypsum, are encountered as the bedrock that can naturally be dissolved by groundwater circulating through them. Sinkholes can occur gradually or suddenly with catastrophic impact depending on the geology and hydrology of the area. Predicting the formation and the collapse of a sinkhole based on the current ground investigation technologies is limited by the high levels of uncertainties in the soil properties and behavior. It is possible that progressing sinkholes can be missed by geotechnical site investigations especially during the development of a very wide area. In this study, a laboratory-scale sinkhole model was constructed to physically simulate the sinkhole phenomenon. The physical model was designed to monitor a network of groundwater table over time around a predetermined sinkhole location. This model was designed to establish a correlation between the groundwater table drops and the sinkhole development. The experimental small-scale model showed that there is a groundwater cone of depression that forms prior the surface collapse of the sinkhole. The cone of water depression can be used to identify the potential location of the sinkhole at early stage of the overburden underground cavities formation in a reverse manner. In addition, monitoring of single groundwater well showed that groundwater level signal has some sudden water drops (progressive drops) which occur at different times (time lags) during the sinkhole development. A time frequency analysis was also used in this study to detect the pattern of these progressive drops of the groundwater table readings. It is observed, based on the model, that the development and growth of sinkhole can be correlated to progressive drops of the groundwater table since the drops start at the monitoring wells that are closer radially to the center of the sinkhole. Subsequently, with time, these drops get transferred to more distant monitoring wells. The time frequency analysis is used to decompose and detect the progressive drops by using a Pattern Detection Algorithm called Auto Modulating Detection Pattern Algorithm (AMD), which was developed by Yun (2013). The results of this analysis showed that the peaks of these progressive drops in the raw groundwater readings are a good indicator of the potential location of sinkholes at early stage when there are no any visible depression of the ground surface. Finally, the effect of several soil parameters on the cone of the water depression during the sinkhole formation is studied. The parametric study showed that both of overburden soil thickness and the initial (encountered) groundwater table level have a clear impact on the time of the sinkhole collapse. While this model used a predetermined crack location to study the groundwater level response around it, the concept of groundwater drops as an indicator of sinkhole progression and collapse may be used to determine the ultimate location of the sinkhole. By monitoring the changes in natural groundwater levels in the field from either an existing network of groundwater monitoring wells or additional installation, the methodology discussed in this dissertation may be used for possible foreseeing of the surface collapse of sinkholes.
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Date Issued: 2016
Identifier: CFE0006249, ucf:51060
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006249

Title: Uncertainty treatment in performance based seismic assessment of typical bridge classes in United States.
Creator: Mehdizadeh Nasrabadi, Mohammad, Mackie, Kevin, Catbas, Necati, Yun, Hae-Bum, Xanthopoulos, Petros, University of Central Florida
Abstract / Description: Bridge networks are expensive and complex infrastructures and are essential components of today's transportation systems. Despite the advancement in computer aided modeling and increasing the computational power which is increasing the accessibility for developing the fragility curves of bridges, the complexity of the problem and uncertainties involved in fragility analysis of the bridge structures in addition to difficulties in validating the results obtained from the analysis requires...
Show moreBridge networks are expensive and complex infrastructures and are essential components of today's transportation systems. Despite the advancement in computer aided modeling and increasing the computational power which is increasing the accessibility for developing the fragility curves of bridges, the complexity of the problem and uncertainties involved in fragility analysis of the bridge structures in addition to difficulties in validating the results obtained from the analysis requires precaution in utilization of the results as a decision making tool. The main focus of this research is to address, study and treatment of uncertainties incorporated in various steps of performance based assessments (PBA) of the bridge structures. In this research the uncertainties is divided into three main categories. First, the uncertainties that come from ground motions time and frequency content alteration because of scarcity of the recorded ground motions in the database. Second, uncertainties associated in the modeling and simulation procedure of PBA, and third uncertainties originated from simplistic approach and methods utilized in the conventional procedure of PBA of the structures. Legitimacy of the scaling of ground motions is studied using the response of several simple nonlinear systems to amplitude scaled ground motions suites. Bias in the response obtained compared to unscaled records for both as recorded and synthetic ground motions.Results from this section of the research show the amount of the bias is considerable and can significantly affect the outcome of PBA. The origin of the bias is investigated and consequently a new metric is proposed to predict the bias induced by ground motion scaling without nonlinear analysis. Results demonstrate that utilizing the predictor as a scaling parameter can significantly reduce the bias for various nonlinear structures. Therefore utilizing the new metric as the intensity measuring parameter of the ground motions is recommended in PBA. To address the uncertainties associated in the modeling and simulation, MSSS concrete girder bridge class were selected due to the frequency of the construction in USCS region and lack of seismic detailing. A large scale parameters screening study is performed using Placket-Burman experimental design that considers a more complete group of parameters to decrease the computational expense of probabilistic study of the structure's seismic response. Fragility analysis for MSSS bridge is performed and the effect of removing the lesser important parameters the probabilistic demand model was investigated. This study reveals parameters reduction based on screening study techniques can be utilized to increase efficiency in fragility analysis procedure without compromising the accuracy of the outcome. The results from this study also provides more direct information on parameter reduction for PBA as well as provide insight into where future investments into higher fidelity finite element and constitutive models should be targeted. Conventional simplistic PBA approach does not account for the fundamental correlation between demand and capacity models. A more comprehensive PBA approach is presented and fragility analysis is performed with implementation of a new formulation in the component fragility analysis for MSSS bridge class and the outcome is compared with the one from conventional procedure. The results shows the correlation between demand and capacity affects the outcome of PBA and the fragility functions variation is not negligible. Therefore using the presented approach is necessary when accuracy is needed.
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Date Issued: 2014
Identifier: CFE0005531, ucf:50309
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005531

Title: Multi-physics modeling of geomechanical systems with coupled hydromechanical behaviors.
Creator: Mohamed, Ahmad Saeid Ammar, Yun, Hae-Bum, Chopra, Manoj, Sallam, Amr, University of Central Florida
Abstract / Description: Geotechnical structures under realistic field conditions are usually influenced with complex interactions of coupled hydromechanical behavior of porous materials. In many geotechnical applications, however, these important coupled interactions are ignored in their constitutive models. Under coupled hydromechanical behavior, stress in porous materials causes volumetric change in strain, which causes fluid diffusion; consequently, pore pressure dissipates through the pores that results in the...
Show moreGeotechnical structures under realistic field conditions are usually influenced with complex interactions of coupled hydromechanical behavior of porous materials. In many geotechnical applications, however, these important coupled interactions are ignored in their constitutive models. Under coupled hydromechanical behavior, stress in porous materials causes volumetric change in strain, which causes fluid diffusion; consequently, pore pressure dissipates through the pores that results in the consolidation of porous material. The objective of this research was to demonstrate the advantages of using hydromechanical models to estimate deformation and pore water pressure of porous materials by comparing with mechanical-only models. Firstly, extensive literature survey was conducted about hydro-mechanical models based on Biot's poroelastic concept. Derivations of Biot's poroelastic equations will be presented. To demonstrate the hydromechanical effects, a numerical model of poroelastic rock materials was developed using COMSOL, a commercialized multiphysics finite element software package, and compared with the analytical model developed by Wang (2000). Secondly, a series of sensitivity analyses was conducted to correlate the effect of poroelastic parameters on the behavior of porous material. The results of the sensitivity analysis show that porosity and Biot's coefficient has dominant contribution to porous material behavior. Thirdly, a coupled hydromechanical finite element model was developed for a real-world example of embankment consolidation. The simulation results show excellent agreement to field measurements of embankment settlement data.
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Date Issued: 2013
Identifier: CFE0004722, ucf:49826
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004722

Title: The Effects of Assumption on Subspace Identification Methods Using Simulation and Experimental Data.
Creator: Kim, Yoonhwak, Yun, Hae-Bum, Catbas, Fikret, Mackie, Kevin, Nam, Boo Hyun, Behal, Aman, University of Central Florida
Abstract / Description: In the modern dynamic engineering field, experimental dynamics is an important area of study. This area includes structural dynamics, structural control, and structural health monitoring. In experimental dynamics, methods to obtain measured data have seen a great influx of research efforts to develop an accurate and reliable experimental analysis result. A technical challenge is the procurement of informative data that exhibits the desired system information. In many cases, the number of...
Show moreIn the modern dynamic engineering field, experimental dynamics is an important area of study. This area includes structural dynamics, structural control, and structural health monitoring. In experimental dynamics, methods to obtain measured data have seen a great influx of research efforts to develop an accurate and reliable experimental analysis result. A technical challenge is the procurement of informative data that exhibits the desired system information. In many cases, the number of sensors is limited by cost and difficulty of data archive. Furthermore, some informative data has technical difficulty when measuring input force and, even if obtaining the desired data were possible, it could include a lot of noise in the measuring data. As a result, researchers have developed many analytical tools with limited informative data. Subspace identification method is used one of tools in these achievements.Subspace identification method includes three different approaches: Deterministic Subspace Identification (DSI), Stochastic Subspace Identification (SSI), and Deterministic-Stochastic Subspace Identification (DSSI). The subspace identification method is widely used for fast computational speed and its accuracy. Based on the given information, such as output only, input/output, and input/output with noises, DSI, SSI, and DSSI are differently applied under specific assumptions, which could affect the analytical results. The objective of this study is to observe the effect of assumptions on subspace identification with various data conditions. Firstly, an analytical simulation study is performed using a six-degree-of-freedom mass-damper-spring system which is created using MATLAB. Various conditions of excitation insert to the simulation test model, and its excitation and response are analyzed using the subspace identification method. For stochastic problems, artificial noise is contained to the excitation and followed the same steps. Through this simulation test, the effects of assumption on subspace identification are quantified.Once the effects of the assumptions are studied using the simulation model, the subspace identification method is applied to dynamic response data collected from large-scale 12-story buildings with different foundation types that are tested at Tongji University, Shanghai, China. Noise effects are verified using three different excitation types. Furthermore, using the DSSI, which has the most accurate result, the effect of different foundations on the superstructure are analyzed.
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Date Issued: 2013
Identifier: CFE0004703, ucf:49822
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0004703

Title: Applications of Computer Vision Technologies of Automated Crack Detection and Quantification for the Inspection of Civil Infrastructure Systems.
Creator: Wu, Liuliu, Yun, Hae-Bum, Nam, Boo Hyun, Catbas, Necati, Foroosh, Hassan, University of Central Florida
Abstract / Description: Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation's resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure....
Show moreMany components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation's resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure. Conventional human visual inspection is still considered as the primary inspection method. However, it is well established that human visual inspection is subjective and often inaccurate. In order to improve current manual visual inspection for crack detection and evaluation of civil infrastructure, this study explores the application of computer vision techniques as a non-destructive evaluation and testing (NDE(&)T) method for automated crack detection and quantification for different civil infrastructures. In this study, computer vision-based algorithms were developed and evaluated to deal with different situations of field inspection that inspectors could face with in crack detection and quantification. The depth, the distance between camera and object, is a necessary extrinsic parameter that has to be measured to quantify crack size since other parameters, such as focal length, resolution, and camera sensor size are intrinsic, which are usually known by camera manufacturers. Thus, computer vision techniques were evaluated with different crack inspection applications with constant and variable depths. For the fixed-depth applications, computer vision techniques were applied to two field studies, including 1) automated crack detection and quantification for road pavement using the Laser Road Imaging System (LRIS), and 2) automated crack detection on bridge cables surfaces, using a cable inspection robot. For the various-depth applications, two field studies were conducted, including 3) automated crack recognition and width measurement of concrete bridges' cracks using a high-magnification telescopic lens, and 4) automated crack quantification and depth estimation using wearable glasses with stereovision cameras.From the realistic field applications of computer vision techniques, a novel self-adaptive image-processing algorithm was developed using a series of morphological transformations to connect fragmented crack pixels in digital images. The crack-defragmentation algorithm was evaluated with road pavement images. The results showed that the accuracy of automated crack detection, associated with artificial neural network classifier, was significantly improved by reducing both false positive and false negative. Using up to six crack features, including area, length, orientation, texture, intensity, and wheel-path location, crack detection accuracy was evaluated to find the optimal sets of crack features. Lab and field test results of different inspection applications show that proposed compute vision-based crack detection and quantification algorithms can detect and quantify cracks from different structures' surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application.
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Date Issued: 2015
Identifier: CFE0005743, ucf:50089
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005743

Structural Health Monitoring (3)	+ -
Machine Learning (2)	+ -
System Identification (2)	+ -
Unsupervised Learning (2)	+ -
Action Detection (1)	+ -

Action Localization (1)	+ -
Action Recognition (1)	+ -
Activity Recognition (1)	+ -
Automated crack detection (1)	+ -
Autonomous Trucks Strategies (1)	+ -
Big Data Analytics (1)	+ -
Biot (1)	+ -
Blind Source Seperation (1)	+ -
Bond-Slip (1)	+ -
Bridge (1)	+ -
Cementitious Materials (1)	+ -
Composites (1)	+ -
Compression (1)	+ -
Computer Vision (1)	+ -
Consensus clustering (1)	+ -
Construction (1)	+ -
Correlation (1)	+ -
Coupling (1)	+ -
Data Driven (1)	+ -
Data-Driven (1)	+ -
Decision Support (1)	+ -
Design of experiment (1)	+ -
EMD (1)	+ -
Ensemble Learning (1)	+ -
Exclusive Truck Lane (1)	+ -

University of Central Florida (23)	+ -
Yun, Hae-Bum (23)	+ -
Chopra, Manoj (7)	+ -
Nam, Boo Hyun (6)	+ -
Catbas, Necati (5)	+ -

Mackie, Kevin (5)	+ -
Catbas, Fikret (4)	+ -
Sallam, Amr (4)	+ -
Shah, Mubarak (4)	+ -
Bagci, Ulas (3)	+ -
Xanthopoulos, Petros (3)	+ -
Foroosh, Hassan (2)	+ -
Mohamed, Ahmad Saeid Ammar (2)	+ -
Qi, GuoJun (2)	+ -
Sukthankar, Gita (2)	+ -
Zheng, Qipeng (2)	+ -
Akhavian, Reza (1)	+ -
Al Rumaithi, Ayad (1)	+ -
Ali, Muhammad (1)	+ -
Alrowaimi, Mohamed (1)	+ -
An, Jin Woo (1)	+ -
An, Linan (1)	+ -
Behal, Aman (1)	+ -
Behzadan, Amir (1)	+ -
Celik, Ozan (1)	+ -
DeMara, Ronald (1)	+ -
Gokce, Hasan (1)	+ -
Gou, Jihua (1)	+ -
Gul, Mustafa (1)	+ -
Heinrich, Mark (1)	+ -

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