During the last two decades, environmental awareness and climate change concerns have encouraged and supported the implementation of recycled techniques in the Transportation Infrastructure Industry for rehabilitating and constructing pavements in the United States. Besides that, pavement roads are public goods that bring economic and social benefits to all countries. Therefore, assessing the pavement structural condition is essential to understand the performance of new materials and determine actions for conservation, maintenance, or rehabilitation. In-situ Pavement monitoring through embedded instrumentation is a type of monitoring technique, which uses several sensors installed within the pavement to obtain the structural responses used in Mechanical-Empirical design to control the performance and define asset management plans. This thesis presents the instrumented response of a Recycled Pavement Section on the Interstate 64 (located in Virginia, USA) to analyze the actual pavement responses (strain and stress) under real traffic and environmental conditions. Several sensors were installed during the construction (including strain gauges, pressure cells, thermocouples, and TDR probes), and two recycling techniques were used (CCPR and Full Depth Reclamation (FDR)) in this project. The Instrumented Recycled Pavement Section analyzed in this research was tested during five months in 2019 to evaluate the effect of temperature, sensor location, and load configuration on the pavement responses collected in the field. During the tests, three loaded trucks ran over the instrumented section. The results showed that the pavement structure is working properly, the stress responses decreased with depth, the maximum strain over the months was compared, and the temperature effect was addressed. Nevertheless, the stress and strain data obtained in each test presented a large variability because it is difficult to control the position where the trucks are passing during this type of experiment. Furthermore, the measured strains were useful to develop a calibrated pavement structural model, which showed that the pavement is expected to have a long structural service life. / M.S. / During the last two decades, different Departments of Transportation have been studying the implementation of recycled materials in pavement structure to provide better economic, environmental, and social benefits by addressing environmental challenges within the Transportation Infrastructure Industry. Among the emerging recycled techniques, Cold-Central Plant Recycling (CCPR) and Full Depth Reclamation (FDR) are included. Both procedures recollect and use the existing asphalt in the rehabilitation or reconstruction of the new pavement structure. The main benefits of pavement recycled materials include reduction of raw materials required and gas emissions. Nevertheless, recycled techniques are not commonly implemented due to the lack of information about long-term performance under real traffic and environmental conditions. In addition, since 2004, when the new Pavement Design Guide was released, the evaluation and validation of new materials require the understanding of the interaction between material properties, traffic, and climate. To address this concern, this thesis analyzed the pavement response measurements obtained in the Interstate 64 Widening Project (Virginia, USA), where two recycling techniques were used (CCPR and FDR). In this project, several sensors were installed during the construction to obtain information regarding the current environment condition (temperature and moisture) and pavement performance (stress and strain). The recycled pavement section was tested during five months of 2019 and trucks with known load configurations were implemented in the field tests. The results showed that the pavement structure is properly working, there is an acceptable stress distribution within the pavement layers, and the overall thickness is expected to have a long structural service life. Besides that, measured strain values obtained through the field experiment were compared with the theoretical ones obtained with computational tools.

Over the past decade, wireless data communication has experienced a phenomenal growth, which is driven by the popularity of wireless devices and the growing number of bandwidth hungry applications. During the same period, various advanced communication technologies have emerged to improve network throughput. Some examples include multi-input multi-output (MIMO), full duplex, cognitive radio, mmWave, among others. An important research direction is to understand the impacts of these new technologies on network throughput performance. Such investigation is critical not only for theoretical understanding, but also can be used as a guideline to design algorithms and network protocols in the field. The goal of this dissertation is to understand the impact of some advanced technologies on network throughput performance. More specifically, we investigate the following three technologies: MIMO, full duplex, and mmWave communication. For each technology, we explore the performance envelope of wireless networks by studying a throughput maximization problem. / Ph. D.

State highway agencies are searching for more cost-effective methods of rehabilitating roads. One sustainable solution is full-depth reclamation (FDR), a pavement rehabilitation technique that involves pulverizing and reusing materials from existing distressed pavements in place. There is, however, limited information on the long-term properties of these recycled materials. One important property, the elastic modulus, indicates the structural capacity of pavement materials and is highly recommended for design purposes by the Mechanistic Empirical Pavements Design Guide (MEPDG). The elastic modulus directly impacts selection of the overall pavement thickness, and an accurate estimation of the modulus is therefore key to a cost-effective pavement design. This thesis researched the modulus trends and functional properties of three in-service pavements rehabilitated with the FDR technique during the 2008 Virginia Department of Transportation (VDOT) construction season. Foamed asphalt (2.7% with 1% cement), asphalt emulsion (3.5%), and Portland cement (5%) were used as stabilizing agents for the FDR layers. Several deflection tests and distress surveys were conducted for the pavement sections before and after construction. An automated road analyzer (ARAN) was used to collect distress data over a period of 7 years. Deterioration models were developed to predict the durability of differently stabilized FDR pavements and compared to reference sections rehabilitated with traditional asphalt concrete (AC) overlays. The results of the moduli measured for the recycled base materials varied significantly over time. These changes were attributed to curing after construction, seasonal effects, and subgrade moisture. The structural capacity of the pavements improved irrespective of the stabilizing agent used. Rutting was higher for the foamed asphalt and emulsion sections. The International Roughness Index (IRI) was better for the cement stabilized sections compared asphalt stabilized sections. The Critical Condition Index (CCI) was similar for all treatments at the end of the evaluation period. The durability of the sections was comparable, with the cement stabilized FDR sections slightly outperforming the asphalt stabilized sections. / Master of Science / Replacing all roads in bad condition with new reconstruction or with traditional rehabilitation alternatives such as the mill and overlay will cost state highway agencies (SHAs) huge sums of funds. State departments of transportation are therefore seeking cost-effective ways to rehabilitate roads under their jurisdiction. An innovative technique being used by several SHAs today is full depth reclamation (FDR) which involves breaking down an existing roadway and immediately reusing the materials to construct a strengthened base layer for a new road. Despite the increasing use of FDR in recent years, several questions remain unanswered regarding the behavior of the strengthened base materials and their performance in the long-term under traffic loads. The elastic modulus is one material property that indicates the strength or structural capacity of pavement materials and usually impacts the selection of the overall thickness of the roadway. This thesis researched the modulus trends and functional properties of three in-service roadways rehabilitated with the FDR technique in 2008 by the Virginia Department of Transportation. Foamed asphalt (2.7% with 1% cement), asphalt emulsion (3.5%), and Portland cement (5%) were used to strengthen the FDR base layers. Several deflection tests and distress surveys were conducted for the pavement sections before and after construction. The moduli measured for the recycled base materials varied significantly over time. These changes were attributed to curing after construction, seasonal effects, and subgrade moisture. Long term performance monitoring of the projects showed that rutting was higher for the foamed asphalt and emulsion sections. The International Roughness Index (IRI), which gives an indication of the overall ride quality i.e. how smooth the pavement surface is, was better for the cement stabilized FDR sections compared to the asphalt stabilized counterparts. The structural capacity of the pavements improved irrespective of the stabilizing treatment used. The Critical Condition Index (CCI) was similar for all treatments at the end of the evaluation period. The durability of the sections was comparable, with the cement stabilized sections projected to last slightly longer than asphalt sections.

State highway agencies are searching for more cost-effective methods of rehabilitating roads. One sustainable solution is full-depth reclamation (FDR), a pavement rehabilitation technique that involves pulverizing and reusing materials from existing distressed pavements in place. There is, however, limited information on the long-term properties of these recycled materials. One important property, the elastic modulus, indicates the structural capacity of pavement materials and is highly recommended for design purposes by the Mechanistic Empirical Pavements Design Guide (MEPDG). The elastic modulus directly impacts selection of the overall pavement thickness, and an accurate estimation of the modulus is therefore key to a cost-effective pavement design. This thesis researched the modulus trends and functional properties of three in-service pavements rehabilitated with the FDR technique during the 2008 Virginia Department of Transportation (VDOT) construction season. Foamed asphalt (2.7% with 1% cement), asphalt emulsion (3.5%), and Portland cement (5%) were used as stabilizing agents for the FDR layers. Several deflection tests and distress surveys were conducted for the pavement sections before and after construction. An automated road analyzer (ARAN) was used to collect distress data over a period of 7 years. Deterioration models were developed to predict the durability of differently stabilized FDR pavements and compared to reference sections rehabilitated with traditional asphalt concrete (AC) overlays. The results of the moduli measured for the recycled base materials varied significantly over time. These changes were attributed to curing after construction, seasonal effects, and subgrade moisture. The structural capacity of the pavements improved irrespective of the stabilizing agent used. Rutting was higher for the foamed asphalt and emulsion sections. The International Roughness Index (IRI) was better for the cement stabilized sections compared asphalt stabilized sections. The Critical Condition Index (CCI) was similar for all treatments at the end of the evaluation period. The durability of the sections was comparable, with the cement stabilized FDR sections slightly outperforming the asphalt stabilized sections. / Master of Science

This thesis proposes a deep learning approach to predict stress at various stages of mechanical loading in 2-D representations of fiber-reinforced composites. More specifically, the full-field stress distribution at elastic and at an early stage of damage initiation is predicted based on the microstructural geometry. The required data set for the purposes of training and validation are generated via high-fidelity simulations of several randomly generated microstructural representations with complex geometries. Two deep learning approaches are employed and their performances are compared: fully convolutional generator and Pix2Pix translation. It is shown that both the utilized approaches can well predict the stress distributions at the designated loading stages with high accuracy. / M.S. / Fiber-reinforced composites are material types with excellent mechanical performance. They form the major material in the construction of space shuttles, aircraft, fancy cars, etc., the structures that are designed to be lightweight and at the same time extremely stiff and strong. Due to the broad application, especially in the sensitives industries, fiber-reinforced composites have always been a subject of meticulous research studies. The research studies to better understand the mechanical behavior of these composites has to be conducted on the micro-scale. Since the experimental studies on micro-scale are expensive and extremely limited, numerical simulations are normally adopted. Numerical simulations, however, are complex, time-consuming, and highly computationally expensive even when run on powerful supercomputers. Hence, this research aims to leverage artificial intelligence to reduce the complexity and computational cost associated with the existing high-fidelity simulation techniques. We propose a robust deep learning framework that can be used as a replacement for the conventional numerical simulations to predict important mechanical attributes of the fiber-reinforced composite materials on the micro-scale. The proposed framework is shown to have high accuracy in predicting complex phenomena including stress distributions at various stages of mechanical loading.

Both the identification and validation of expedient portland cement concrete (PCC) repair technologies have been the focus of the pavements research community for decades due to ever decreasing construction timelines. Precast concrete panel technology offers a potential repair alternative to conventional cast-in-place PCC because the panel is fully cured and has gained full strength prior to its use. This repaired surface may be trafficked immediately, thus eliminating the need for long curing durations required for conventional PCC. The literature reveals a number of precast PCC panel investigations in the past 50 years; however precast technology has only recently gained acceptance and increased use in the US for highway pavements. Furthermore, only limited information regarding performance of airfield applications is available. Following a review of the available technologies, an existing panel prototype was redesigned to allow for both single- and multiple-panel repairs. A series of various sized repairs were conducted in a full-scale airfield PCC test section. Results of accelerated testing indicated that precast panels were suitable for airfield repairs, withstanding between 5,000 and 10,000 passes of C-17 aircraft traffic prior to failure. Failure was due to spalling of the transverse doweled joints. The load transfer characteristics of the transverse joint were studied to determine if the joint load test could be used to predict failure. Results showed that the load transfer efficiency calculations from the joint load test data were not useful for predicting failure; however differential deflections could possibly be applied. Additionally, the practice of filling the joints with rapid-setting grout may have resulted in higher measurements of load transfer efficiency. To determine the stresses generated in the doweled joint, three-dimensional finite element analyses were conducted. Results indicated that the dowel diameter should be increased to reduce stresses and to improve repair performance. Finally, the precast repair technology was compared to other expedient repair techniques in terms of repair speed, performance, and cost. Compared to other methods, the precast panel repair alternative provided similar return-to-service timelines and traffic performance at a slightly higher cost. Costs can be minimized through modification to the panel design and by fabricating panels in a precast facility. Modifications to the system design and placement procedures are also recommended to improve the field performance of the panels. / Ph. D.

The bi-directional dc/dc converter is a very popular and effective tool for alternative energy applications. One way it can be utilized is to charge and discharge batteries used in residential solar energy systems. In the day, excess power from the PV panels is used to charge the batteries. During the night, the charged batteries will power the dc bus for loads in the house such as home appliances. The dual active bridge (DAB) converter is very useful because of its high power capability and efficiency. Its symmetry is effective in transferring power in both directions. However, the DAB converter has drawbacks in the start-up stage. These drawbacks in boost mode include high in-rush current during start-up, and the fact that the high side voltage cannot be lower than the low side voltage. A popular existing method to alleviate this problem is the use of an active clamp and a flyback transformer in the circuit topology to charge the high side before the converter is switched into normal boost operation. The active clamp not only helps eliminate the transient spike caused by the transformer leakage, but also continues to be used during steady state. However, this method introduces a new current spike occurring when the converter transitions from start-up mode to boost mode. To alleviate this new setback, an additional transitional stage is proposed to significantly reduce the current spike without the use of any additional components. The converter is current-fed on the low side, and voltage-fed on the high side. A simple phase shift control is used in buck mode and PWM control is used during the boost mode for both the start-up mode and the normal boost operation. This thesis discusses the performance results of a 48-400 V dc/dc converter with 1000 W power output. / Master of Science

Full Waveform Inversion (FWI) is a widely used optimization technique for subsurface imaging where the goal is to estimate the seismic wave velocity beneath the Earth's surface from the observed seismic data at the surface. The problem is primarily governed by the wave equation, which is a non-linear second-order partial differential equation. A number of approaches have been developed for FWI including physics-based iterative numerical solvers as well as data-driven machine learning (ML) methods. Existing numerical solutions to FWI suffer from three major challenges: (1) sensitivity to initial velocity guess (2) non-convex loss landscape, and (3) sensitivity to noise. Additionally, they suffer from high computational cost, making them infeasible to apply in complex real-world applications. Existing ML solutions for FWI only solve for the inverse and are prone to yield non-unique solutions. In this work, we propose to solve both forward and inverse problems jointly to alleviate the issue of non-unique solutions for an inverse problem. We study the FWI problem from a new perspective and propose a novel approach based on Invertible Neural Networks. This type of neural network is designed to learn bijective mappings between the input and target distributions and hence they present a potential solution to solve forward and inverse problems jointly. In this thesis, we developed a data-driven framework that can be used to learn forward and inverse mappings between any arbitrary input and output space. Our model, Invertible X-net, can be used to solve FWI to obtain high-quality velocity images and also predict the seismic waveforms data. We compare our model with the existing baseline mod- els and show that our model outperforms them in velocity reconstruction on the OpenFWI dataset. Additionally, we also compare the predicted waveforms with a baseline and ground truth and show that our model is capable of predicting highly accurate seismic waveforms simultaneously. / Master of Science / Recent advancements in deep learning have led to the development of sophisticated methods that can be used to solve scientific problems in many disciplines including medical imaging, geophysics, and signal processing. For example, in geophysics, we study the internal structure of the Earth from indirect physical measurements. Often, these kind of problems are challenging due to existence of non-unique and unstable solutions. In this thesis, we look at one such problem called Full Waveform Inversion which aims to estimate velocity of mechanical wave inside the Earth from wave amplitude observations on the surface. For this problem, we explore a special class of neural networks that allows to uniquely map the input and output space and thus alleviate the non-uniqueness and instability in performing Full Waveform Inversion for seismic imaging.

This thesis focuses on the design issues of the multiple-output boost full-bridge converter, which is constructed by cascading the boost regulator with the inductor-less full-bridge converter. The design of the boost regulator has been proposed briefly with component selection and compensator design. After that, the inductor-less full-bridge converter is analyzed extensively. In the first place, the operation principle of the inductor-less full-bridge converter is introduced. Later, the effect of parasitic resistance and inductance is analyzed in an L-R series circuit model as step-response, which relates the drop of output voltage to the load current. Then, the effects of the dc blocking capacitor for the unbalanced load condition and unbalanced duty cycle are tackled. The theoretical results are compared with the experimental results and the simulation results to verify the relationship between the output voltage drop and load current. The overall efficiency of the converter is tested under various conditions. The design of the planar transformer is critical to limit the profile of the converter and the leakage phenomenon. A planar transformer fit for the inductor-less full-bridge converter is designed and analyzed in 3D FEA software. An N-port transformer model is proposed to implement the inductance matrix into the leakage inductance matrix for circuit analysis. Based on this N-port model several measurements to extract the parameters in this model are proposed, where only the impedance analyzer is needed. Finally, the effects of trace layout and encapsulation on breakdown voltage in PCB are summarized from experimental results. / Master of Science

Steel moment frames are a popular seismic-force resisting system, but it is believed that they are susceptible to early fracture if there is a stress concentration in the plastic hinge region, also known as the protected zone. If a defect is present in this area, it may be repaired by grinding and/or welding, but little research has investigated how the repairs affect the performance of full-scale moment connections subjected to inelastic rotations. Thus, the goals of this research were to establish the performance of full-scale moment connections with repairs and defects, then develop a method for predicting fracture of the full-scale specimens using more economical cyclic bend tests. To do this, six full-scale reduced beam section (RBS) connections were tested having arrays of repairs or defects applied to the flanges. The repairs were 0.125 in. deep notches ground to a smooth taper and 0.25 in. deep notches ground to a smooth taper, welded, and ground smooth. The defects were sharp 0.25 in. and 0.375 in. notches. In addition, 54 bend tests were conducted on beam flange and bar stock coupons having the same repairs and defects, power actuated fasteners, puddle welds, and no artifacts. Finally, Coffin-Manson low-cycle fatigue relationships were calibrated using results from the cyclic bend tests with each artifact (repair, defect, or attachment method) and used in conjunction with estimates of full-scale plastic strain amplitudes to predict fracture of full-scale specimens. All four of the full-scale moment connections with repairs satisfied special moment frame qualification criteria (SMF). One full-scale specimen with sharp 0.25 in. notches satisfied SMF qualification criteria, but the flexural resistance dropped rapidly after the qualification cycle. On the other hand, the specimen with sharp 0.375 in. notches did not satisfy SMF qualification criteria due to ductile fractures propagating from the notches. The proposed method for predicting fracture of full-scale connections was validated using the six current and six previous full-scale RBS specimens. This method underpredicted fracture for eleven of the twelve specimens. The ratio of the actual to predicted cumulative story drift at fracture had a mean of 1.13 and a standard deviation of 0.19. / M.S. / Moment connections in steel structures resist earthquake loads by permanently deforming the material near the connection. This area is called the protected zone and is critical to the safety of the structure in an earthquake. Due to this importance, no defects are allowed near the connection, which can include gouges or notches. If a defect does occur, it must repaired by a grinding or welding. These are the required repair methods, but there have be no tests to determine how the repairs affect the strength and ductility of the connection. This research tested six full-scale moment connections with defects repaired by grinding and welding, as well as unrepaired defects. A correlation was also developed and validated between the full-scale tests and small-scale bend tests of steel bars with the same defects and repairs. This relationship is valuable because the small-scale tests are quicker and less expensive to conduct than the full-scale tests, meaning other defects or repairs could be easily tested in the future. All but one of the six full-scale specimens met the strength requirements and had adequate ductility. The one test specimen that failed had an unrepaired defect. The relationship between the full-scale and small-scale tests underpredicted fracture (a conservative estimate) for the five of the full-scale tests and overpredicted fracture (unconservative estimate) for one test.