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Title: Physical Hydrogeological Modeling of Florida's Sinkhole Hazard.
Creator: Perez, Adam, Nam, Boo Hyun, Wang, Dingbao, Chopra, Manoj, Singh, Arvind, An, Jin Woo, University of Central Florida
Abstract / Description: Sinkholes are one of the major geohazards in karst terrain and pose a social, economic, and environmental risk. In Florida, sinkhole-related insurance claims between 2006 and the third quarter of 2010 amounted to $1.4 billion. Approximately 20 % of the United States is underlain by karst terrain formed from the dissolution of soluble rocks and is susceptible to a sinkhole hazard. Particularly, Texas, Florida, Tennessee, Alabama, Missouri, Kentucky, and Pennsylvania are known as sinkhole...
Show moreSinkholes are one of the major geohazards in karst terrain and pose a social, economic, and environmental risk. In Florida, sinkhole-related insurance claims between 2006 and the third quarter of 2010 amounted to $1.4 billion. Approximately 20 % of the United States is underlain by karst terrain formed from the dissolution of soluble rocks and is susceptible to a sinkhole hazard. Particularly, Texas, Florida, Tennessee, Alabama, Missouri, Kentucky, and Pennsylvania are known as sinkhole states.The scope of this study is to develop a physical model to simulate sinkholes (referred to as a sinkhole simulator), which can assess the qualitative behavior of the hydrogeological mechanism of Florida's sinkhole formations. Two sinkhole simulators were developed, with the second simulator constructed to overcoming the limitations of the first. The first generation sinkhole simulator incorporated a falling head groundwater system and the sinkhole could only be observed once the ground surface was breached. The second generation sinkhole simulator incorporated a constant head groundwater system which accurately depicts field conditions and the sinkhole was able to be observed during all stages of formation within this model. In both simulators multiple hydrogeological conditions were created and water level transducers were installed at various locations within the soil profile to monitor variations in the groundwater table during the sinkhole process, this was done to investigate the soil-groundwater behavior.Findings from this study include: 1) groundwater recharge is a critical sinkhole triggering factor, 2) the groundwater table cone of depression increases as the raveled zone or void travels up through the overburden due to sinkhole formation, 3) The cover-subsidence sinkhole failure mechanism is similar to the failure mechanism present in Terzaghi's trapdoor experiment and the cover-collapse failure mechanism consists of four district components: failure planes with erosion envelope, arch dropout failure, formation of elliptical void, and slope stability failure, and 4) a strong qualitative relationship between soil strength and type of sinkhole formed (cover-subsidence or cover-collapse) was observed.
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Date Issued: 2017
Identifier: CFE0006637, ucf:51247
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006637

Title: Development of Regional Optimization and Market Penetration Models For the Electric Vehicles in the United States.
Creator: Noori, Mehdi, Tatari, Omer, Oloufa, Amr, Nam, Boo Hyun, Xanthopoulos, Petros, University of Central Florida
Abstract / Description: Since the transportation sector still relies mostly on fossil fuels, the emissions and overall environmental impacts of the transportation sector are particularly relevant to the mitigation of the adverse effects of climate change. Sustainable transportation therefore plays a vital role in the ongoing discussion on how to promote energy insecurity and address future energy requirements. One of the most promising ways to increase energy security and reduce emissions from the transportation...
Show moreSince the transportation sector still relies mostly on fossil fuels, the emissions and overall environmental impacts of the transportation sector are particularly relevant to the mitigation of the adverse effects of climate change. Sustainable transportation therefore plays a vital role in the ongoing discussion on how to promote energy insecurity and address future energy requirements. One of the most promising ways to increase energy security and reduce emissions from the transportation sector is to support alternative fuel technologies, including electric vehicles (EVs). As vehicles become electrified, the transportation fleet will rely on the electric grid as well as traditional transportation fuels for energy. The life cycle cost and environmental impacts of EVs are still very uncertain, but are nonetheless extremely important for making policy decisions. Moreover, the use of EVs will help to diversify the fuel mix and thereby reduce dependence on petroleum. In this respect, the United States has set a goal of a 20% share of EVs on U.S. roadways by 2030. However, there is also a considerable amount of uncertainty in the market share of EVs that must be taken into account. This dissertation aims to address these inherent uncertainties by presenting two new models: the Electric Vehicles Regional Optimizer (EVRO), and Electric Vehicle Regional Market Penetration (EVReMP). Using these two models, decision makers can predict the optimal combination of drivetrains and the market penetration of the EVs in different regions of the United States for the year 2030.First, the life cycle cost and life cycle environmental emissions of internal combustion engine vehicles, gasoline hybrid electric vehicles, and three different EV types (gasoline plug-in hybrid EVs, gasoline extended-range EVs, and all-electric EVs) are evaluated with their inherent uncertainties duly considered. Then, the environmental damage costs and water footprints of the studied drivetrains are estimated. Additionally, using an Exploratory Modeling and Analysis method, the uncertainties related to the life cycle costs, environmental damage costs, and water footprints of the studied vehicle types are modeled for different U.S. electricity grid regions. Next, an optimization model is used in conjunction with this Exploratory Modeling and Analysis method to find the ideal combination of different vehicle types in each U.S. region for the year 2030. Finally, an agent-based model is developed to identify the optimal market shares of the studied vehicles in each of 22 electric regions in the United States. The findings of this research will help policy makers and transportation planners to prepare our nation's transportation system for the future influx of EVs.The findings of this research indicate that the decision maker's point of view plays a vital role in selecting the optimal fleet array. While internal combustion engine vehicles have the lowest life cycle cost, the highest environmental damage cost, and a relatively low water footprint, they will not be a good choice in the future. On the other hand, although all-electric vehicles have a relatively low life cycle cost and the lowest environmental damage cost of the evaluated vehicle options, they also have the highest water footprint, so relying solely on all-electric vehicles is not an ideal choice either. Rather, the best fleet mix in 2030 will be an electrified fleet that relies on both electricity and gasoline. From the agent-based model results, a deviation is evident between the ideal fleet mix and that resulting from consumer behavior, in which EV shares increase dramatically by the year 2030 but only dominate 30 percent of the market. Therefore, government subsidies and the word-of-mouth effect will play a vital role in the future adoption of EVs.
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Date Issued: 2015
Identifier: CFE0005852, ucf:50927
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005852

Title: Evaluation of strength and hydraulic properties of buried pipe systems used for stormwater harvesting.
Creator: Samson Mena, Mario, Chopra, Manoj, Wang, Dingbao, Nam, Boo Hyun, Gogo-Abite, Ikiensinma, University of Central Florida
Abstract / Description: Water scarcity has been identified as a global issue. Both water harvesting and an efficient water piping system are some of the important factors to meet the water demand. In this study, high-density polyethylene (HDPE) pipes used as an underground storage was evaluated and a Microsoft EXCEL based model was developed, called PIPE-R Model. To study the structural integrity of the pipes, laboratory and field testing were conducted. For the water harvesting, UCF Stormwater Management Academy...
Show moreWater scarcity has been identified as a global issue. Both water harvesting and an efficient water piping system are some of the important factors to meet the water demand. In this study, high-density polyethylene (HDPE) pipes used as an underground storage was evaluated and a Microsoft EXCEL based model was developed, called PIPE-R Model. To study the structural integrity of the pipes, laboratory and field testing were conducted. For the water harvesting, UCF Stormwater Management Academy designed an EXCEL based model to simulate the system's performance to store and redistribute water for an average year.The purpose of PIPE-R Model was to provide average yearly values such as groundwater recharge, hydrologic efficiency and make up water needed in order to guide the user in the design process. The PIPE-R Model consisted on evaluating specific pipe systems based on properties selected by the user. Input variables such as system dimensions, soil type and reuse water demand provided flexibility to the user while evaluating the system. Results of the study showed that the PIPE-R Model might be an effective tool while designing these pipe systems. A detailed example was shown to help visualize the process required to use the model. The PIPE-R model allowed the user a wide range of possibilities and obtain important performance data that will hopefully optimize the cost for its construction.For the evaluation of the structural integrity of the pipe system, laboratory testing was conducted in accordance with ASTM D2412 ? 11 (")Determination of External Loading Characteristics of Plastic Pipe by Parallel-Plate Loading("). This method helps evaluate the structural performance based on the pipe stiffness (PS) against the standard values stated by AASHTO M252. The test procedure consisted on establishing load-deflection relationship of aivsingle pipe under parallel plate loading. However, this research project involved the analysis of bundled pipes of different sizes and levels. Thus, modifications were added to the formula in order to evaluate multiple pipes by accounting the number of pipes in contact with the loading plate. Laboratory results demonstrated that the pipes exceeded the minimum requirements stated by AASHTO M252 and that strength is decreased as the number of levels increases.In addition, field testing was conducted to study the behavior of bundle systems under the effects of dead and live loads. Three different cover configuration were studied ranging from 18 inches to 43 inches of depth. Draw-wire sensors, a type of displacement sensors, were placed inside buried housing structures to monitor deformation values experienced by the pipe bundles during the test. Average deformations founds for the cover depths of 43 in, 30 in and 18 in were 0.07 in, 0.32 in and 0.64 in, respectively. Based on these results, the field testing revealed that a minimum of 30 inches of cover is seemed to be appropriate if live loads are applicable.
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Date Issued: 2015
Identifier: CFE0006055, ucf:50976
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006055

Title: Integrated Sustainability Assessment Framework for the U.S. Transportation.
Creator: Onat, Nuri, Tatari, Omer, Nam, Boo Hyun, Oloufa, Amr, Pazour, Jennifer, University of Central Florida
Abstract / Description: This dissertation aims to investigate the sustainability impacts of alternative vehicle technologies and develop comprehensive sustainability assessment frameworks to analyze potential impacts of these vehicles in the U.S. In order to assess sustainability impact of vehicle alternatives, life-cycle based models has been extensively used in the literature. Although life cycle-based models are often used for environmental impacts of alternative vehicles, analysis of social and economic impacts...
Show moreThis dissertation aims to investigate the sustainability impacts of alternative vehicle technologies and develop comprehensive sustainability assessment frameworks to analyze potential impacts of these vehicles in the U.S. In order to assess sustainability impact of vehicle alternatives, life-cycle based models has been extensively used in the literature. Although life cycle-based models are often used for environmental impacts of alternative vehicles, analysis of social and economic impacts of these vehicles has gained a tremendous interest. In this regard, there is a growing interest among the international platform and academia to use the Life Cycle Sustainability Assessment framework to have more informed sustainable products, material and technology choices by considering the environmental, as well as social and economic impacts. The Life Cycle Sustainability Assessment framework is still under development and there is an ongoing research to advance it for future applications. In this dissertation, current and future needs of sustainability assessment frameworks and the U.S. transportation are identified and addressed. The major research gaps are identified as follows: (1) there has been small emphasis on effects of spatial and temporal variations on the sustainability impacts of alternative vehicle technologies, (2) no national research efforts as of now have been directed specifically toward understanding the fundamental relationship between the adoption of electric vehicles and water demand, (3) there has been a lack of understanding the dynamic complexity of transportation sustainability, encompassing feedback mechanisms, and interdependencies, for the environmental, social, and economic impacts of alternative vehicles, and (4) there is no emphasis on addressing uncertainties inherent to the U.S. transportation and its complex relationships with the environment, society, and economy.The environmental, economic, and social impacts of alternative vehicles are highly critical for truly assessing and understanding the long-term sustainability of vehicles and propose economically viable, socially acceptable, and environmentally-friendly transportation solutions for U.S. passenger transportation. This dissertation provides a more comprehensive sustainability assessment framework by realizing following objectives: (1) inclusion of spatial and temporal variations when quantifying carbon, energy, and water footprints of alternative vehicle technologies, (2) quantifying environmental, social, and economic impacts of alternative vehicle technologies, (3) capturing the dynamic relations among the parameters of U.S. transportation system, environment, society, and the economy, (4) dealing with uncertainties inherent to the U.S. transportation sector considering the complexity of the system and dynamic relationships. The results of this dissertation reveal that the results with consideration of uncertainties, temporal and spatial variations, and dynamic complex relationships among the system variables can be significantly different than those of without consideration of those. Therefore, when developing policies the robustness of proposed scenarios should be valuated with consideration of uncertainties, temporal and spatial variations as well as the dynamic feedback mechanisms. The outcomes of this study can pave the way for advancement in the state-of-the-art and state-of-the-practice in the sustainability research by presenting novel approaches to deal with uncertainties and complex systems.
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Date Issued: 2015
Identifier: CFE0005857, ucf:50904
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005857

Title: Urban Expressway Safety and Efficiency Evaluation and Improvement using Big Data.
Creator: Shi, Qi, Abdel-Aty, Mohamed, Eluru, Naveen, Nam, Boo Hyun, Lee, Chris, University of Central Florida
Abstract / Description: In an age of data explosion, almost every aspect of social activities is impacted by the abundance of information. The information, characterized by alarming volume, velocity and variety, is often referred to as (")Big Data("). As one fundamental elements of human life, transportation also confronts the promises and challenges brought about by the Big Data era. Big Data in the transportation arena, enabled by the rapid popularization of Intelligent Transportation Systems (ITS) in the past few...
Show moreIn an age of data explosion, almost every aspect of social activities is impacted by the abundance of information. The information, characterized by alarming volume, velocity and variety, is often referred to as (")Big Data("). As one fundamental elements of human life, transportation also confronts the promises and challenges brought about by the Big Data era. Big Data in the transportation arena, enabled by the rapid popularization of Intelligent Transportation Systems (ITS) in the past few decades, are often collected continuously from different sources over vast geographical scale. Huge in size and rich in information, the seemingly disorganized data could considerably enhance experts' understanding of their system. In addition, proactive traffic management for better system performance is made possible due to the real-time nature of the Big Data in transportation.Operation efficiency and traffic safety have long been deemed as priorities among highway system performance measurement. While efficiency could be evaluated in terms of traffic congestion, safety is studied through crash analysis. Extensive works have been conducted to identify the contributing factors and remedies of traffic congestion and crashes. These studies lead to gathering consensus that operation and safety have played as two sides of a coin, ameliorating either would have a positive effect on the other. With the advancement of Big Data, monitoring and improvement of both operation and safety proactively in real-time have become an urgent call.In this study, the urban expressway network operated by Central Florida Expressway Authority's (CFX) traffic safety and efficiency was investigated. The expressway system is equipped with multiple Intelligent Transportation Systems (ITS). CFX utilizes Automatic Vehicle Identification (AVI) system for Electronic Toll Collection (ETC) as well as for the provision of real-time information. Recently, the authority introduced Microwave Vehicle Detection System (MVDS) on their expressways for more precise traffic monitoring. These traffic detection systems collect different types of traffic data continuously on the 109-mile expressway network, making them one of the sources of Big Data. In addition, multiple Dynamic Message Signs are currently in use to communicate between CFX and motorists. Due to their dynamic nature, they serve as an ideal tool for efficiency and safety improvement. Careful examination of the Big Data from the ITS traffic detection systems was carried out. Based on the characteristics of the data, three types of congestion measures based on the AVI and MVDS system were proposed for efficiency evaluation. MVDS-based congestion measures were found to be better at capturing the subtle changes in congestion in real-time compared with the AVI-based congestion measure. Moreover, considering the high deployment density of the MVDS system, the whole expressway network is well covered. Thus congestion could be evaluated at the microscopic level in both spatial and temporal dimensions. According to the proposed congestion measurement, both mainline congested segments and ramps experiencing congestion were identified. For congestion alleviation, the existing DMS that could be utilized for queue warning were located. In case of no existing DMS available upstream to the congestion area, the potential area where future DMS could be considered was suggested. Substantial efforts have also been dedicated to Big Data applications in safety evaluation and improvement. Both aggregate crash frequency modeling and disaggregate real-time crash prediction were constructed to explore the use of ITS detection data for urban expressway safety analyses. The safety analyses placed an emphasis on the congestion's effects on the Expressway traffic safety. In the aggregate analysis the three congestion measures developed in this research were tested in the context of safety modeling and their performances compared. Multi-level Bayesian ridge regression was utilized to deal with the multicollinearity issue in the modeling process. While all of the congestion measures indicated congestion was a contributing factor to crash occurrence in the peak hours, they suggested that off-peak hour crashes might be caused by factors other than congestion. Geometric elements such as the horizontal curves and existence of auxiliary lanes were also identified to significantly affect the crash frequencies on the studied expressways.In the disaggregate analysis, rear-end crashes were specifically studied since their occurrence was believed to be significantly related to the traffic flow conditions. The analysis was conducted in Bayesian logistic regression framework. The framework achieved relatively good classifier performance. Conclusions confirmed the significant effects of peak hour congestion on crash likelihood. Moreover, a further step was taken to incorporate reliability analysis into the safety evaluation. With the developed logistic model as a system function indicating the safety states under specific traffic conditions, this method has the advantage that could quantitatively determine the traffic states appropriate to trigger safety warning to motorists. Results from reliability analysis also demonstrate the peak hours as high risk time for rear-end crashes. Again, DMS would be an essential tool to carry the messages to drivers for potential safety benefits. In existing safety studies, the ITS traffic data were normally used in aggregated format or only the pre-crash traffic data were used for real-time prediction. However, to fully realize their applications, this research also explored their use from a post-crash perspective. The real-time traffic states immediately before and after crash occurrence were extracted to identify whether the crash caused traffic deterioration. Elements regarding spatial, temporal, weather and crash characteristics from individual crash reports were adopted to analyze under what conditions a crash could significantly worsen traffic conditions on urban expressways. Multinomial logit model and two separate binomial models were adopted to identify each element's effects. Expected contribution of this work is to shorten the reaction and clearance time to those crashes that might cause delay on expressways, thus reducing congestion and probability of secondary crashes simultaneously.Finally, potential relevant applications beyond the scope of this research but worth investigation in the future were proposed.
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Date Issued: 2014
Identifier: CFE0005886, ucf:50888
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005886

Title: A Macro-Level Sustainability Assessment Framework for Optimal Distribution of Alternative Passenger Vehicles.
Creator: Onat, Nuri, Tatari, Omer, Nam, Boo Hyun, Oloufa, Amr, Pazour, Jennifer, University of Central Florida
Abstract / Description: Although there are many studies focusing on the environmental impacts of alternative vehicle options, social and economic dimensions and trade-off relationships among all of these impacts were not investigated sufficiently. Moreover, most economic analyses are limited to life cycle cost analyses and do not consider macro-level economic impacts. Therefore, this thesis aims to advance the Life Cycle Sustainability Assessment literature and electric vehicle sustainability research by presenting...
Show moreAlthough there are many studies focusing on the environmental impacts of alternative vehicle options, social and economic dimensions and trade-off relationships among all of these impacts were not investigated sufficiently. Moreover, most economic analyses are limited to life cycle cost analyses and do not consider macro-level economic impacts. Therefore, this thesis aims to advance the Life Cycle Sustainability Assessment literature and electric vehicle sustainability research by presenting a novel combined application of Multi Criteria Decision Making techniques with Life Cycle Sustainability Assessment for decision analysis. With this motivation in mind, this research will construct a compromise-programming model (multi-objective optimization method) in order to calculate the optimum vehicle distribution in the U.S. passenger car fleet while considering the trade-offs between environmental, economic, and social dimensions of the sustainability. The findings of this research provide important insights for policy makers when developing strategies to estimate optimum vehicle distribution strategies based on various environmental and socio-economic priorities. For instance, compromise programming results can present practical policy conclusions for different states which might have different priorities for environmental impact mitigation and socio-economic development. Therefore, the conceptual framework presented in this work can be applicable for different regions in U.S. and decision makers can generate balanced policy conclusions and recommendations based on their environmental, economic and social constraints. The compromise programming results provide vital guidance for policy makers when optimizing the use of alternative vehicle technologies based on different environmental and socio-economic priorities. This research also effort aims to increase awareness of the inherent benefits of Input-Output based a Life Cycle Sustainability Assessment and multi-criteria optimization.
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Date Issued: 2015
Identifier: CFE0005858, ucf:50901
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005858

Title: Mechanistic Behavior of UHPC and UHPC Composite Structural Components.
Creator: Al-Ramahee, Munaf, Mackie, Kevin, Makris, Nicos, Nam, Boo Hyun, Gou, Jihua, University of Central Florida
Abstract / Description: The resistance of reinforced concrete is mobilized through the composite action of two materials with different mechanical behaviors and physical features. Enabling the composite action requires a transfer mechanism between the concrete and the reinforcement which is referred to as bond. The bond model can be defined as a traction-slip relation tangent to the interface. The bond strength between different types of concrete, internal reinforcement, and external reinforcement has been of...
Show moreThe resistance of reinforced concrete is mobilized through the composite action of two materials with different mechanical behaviors and physical features. Enabling the composite action requires a transfer mechanism between the concrete and the reinforcement which is referred to as bond. The bond model can be defined as a traction-slip relation tangent to the interface. The bond strength between different types of concrete, internal reinforcement, and external reinforcement has been of interest to structural engineers for decades. Experimental tests have been carried out to validate the existing bond models and introduce new bond models for special cases of concrete or reinforcement. The effect of various parameters on the bond stress, such as bar diameter, concrete compressive strength, presence of fibers, cyclic loading, etc. have been investigated. However, little attention has been directed to the contribution of normal (to the interface) stress and state of stress of the substrate layer on the mechanical response of the interface. Since the state of stress (tangential, normal, and substrate) within each type of experimental test is different, the resulting bond models are not consistent.Behavior of ultra-high performance concrete (UHPC) composite flexural members are studied using experimental, analytical, and numerical approaches in this research. A new bond-slip model is proposed that contains an explicit representation of the normal stress and constitutive model of the substrate. The parameters of the model were calibrated from beam and pullout tests using UHPC and HSS. The calibrated results showed consistency in the material point behavior between the pullout and beam test although the states of stress were different. The effect of the normal force was verified throughout a numerical model compared with experimental flexural tests. Single and double lap shear tests were carried out for UHPC and FRP, and parameters of the bilinear model were calibrated and used in the finite element model of the new composite deck.A new lightweight composite deck system is proposed that uses fiber reinforced polymers (FRP) bonded to UHPC using vacuum-assisted resin transfer molding. The high-performance deck system has application in deck design and replacement for bridges with weight restrictions as well as for accelerated bridge construction. Results show the deck satisfies strength and serviceability criteria under monotonic load. The bond strength between the UHPC and the glass fiber reinforced polymers (GFRP) plays a significant role in the performance of the proposed deck and controls the behavior of the system. However, live loads on bridges are inherently cyclic and therefore research on serviceability and fatigue behavior of UHPC and UHPC composite members were carried out. The UHPC beams were strengthened using glass GFRP plates on compression side to obtain data that could be utilized for the future design. The effect of fatigue loading on the interfacial shear stress between UHPC and GFRP was also investigated and it is found to be minor under low load level. However, a noticeable progression in the interfacial shear stress was found for the higher load ratio.
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Date Issued: 2016
Identifier: CFE0006431, ucf:51464
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006431

Title: GETTING TO NET ZERO ENERGY BUILDINGS: A HOLISTIC TECHNO-ECOLOGICAL MODELING APPROACH.
Creator: Alirezaei, Mehdi, Tatari, Omer, Oloufa, Amr, Nam, Boo Hyun, Xanthopoulos, Petros, University of Central Florida
Abstract / Description: Buildings in the United States are responsible for more than 40% of the primary energy and 70% of electricity usage and greatly in CO2 emission by about 39%, more than any other sector including transportation and industry sectors. This energy consumption is expected to grow mainly due to increasing trends in new buildings construction. Rising energy prices alongside with energy independencies, limited resources, and climate change have made the current situation even worse. An Energy...
Show moreBuildings in the United States are responsible for more than 40% of the primary energy and 70% of electricity usage and greatly in CO2 emission by about 39%, more than any other sector including transportation and industry sectors. This energy consumption is expected to grow mainly due to increasing trends in new buildings construction. Rising energy prices alongside with energy independencies, limited resources, and climate change have made the current situation even worse. An Energy Efficient (EE) building is able to reduce the heating and cooling load significantly compared with a code compliant building. Furthermore, integrating renewable energy sources in the building energy portfolio could drive the building's grid reliance further down. Such buildings that are able to passively save and actively produce energy are called Net Zero Energy Buildings (NZEB). Despite all new energy efficient technologies, reaching NZEB is challenging due to high first cost of super-efficient measures and renewable energy sources as well as integration of the newly on-site generated electricity to the grid. Achieving NZEB without looking at its surrounding environment may result in sub-optimal solutions. Currently, 95% of American households own a car, and with the help of newly introduced Vehicle to Home (V2H) technologies, building, vehicle, renewable energy sources, and ecological environment can work together as a techno-ecological system to fulfill the requirement of an NZEB ecosystem.Due to the great flexibility of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) in interacting with the power grid, they will play a significant role in the future of the power system. In a large scale, an organized fleet of EVs can be considered as reliable and flexible power storage for a set of building blocks or in a smaller scale, individual EV owners can use their own vehicles as a source of power alongside with other sources of power. To this end, V2H technologies can utilize idle EV battery power as an electricity storage tool to mitigate fluctuations in renewable electric power supply, to provide electricity for the building during the peak time, and to help in supplying electricity during emergency situation and power outage. V2H is said to be the solution to a successful integration of renewables and at the same time maintaining the integrity of the grid. This happens through depleting the stored power in the battery of EV and then charging the battery when the demand is low, using the electricity provided by grid or renewables. Government incentives can play an important role in employing this technology by buying out the high first time cost request. According to Energy Information Administration (EIA), U.S. residential utility customers consume 29.95 kWh electricity on average per household-day. With the current technology, EV batteries could store up to 30 kWh electricity. As a result, even for a code compliant house, a family could use EV battery as a source of energy for one normal day operation. For an energy efficient home, there could even be a surplus of energy that could be transferred to the grid. In summary, Achieving NZEB is facing various obstacles and removing these barriers require a more holistic view on a greater system and environment, where a building interacts with on-site renewable energy sources, EV, and its surrounded ecological environment.This dissertation aims to utilize the application of Vehicle to Home technology to reach NZEB by developing two new models in two phases; the macro based excel model (NZEB-VBA) and agent based model (NZEB-ABM). Using these two models, homeowners can calculate the savings through implementing abovementioned technologies which can be considered as a motivation to move toward greener buildings. In the first step, an optimization analysis is performed first to select the best design alternatives for an energy-efficient building under the relevant economic and environmental constraints. Next, solar photovoltaic sources are used to supply the building's remaining energy demand and thereby minimize the building's grid reliance. Finally, Vehicle to Home technology is coupled with the renewable energy source as a substitute for power from the grid. The whole algorithm for this process will be running in the visual basic environment.In the second phase of the study, the focus is more on the dynamic interaction of different components of the system with each other. Although the general procedure is the same, the modeling will take place in a different environment. Showing the status of different parts of the system at any specific time, changing the values of different parameters of the system and observing the results, and investigating the impact of each parameter's on overall behavior of the system are among the advantages of the agent based model. Having real time data can greatly enhance the capabilities of this system. The results indicate that, with the help of energy-efficient design features and a properly developed algorithm to draw electricity from EV and solar energy, it is possible to reduce the required electricity from the power grid by 59% when compared to a standard energy-efficient building and by as much as 90% when compared to a typical code-compliant building. This thereby reduces the electricity cost by 1.55 times the cost of the conventional method of drawing grid electricity. This savings can compensate the installation costs of solar panels and other technologies necessary for a Net Zero Energy Building. In the last phase of the study, a regional analysis will be performed to investigate the effect of different weather conditions, traffic situation and driving behavior on the behavior of this system.
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Date Issued: 2016
Identifier: CFE0006830, ucf:51797
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006830

Title: Application of Multiaxial Cyclic Loading for Constitutive Model and Parameter Determination of Steels.
Creator: Felemban, Bassem, Gordon, Ali, Moslehy, Faissal, Bai, Yuanli, Nam, Boo Hyun, University of Central Florida
Abstract / Description: For many candidate materials, constitutive models and their parameters are identified using uniaxial test data. Real components, however, generally operate in a multi-axial loading environments. Consequently, constitutive models deployed by uniaxial conditions may carry over to service conditions with inherit limitations. Research is proposed to determine the constitutive model constants for the creep and plasticity responses of a material via multi-axial fatigue testing which may contain...
Show moreFor many candidate materials, constitutive models and their parameters are identified using uniaxial test data. Real components, however, generally operate in a multi-axial loading environments. Consequently, constitutive models deployed by uniaxial conditions may carry over to service conditions with inherit limitations. Research is proposed to determine the constitutive model constants for the creep and plasticity responses of a material via multi-axial fatigue testing which may contain ratcheting. It is conjectured that directly regressing data under conditions that favor those of actual service use will lead to more accurate modeling under these conditions, as well as a reduced consumption of model development resources. Application of observations of multiaxial loading in the determination of constitutive modeling constants and model selection represents a paradigm shift for material characterization. Numerical simulation and experimentation are necessary for material selection for application at high temperature. The candidate material used in this study is primarily applied for structural components in high-temperature environments for steam generating systems (-) 304 stainless steel. It confers an excellent balance of ductility, corrosion resistance, and creep resistance at moderate temperatures (i.e., up to 550?C). Under service conditions, both creep and cyclic plasticity can occur under either isothermal or non-isothermal conditions. Accurate deformation modeling and life prediction of these structures only achieved with an accurate understanding of how this and other key alloys behave under complex conditions. This research conveys a proposed methodology that can be used to apply creep and plasticity constitutive models that correlate with experimental data. Several creep and plasticity models are examined to augment the accuracy of the models. These results are presented to illustrate modeling performance. Based on this idea has been determined that novel methods of measuring the accuracy of modeling be needed, as well as methods for optimizing material response under multiaxial conditions. The models are applied under service-like conditions to gain an understanding of how this and other key alloys behave under complex conditions. This research will study the complex tensile-torsion loading to determine the constitutive constants for material, and thus will decrease the number of uniaxial experiments. Additionally, combined analytical and experimental methods will be used to establish the Bree diagram for elevated temperature tensile-torsion responses. This deformation mechanism map has been useful as a design tool for materials undergoing ratcheting.
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Date Issued: 2017
Identifier: CFE0006875, ucf:51760
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006875

Title: Understanding Hydroclimatic Controls on Stream Network Dynamics using LiDAR Data.
Creator: Kim, Seoyoung, Wang, Dingbao, Medeiros, Stephen, Nam, Boo Hyun, Singh, Arvind, Sumner, David, University of Central Florida
Abstract / Description: This dissertation investigates the hydroclimatic controls on drainage network dynamics and characterizes the variation of drainage density in various climate regions. The methods were developed to extract the valley and wet channel networks based on Light Detection and Ranging (LiDAR) data including the elevation and intensity of laser returns. The study watersheds were selected based on the availability of streamflow observations and LiDAR data. Climate aridity index was used as a...
Show moreThis dissertation investigates the hydroclimatic controls on drainage network dynamics and characterizes the variation of drainage density in various climate regions. The methods were developed to extract the valley and wet channel networks based on Light Detection and Ranging (LiDAR) data including the elevation and intensity of laser returns. The study watersheds were selected based on the availability of streamflow observations and LiDAR data. Climate aridity index was used as a quantitative indicator for climate. The climate controls on drainage density were re-visited using watersheds with minimal anthropogenic interferences and compared with the U-shape relationship reported in the previous studies. A curvature-based method was developed to extract a valley network from 1-m LiDAR-based Digital Elevation Models. The relationship between drainage density and climate aridity index showed a monotonic increasing trend and the discrepancy was explained by human interventions and underestimated drainage density due to the coarse spatial resolution (30-meter) of the topographic maps used in previous research. Observations of wet channel networks are limited, especially in headwater catchments in comparison with the importance of stream network expansion and contraction. A systematic method was developed to extract wet channel networks based on the signal intensities of LiDAR ground returns, which are lower on water surfaces than on dry surfaces. The frequency distributions of intensities associated with wet surface and dry surface returns were constructed. With the aid of LiDAR-based ground elevations, signal intensity thresholds were identified for extracting wet channels. The developed method was applied to Lake Tahoe area during recession periods in five watersheds. A power-law relationship between streamflow and wet channel length was obtained and the scaling exponent was consistent with the reported findings from field work in other regions.Perennial streams flow for the most of the time during normal years and are usually defined based on a flow duration threshold. The streamflow characteristics of perennial streams in this research were assessed using the relationship between streamflow exceedance probability and wet channel ratio based on wet channel networks extracted from LiDAR data. Non-dimensional analysis based on the relationship between streamflow exceedance probability and wet channel ratio showed that results were consistent with previous research about perennial stream definition, and provided the possibility to use wet channel ratio to define perennial streams. Wetlands are important natural resources and need to be monitored regularly in order to understand their inundation dynamics, function and health. Wetland mapping is a key part of monitoring programs. A framework for detecting wetland was developed based on LiDAR elevation and intensity information. After masking out densely vegetated areas, wet areas were identified based on signal intensity of ground returns for barrier islands in East-Central Florida. The intensity threshold of wet surface was identified by decomposing composite probability distribution functions using a Gamma mixture model and the Expectation-Maximization algorithm. This method showed good potential for wetland mapping.The methodology developed in this dissertation demonstrated that incorporating LiDAR data into the drainage networks, stream network dynamics and wetlands results in enhanced understanding of hydroclimatic controls on stream network dynamics. LiDAR data provide a rich information source including elevation and intensity, and are of great benefit to hydrologic research community.
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Date Issued: 2016
Identifier: CFE0006532, ucf:51372
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006532

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: Investigation of infrared thermography for subsurface damage detection of concrete structures.
Creator: Hiasa, Shuhei, Catbas, Necati, Tatari, Omer, Nam, Boo Hyun, Zaurin, Ricardo, Xanthopoulos, Petros, University of Central Florida
Abstract / Description: Deterioration of road infrastructure arises from aging and various other factors. Consequently, inspection and maintenance have been a serious worldwide problem. In the United States, degradation of concrete bridge decks is a widespread problem among several bridge components. In order to prevent the impending degradation of bridges, periodic inspection and proper maintenance are indispensable. However, the transportation system faces unprecedented challenges because the number of aging...
Show moreDeterioration of road infrastructure arises from aging and various other factors. Consequently, inspection and maintenance have been a serious worldwide problem. In the United States, degradation of concrete bridge decks is a widespread problem among several bridge components. In order to prevent the impending degradation of bridges, periodic inspection and proper maintenance are indispensable. However, the transportation system faces unprecedented challenges because the number of aging bridges is increasing under limited resources, both in terms of budget and personnel. Therefore, innovative technologies and processes that enable bridge owners to inspect and evaluate bridge conditions more effectively and efficiently with less human and monetary resources are desired. Traditionally, qualified engineers and inspectors implemented hammer sounding and/or chain drag, and visual inspection for concrete bridge deck evaluations, but these methods require substantial field labor, experience, and lane closures for bridge deck inspections. Under these circumstances, Non-Destructive Evaluation (NDE) techniques such as computer vision-based crack detection, impact echo (IE), ground-penetrating radar (GPR) and infrared thermography (IRT) have been developed to inspect and monitor aging and deteriorating structures rapidly and effectively. However, no single method can detect all kinds of defects in concrete structures as well as the traditional inspection combination of visual and sounding inspections; hence, there is still no international standard NDE methods for concrete bridges, although significant progress has been made up to the present.This research presents the potential to reduce a burden of bridge inspections, especially for bridge decks, in place of traditional chain drag and hammer sounding methods by IRT with the combination of computer vision-based technology. However, there were still several challenges and uncertainties in using IRT for bridge inspections. This study revealed those challenges and uncertainties, and explored those solutions, proper methods and ideal conditions for applying IRT in order to enhance the usability, reliability and accuracy of IRT for concrete bridge inspections. Throughout the study, detailed investigations of IRT are presented. Firstly, three different types of infrared (IR) cameras were compared under active IRT conditions in the laboratory to examine the effect of photography angle on IRT along with the specifications of cameras. The results showed that when IR images are taken from a certain angle, each camera shows different temperature readings. However, since each IR camera can capture temperature differences between sound and delaminated areas, they have a potential to detect delaminated areas under a given condition in spite of camera specifications even when they are utilized from a certain angle. Furthermore, a more objective data analysis method than just comparing IR images was explored to assess IR data. Secondly, coupled structural mechanics and heat transfer models of concrete blocks with artificial delaminations used for a field test were developed and analyzed to explore sensitive parameters for effective utilization of IRT. After these finite element (FE) models were validated, critical parameters and factors of delamination detectability such as the size of delamination (area, thickness and volume), ambient temperature and sun loading condition (different season), and the depth of delamination from the surface were explored. This study presents that the area of delamination is much more influential in the detectability of IRT than thickness and volume. It is also found that there is no significant difference depending on the season when IRT is employed. Then, FE model simulations were used to obtain the temperature differences between sound and delaminated areas in order to process IR data. By using this method, delaminated areas of concrete slabs could be detected more objectively than by judging the color contrast of IR images. However, it was also found that the boundary condition affects the accuracy of this method, and the effect varies depending on the data collection time. Even though there are some limitations, integrated use of FE model simulation with IRT showed that the combination can be reduce other pre-tests on bridges, reduce the need to have access to the bridge and also can help automate the IRT data analysis process for concrete bridge deck inspections. After that, the favorable time windows for concrete bridge deck inspections by IRT were explored through field experiment and FE model simulations. Based on the numerical simulations and experimental IRT results, higher temperature differences in the day were observed from both results around noontime and nighttime, although IRT is affected by sun loading during the daytime heating cycle resulting in possible misdetections. Furthermore, the numerical simulations show that the maximum effect occurs at night during the nighttime cooling cycle, and the temperature difference decreases gradually from that time to a few hours after sunrise of the next day. Thus, it can be concluded that the nighttime application of IRT is the most suitable time window for bridge decks. Furthermore, three IR cameras with different specifications were compared to explore several factors affecting the utilization of IRT in regards to subsurface damage detection in concrete structures, specifically when the IRT is utilized for high-speed bridge deck inspections at normal driving speeds under field laboratory conditions. The results show that IRT can detect up to 2.54 cm delamination from the concrete surface at any time period. This study revealed two important factors of camera specifications for high-speed inspection by IRT as shorter integration time and higher pixel resolution.Finally, a real bridge was scanned by three different types of IR cameras and the results were compared with other NDE technologies that were implemented by other researchers on the same bridge. When compared at fully documented locations with 8 concrete cores, a high-end IR camera with cooled detector distinguished sound and delaminated areas accurately. Furthermore, indicated location and shape of delaminations by three IR cameras were compared to other NDE methods from past research, and the result revealed that the cooled camera showed almost identical shapes to other NDE methods including chain drag. It should be noted that the data were collected at normal driving speed without any lane closures, making it a more practical and faster method than other NDE technologies. It was also presented that the factor most likely to affect high-speed application is integration time of IR camera as well as the conclusion of the field laboratory test.The notable contribution of this study for the improvement of IRT is that this study revealed the preferable conditions for IRT, specifically for high-speed scanning of concrete bridge decks. This study shows that IRT implementation under normal driving speeds has high potential to evaluate concrete bridge decks accurately without any lane closures much more quickly than other NDE methods, if a cooled camera equipped with higher pixel resolution is used during nighttime. Despite some limitations of IRT, the data collection speed is a great advantage for periodic bridge inspections compared to other NDE methods. Moreover, there is a high possibility to reduce inspection time, labor and budget drastically if high-speed bridge deck scanning by the combination of IRT and computer vision-based technology becomes a standard bridge deck inspection method. Therefore, the author recommends combined application of the high-speed scanning combination and other NDE methods to optimize bridge deck inspections.
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Date Issued: 2016
Identifier: CFE0006323, ucf:51575
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0006323

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: Sustainable Material Solution for Flexible Pavements; Performance Evaluation and Impact Assessment of Utilizing Multiple Recycled Materials in HMA.
Creator: Golestani, Behnam, Nam, Boo Hyun, Chopra, Manoj, Tatari, Omer, Bai, Yuanli, University of Central Florida
Abstract / Description: The demand for pollution-free and recyclable engineering materials has been increased as the cost of energy and environmental concerns have risen. Green material design can lead to better environmental quality and sustainability of civil infrastructure. Road construction is one of the largest consumers of natural resources. Beneficial utilization of recycled materials can result in an important opportunity to save the mining and use of virgin materials, to preserve energy, and to save...
Show moreThe demand for pollution-free and recyclable engineering materials has been increased as the cost of energy and environmental concerns have risen. Green material design can lead to better environmental quality and sustainability of civil infrastructure. Road construction is one of the largest consumers of natural resources. Beneficial utilization of recycled materials can result in an important opportunity to save the mining and use of virgin materials, to preserve energy, and to save landfill space. Two main research questions addressed in this study are: (1) How much pollution, energy, natural resources, time and money can be salvaged by applying recycling materials to Hot-Mix Asphalt (HMA)?, (2) What are the optimum mix designs for those recycled materials in HMA?, and (3) Can multiple recycled materials be used at the same time to compensate each other's drawbacks? This study evaluates the structural performance and environmental-economical cost and benefit by substituting one or a combination of three recycled materials in HMA. The three recycled materials are Recycled Asphalt Shingle (RAS), Municipal Solid Waste Incineration (MSWI) Bottom Ash, and Recycled Concrete Aggregate (RCA). Performance evaluation of the HMA including those recycled materials has been performed by a series of laboratory experimental tests while the environmental impact was investigated by the Life Cycle Assessment (LCA). In addition, Life Cycle Cost Analysis (LCCA) method has been employed to evaluate the benefit of the aforementioned recycled materials.In 2008, the Florida Legislature established a new statewide recycling goal of 75% to be achieved by the year 2020. The impact of this research aligns with this policy as it introduces a sustainable HMA that reduces the necessity of virgin aggregate and asphalt binder to 50% and 20%, respectively. In terms of environmental and economic impacts, in comparison with the regular HMA, it generates 25% less greenhouse gas emission, and for a period of 20 years, the cost of construction and maintenance would be 65% less.
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Date Issued: 2015
Identifier: CFE0005798, ucf:50038
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005798

Title: Beneficial Utilization of Municipal Solid Waste Incineration Ashes as Sustainable Road Construction Materials.
Creator: Tasneem, Kazi, Nam, Boo Hyun, Chopra, Manoj, Reinhart, Debra, Sohn, Yongho, University of Central Florida
Abstract / Description: Incineration of municipal solid waste (MSW) is common for energy recovery, and management of municipal solid waste incineration (MSWI) ashes has received a growing attention around the world. In the U.S., generation of MSW has increased up to 65% since 1980, to the current level of 251 million tons per year with 53.8% landfilled, 34.5% recycled and composted, and 11.7% incinerated with energy recovery. In the process of incineration, MSWI ash is being produced as byproducts; about 80 to 90%...
Show moreIncineration of municipal solid waste (MSW) is common for energy recovery, and management of municipal solid waste incineration (MSWI) ashes has received a growing attention around the world. In the U.S., generation of MSW has increased up to 65% since 1980, to the current level of 251 million tons per year with 53.8% landfilled, 34.5% recycled and composted, and 11.7% incinerated with energy recovery. In the process of incineration, MSWI ash is being produced as byproducts; about 80 to 90% of the MSWI ash is bottom ash (BA) and 10 to 20% is fly ash (FA) by weight. The current practice of the U.S. is to combine both BA and FA to meet the criteria to qualify as non-hazardous, and all combined ashes are disposed in landfills.European countries have utilized MSWI BA as beneficial construction materials by separating it from FA. The FA is mostly limited to landfill disposal as hazardous material due to its high content of toxic elements and salts. BA has been actively recycled in the areas of roadbed, asphalt paving, and concrete products in many of 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 of MSWI ashes instead of landfill disposal. Moreover, many studies have demonstrated the beneficial use of MSWI ashes as engineering materials with minimum environmental impacts.On the other hand, the U.S. has shown a lack of consistent and effective management plans, as well as environmental regulations for the use of MSWI ashes., Due to persistent uncertainty of engineering properties and inconsistency in the Federal and State regulations in the U.S., however, the recycling of the MSWI ashes has been hindered and they are mostly disposed in landfills.In this research work, current management practice, existing regulations, and environmental consequences of MSWI ashes utilization are comprehensively reviewed worldwide and nationwide with an emphasis of the potential area of its utilization in asphalt paving and concrete product. This research also entails a detailed chemical and microstructural characterization of MSWI BA and FA produced from a Refuse Derived Fuel (RDF) facility in Florida so that the MSWI ash is well characterized for its beneficial uses as construction materials.The material characterization includes Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and X-ray Diffraction (XRD) techniques. In addition, leaching experiments have been conducted to investigate the environmental properties (e.g. leachate concentration) of BA and ash-mixed hot mix asphalt (HMA) and Portland cement concrete (PCC). Leaching results reveals the reduced leaching potential of toxic material from MSWI ashes while incorporated in HMA and PCC. Lastly, a preliminary experimental approach has been devised for the vitrification of FA which is a promising thermal process of transferring material into glassy state with higher physical and chemical integrity to reduce toxicity so that utilization of FA can be possible.
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Date Issued: 2014
Identifier: CFE0005425, ucf:50404
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005425

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

Title: Use of Accelerated Calcite Precipitation Method to Investigate Calcium Carbonate Precipitation from Recycled Concrete for Drainage System Applications.
Creator: McCulloch, Toni, Nam, Boo Hyun, Chopra, Manoj, Arboleda Monsalve, Luis, An, Jin Woo, Filler, Dennis, University of Central Florida
Abstract / Description: Recycled concrete aggregate (RCA) is a sustainable construction material that is a viable option for use in drainage systems by replacing virgin aggregate. Replacing virgin aggregate with RCA is beneficial from both economic and environmental perspectives. However, the use of RCA as pipe backfill materials may cause a long-term performance issue such as potential clogging due to fines accumulation and calcite precipitation on filter fabric. Previous studies investigated the long-term...
Show moreRecycled concrete aggregate (RCA) is a sustainable construction material that is a viable option for use in drainage systems by replacing virgin aggregate. Replacing virgin aggregate with RCA is beneficial from both economic and environmental perspectives. However, the use of RCA as pipe backfill materials may cause a long-term performance issue such as potential clogging due to fines accumulation and calcite precipitation on filter fabric. Previous studies investigated the long-term performance of RCA regarding flow rate. Therefore, this study investigated calcite precipitation potential of RCA. The Accelerated Calcite Precipitation (ACP) procedure was devised and used to estimate (")life-time(") calcite precipitation of RCA for French Drains. The ACP procedure was studied further and improved to optimize the calcite precipitation procedure. The enhanced method was used to compare the calcite precipitation of limestone and RCA samples - sources with varying chemistry and history. Key findings are (1) the clogging due to calcite precipitation of RCA is not as significant as clogging due to the existing and/or accumulated fines, (2) the calcite precipitation can be increased with a temperature of 75(&)deg;C and 17-hour heating time, and (3) the potential for calcite precipitation from RCA is not as significant as limestone for Type I underdrain gradation.
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Date Issued: 2018
Identifier: CFE0007321, ucf:52132
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0007321

Title: Development of Traffic Safety Zones and Integrating Macroscopic and Microscopic Safety Data Analytics for Novel Hot Zone Identification.
Creator: Lee, JaeYoung, Abdel-Aty, Mohamed, Radwan, Ahmed, Nam, Boo Hyun, Kuo, Pei-Fen, Choi, Keechoo, University of Central Florida
Abstract / Description: Traffic safety has been considered one of the most important issues in the transportation field. With consistent efforts of transportation engineers, Federal, State and local government officials, both fatalities and fatality rates from road traffic crashes in the United States have steadily declined from 2006 to 2011.Nevertheless, fatalities from traffic crashes slightly increased in 2012 (NHTSA, 2013). We lost 33,561 lives from road traffic crashes in the year 2012, and the road traffic...
Show moreTraffic safety has been considered one of the most important issues in the transportation field. With consistent efforts of transportation engineers, Federal, State and local government officials, both fatalities and fatality rates from road traffic crashes in the United States have steadily declined from 2006 to 2011.Nevertheless, fatalities from traffic crashes slightly increased in 2012 (NHTSA, 2013). We lost 33,561 lives from road traffic crashes in the year 2012, and the road traffic crashes are still one of the leading causes of deaths, according to the Centers for Disease Control and Prevention (CDC). In recent years, efforts to incorporate traffic safety into transportation planning has been made, which is termed as transportation safety planning (TSP). The Safe, Affordable, Flexible Efficient, Transportation Equity Act (-) A Legacy for Users (SAFETEA-LU), which is compliant with the United States Code, compels the United States Department of Transportation to consider traffic safety in the long-term transportation planning process. Although considerable macro-level studies have been conducted to facilitate the implementation of TSP, still there are critical limitations in macroscopic safety studies are required to be investigated and remedied. First, TAZ (Traffic Analysis Zone), which is most widely used in travel demand forecasting, has crucial shortcomings for macro-level safety modeling. Moreover, macro-level safety models have accuracy problem. The low prediction power of the model may be caused by crashes that occur near the boundaries of zones, high-level aggregation, and neglecting spatial autocorrelation.In this dissertation, several methodologies are proposed to alleviate these limitations in the macro-level safety research. TSAZ (Traffic Safety Analysis Zone) is developed as a new zonal system for the macroscopic safety analysis and nested structured modeling method is suggested to improve the model performance. Also, a multivariate statistical modeling method for multiple crash types is proposed in this dissertation. Besides, a novel screening methodology for integrating two levels is suggested. The integrated screening method is suggested to overcome shortcomings of zonal-level screening, since the zonal-level screening cannot take specific sites with high risks into consideration. It is expected that the integrated screening approach can provide a comprehensive perspective by balancing two aspects: macroscopic and microscopic approaches.
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Date Issued: 2014
Identifier: CFE0005195, ucf:50653
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005195

Title: Analytical study of computer vision-based pavement crack quantification using machine learning techniques.
Creator: Mokhtari, Soroush, Yun, Hae-Bum, Nam, Boo Hyun, Catbas, Necati, Shah, Mubarak, Xanthopoulos, Petros, University of Central Florida
Abstract / Description: Image-based techniques are a promising non-destructive approach for road pavement condition evaluation. The main objective of this study is to extract, quantify and evaluate important surface defects, such as cracks, using an automated computer vision-based system to provide a better understanding of the pavement deterioration process. To achieve this objective, an automated crack-recognition software was developed, employing a series of image processing algorithms of crack extraction, crack...
Show moreImage-based techniques are a promising non-destructive approach for road pavement condition evaluation. The main objective of this study is to extract, quantify and evaluate important surface defects, such as cracks, using an automated computer vision-based system to provide a better understanding of the pavement deterioration process. To achieve this objective, an automated crack-recognition software was developed, employing a series of image processing algorithms of crack extraction, crack grouping, and crack detection. Bottom-hat morphological technique was used to remove the random background of pavement images and extract cracks, selectively based on their shapes, sizes, and intensities using a relatively small number of user-defined parameters. A technical challenge with crack extraction algorithms, including the Bottom-hat transform, is that extracted crack pixels are usually fragmented along crack paths. For de-fragmenting those crack pixels, a novel crack-grouping algorithm is proposed as an image segmentation method, so called MorphLink-C. Statistical validation of this method using flexible pavement images indicated that MorphLink-C not only improves crack-detection accuracy but also reduces crack detection time.Crack characterization was performed by analysing imagerial features of the extracted crack image components. A comprehensive statistical analysis was conducted using filter feature subset selection (FSS) methods, including Fischer score, Gini index, information gain, ReliefF, mRmR, and FCBF to understand the statistical characteristics of cracks in different deterioration stages. Statistical significance of crack features was ranked based on their relevancy and redundancy. The statistical method used in this study can be employed to avoid subjective crack rating based on human visual inspection. Moreover, the statistical information can be used as fundamental data to justify rehabilitation policies in pavement maintenance.Finally, the application of four classification algorithms, including Artificial Neural Network (ANN), Decision Tree (DT), k-Nearest Neighbours (kNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is investigated for the crack detection framework. The classifiers were evaluated in the following five criteria: 1) prediction performance, 2) computation time, 3) stability of results for highly imbalanced datasets in which, the number of crack objects are significantly smaller than the number of non-crack objects, 4) stability of the classifiers performance for pavements in different deterioration stages, and 5) interpretability of results and clarity of the procedure. Comparison results indicate the advantages of white-box classification methods for computer vision based pavement evaluation. Although black-box methods, such as ANN provide superior classification performance, white-box methods, such as ANFIS, provide useful information about the logic of classification and the effect of feature values on detection results. Such information can provide further insight for the image-based pavement crack detection application.
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Date Issued: 2015
Identifier: CFE0005671, ucf:50186
Format: Document (PDF)
PURL: http://purl.flvc.org/ucf/fd/CFE0005671

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Nam, Boo Hyun (40)	+ -
University of Central Florida (40)	+ -
Chopra, Manoj (15)	+ -
Wang, Dingbao (9)	+ -
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Singh, Arvind (4)	+ -
An, Jin Woo (3)	+ -
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