Browsing by Author "Wang, Linbing"

Now showing 1 - 20 of 111
  • Thumbnail Image
    Acoustic Emission Wave Velocity Measurement of Asphalt Mixture by Arbitrary Wave Method
    Li, Jianfeng; Liu, Huifang; Wang, Wentao; Zhao, Kang; Ye, Zhoujing; Wang, Linbing (MDPI, 2021-09-13)
    The wave velocity of acoustic emission (AE) can reflect the properties of materials, the types of AE sources and the propagation characteristics of AE in materials. At the same time, the wave velocity of AE is also an important parameter in source location calculation by the time-difference method. In this paper, a new AE wave velocity measurement method, the arbitrary wave (AW) method, is proposed and designed to measure the AE wave velocity of an asphalt mixture. This method is compared with the pencil lead break (PLB) method and the automatic sensor test (AST) method. Through comparison and analysis, as a new wave velocity measurement method of AE, the AW method shows the following advantages: A continuous AE signal with small attenuation, no crosstalk and a fixed waveform can be obtained by the AW method, which is more advantageous to distinguish the first arrival time of the acoustic wave and calculate the wave velocity of AE more accurately; the AE signal measured by the AW method has the characteristics of a high frequency and large amplitude, which is easy to distinguish from the noise signal with the characteristics of a low frequency and small amplitude; and the dispersion of the AE wave velocity measured by the AW method is smaller, which is more suitable for the measurement of the AE wave velocity of an asphalt mixture.
  • Thumbnail Image
    Airport Performance Metrics Analysis: Application to Terminal Airspace, Deicing, and Throughput
    Alsalous, Osama (Virginia Tech, 2022-06-08)
    The Federal Aviation Administration (FAA) is continuously assessing the operational performance of the National Airspace System (NAS), where they analyze trends in the aviation industry to help develop strategies for a more efficient air transportation system. To measure the performance of various elements of the aviation system, the FAA and the International Civil Aviation Organization (ICAO) developed nineteen key performance indicators (KPIs). This dissertation contains three research studies, each written in journal format, addressing select KPIs. These studies aim at answering questions that help understand and improve different aspects of airport operational efficiency. In the first study, we model the flight times within the terminal airspace and compare our results with the baseline methodology that the FAA uses for benchmarking. In the second study, we analyze the efficiency of deicing operations at Chicago O'Hare (ORD) by developing an algorithm that analyzes radar data. We also use a simulation model to calculate potential improvements in the deicing operations. Lastly, we present our results of a clustering analysis surrounding the response of airports to demand and capacity changes during the COVID-19 pandemic. The findings of these studies add to literature by providing a methodology that predicts travel times within the last 100 nautical miles with greater accuracy, by providing deicing times per aircraft type, and by providing insight into factors related to airport response to shock events. These findings will be useful for air traffic management decision makers in addition to other researchers in related future studies and airport simulations.
  • Thumbnail Image
    Analysis of Hot Mix Asphalt (HMA) Linear Viscoelastic and Bimodular Properties Using Uniaxial Compression and Indirect Tension (IDT) Tests
    Katicha, Samer (Virginia Tech, 2007-09-07)
    The major Hot-Mix Asphalt (HMA) input for mechanistic-empirical (M-E) flexible pavement design is the dynamic complex modulus obtained from either the uniaxial or triaxial compressive dynamic modulus test. Furthermore, as part of the performance-based mix design process, the triaxial dynamic modulus has been selected to predict rutting and fatigue cracking, and the Indirect Tension (IDT) creep compliance test to predict low-temperature thermal cracking. The creep compliance and dynamic modulus are measured responses (viscoelastic functions) of viscoelastic materials under transient and cyclic loading, respectively. Under the assumptions of linearity, linear viscoelastic functions are equivalent. Moreover, these properties should be the same whether they are obtained from a uniaxial compressive or IDT test. For this dissertation, we tested the applicability of linear viscoelastic (LVE) theory to HMA mixes and determined whether HMA need to be modeled as a bimodular material to analyze IDT creep compliance test results. The need to model HMA as a bimodular material is a result of a number of studies that suggest that HMA tensile and compressive properties are different. A testing program was developed to experimentally measure the uniaxial compression, and IDT creep compliance, and the uniaxial compression dynamic modulus for different HMA mixes. The uniaxial compressive creep compliance and dynamic modulus master curves are constructed and the shift factors obtained from each test are compared. Interconversion between the creep compliance and dynamic modulus experimental results confirm the applicability of LVE theory for the HMA mixes investigated. Based on the applicability of LVE theory, a methodology to determine HMA LVE properties from the combined creep compliance and dynamic modulus test results was developed. As a practical application that is relevant to the M-E flexible pavement design procedure, LVE theory was used and compared to proposed approximate methods to perform the conversion of testing frequency to loading time. Specifically, dynamic modulus results were converted to relaxation modulus, creep compliance, and resilient modulus. Finally, the HMA IDT creep compliance test results at low and intermediate temperature (<20oC) were successfully analyzed using a HMA bimodular material model based on the Ambartsumyan model. The difference between the compressive modulus and the modulus calculated from the IDT test using Hondros' stress distribution is calculated. In addition, a method to determine the compressive-to-tensile modulus ratio using uniaxial compressive and IDT test results is illustrated for one of the tested HMA mixes.
  • Thumbnail Image
    Applying the Newmark Method to the Discontinuous Deformation Analysis
    Peng, Bo (Virginia Tech, 2014-12-08)
    Discontinuous deformation analysis (DDA) is a newly developed simulation method for discontinuous systems. It was designed to simulate systems with arbitrary shaped blocks with high efficiency while providing accurate solutions for energy dissipation. But DDA usually exhibits damping effects that are inconsistent with theoretical solutions. The deep reason for these artificial damping effects has been an open question, and it is hypothesized that these damping effects could result from the time integration scheme. In this thesis two time integration methods are investigated: the forward Euler method and the Newmark method. The work begins by combining the Newmark method and the DDA. An integrated Newmark method is also developed, where velocity and acceleration do not need to be updated. In simulations, two of the most widely used models are adopted to test the forward Euler method and the Newmark method. The first one is a sliding model, in which both the forward Euler method and the Newmark method give accurate solutions compared with analytical results. The second model is an impacting model, in which the Newmark method has much better accuracy than the forward Euler method, and there are minimal damping effects.
  • Thumbnail Image
    Assessment of Vehicle-to-Vehicle Communication based Applications in an Urban Network
    Kim, Taehyoung (Virginia Tech, 2015-06-23)
    Connected Vehicle research has emerged as one of the highest priorities in the transportation systems because connected vehicle technology has the potential to improve safety, mobility, and environment for the current transportation systems. Various connected vehicle based applications have been identified and evaluated through various measurements to assess the performance of connected vehicle applications. However, most of these previous studies have used hypothetical study areas with simple networks for connected vehicle environment. This study represents connected vehicle environment in TRANSIMS to assess the performance of V2V communication applications in the realistic urban network. The communication duration rate and spatial-temporal dispersion of equipped vehicles are investigated to evaluate the capability of V2V communication based on the market penetration rate of equipped vehicles and wireless communication coverage in the whole study area. The area coverage level is used to assess the spatial-temporal dispersion of equipped vehicles for two study areas. The distance of incident information propagation and speed estimation error are used to measure the performance of event-driven and periodic applications based on different market penetration rates of equipped vehicles and wireless communication coverage in both morning peak and non-peak times. The wireless communication coverage is the major factor for event-driven application and the market penetration rate of equipped vehicles has more impact on the performance of periodic application. The required minimum levels of deployment for each application are determined for each scenario. These study findings will be useful for making decisions about investments on deployment of connected vehicle applications to improve the current transportation systems. Notably, event-driven applications can be reliably deployed in the initial stage of deployment despite the low level of market penetration of equipped vehicles.
  • Thumbnail Image
    Atomistic Characterization and Modeling of the Deformation and Failure Properties of Asphalt-Aggregate Interface
    Lu, Yang (Virginia Tech, 2010-04-20)
    This dissertation is dedicated to develop models and methods to bridge atomistic and continuum scales of deformation processes in asphalt-aggregate interfacial composite materials systems. The deformation and failure behaviors, e.g. nanoscale strength, deformation, stiffness, and adhesion/cohesion at asphalt-aggregate interfaces are all evaluated by means of atomistic simulations. The atomistic modeling approach is employed to simulate mechanical properties, which is connected by their common dependence on the nanoscale bonding and their sensitive dependences on mechanics and moisture sensitivity. Specifically, CVFF-aug forcefield is employed in the atomistic calculations to study the fundamental failure processes that appear at the interface as a result of a mechanical deformation. There are five primary aspects to this dissertation. First, the multiscale features of asphalt concrete materials are characterized by using nanoscale characterization & fabrication devices, e.g. High Resolution Optical Microscope (HROM), Environmental Scanning Electron Microscope (ESEM), Transmission Electron Microscope (TEM), Focused Ion Beam (FIB), and Atomistic Force Microscope (AFM). Second, based on the multiscale devices characterization of the interfaces, a 2-layer atomistic bitumen-rock interface structure is constructed. Interface structure evolution under uniaxial tension is performed with various deformation rates. Comparison is made between both theoretical and experimental characterizations of interface configuration. Molecular dynamics (MD) simulations are used to investigate potential relationships between interface structure and morphology. Influences of deformation rate and temperature factors are discussed in terms of interface region stress-strain relation and loading time duration. Third, molecular dynamics simulations are also performed to provide a characterization of atomic scale mechanical behaviors for a 3-layer confined shear structure which leads to interfacial shear failure. In addition, atomistic static simulation approach is employed to calculate a couple of mineral crystals' elastic constants. Furthermore, molecular dynamics simulations are also used to predict the static, thermodynamic, and mechanical properties of three asphalt molecular models. Fourth, the high performance parallel computing technology is extensively employed throughout this dissertation. In addition to use the large-scale MD program, LAMMPS, the author developed a high performance parallel distributive computing program, MPI_multistress, to implement the multiscale understanding/predicting of materials mechanical behaviors. Finally, this research also focuses on the evaluation of the susceptibility of aggregates and asphalts to moisture damage through understanding the nano-mechanisms that influence adhesive bond between aggregates and asphalt, as well as the cohesive strength and moisture susceptibility of the specific asphalt-aggregate interfaces. Surface energy theory and pull-out simulation are used to compute the adhesive bond strength between the aggregates and asphalt, as well as the cohesive bond strength within the binder. In general, this dissertation has focused on the development of nanoscale modeling methods to assess asphalt-aggregate interfacial atomistic deformation and failure behaviors, as well as moisture effects on asphalt mixture strength. Simulation results provide valuable insights into mechanistic details of nanoscale interactions, particularly under conditions of various deformation rates and different temperatures. The results obtained show that a reasonable agreement between the theoretical and pavement industry observations is satisfactory. We conclude that the theoretical calculations presented here are useful in asphalt concrete industry for predicting the mechanical properties of asphalt-aggregate interfaces, which are difficult to obtain experimentally because of their small size.
  • Thumbnail Image
    Aviation Global Demand Forecast Model Development: Air Transportation Demand Distribution and Aircraft Fleet Evolution
    Freire Burgos, Edwin R. (Virginia Tech, 2017-09-08)
    The Portfolio Analysis Management Office (PAMO) for the Aeronautics Research Mission Directorate (ARMD) at NASA Headquarters tasked the Systems Analysis and Concepts Directorate at NASA Langley to combine efforts with Virginia Tech to develop a global demand model with the capability to predict future demand in the air transportation field. A previous study (Alsalous, 2015) started the development of the Global Demand Mode (GDM) to predict air travel demand based on Gross Domestic Product (GDP) and population trends for 3,974 airports worldwide. The study was done from year 2016 to year 2040. This research project intends to enhance the GDM capabilities. A Fratar model is implemented for the distribution of the forecast demand during each year. The Fratar model uses a 3,974 by 3,974 origin-destination matrix to distribute the demand among 55,612 unique routes in the network. Moreover, the GDM is capable to estimate the aircraft fleet mix per route and the number of flights per aircraft that are needed to satisfy the forecast demand. The model adopts the aircraft fleet mix from the Official Airline Guide data for the year 2015. Once the aircraft types are distributed and flights are assigned, the GDM runs an aircraft retirement and replacement analysis to remove older generation aircraft from the network and replace them with existing or newer aircraft. The GDM continues to evolve worldwide aircraft fleet by introducing 14 new generation aircraft from Airbus, Boeing, Bombardier, and Embraer and 5 Advanced Technology Aircraft from NASA.
  • Thumbnail Image
    Balanced asphalt mix design and pavement distress predictive models based on machine learning
    Liu, Jian (Virginia Tech, 2022-09-22)
    Traditional asphalt mix design procedures are empirical and need random and lengthy trials in a laboratory, which can cost much labor, material resources, and finance. The initiative (Material Genome initiative) was launched by President Obama to revitalize American manufacturing. To achieve the objective of the MGI, three major tools which are computational techniques, laboratory experiments, and data analytics methods are supposed to have interacted. Designing asphalt mixture with laboratory and computation simulation methods has developed in recent decades. With the development of data science, establishing a new design platform for asphalt mixture based on data-driven methods is urgent. A balanced mix design, defined as an asphalt mix design simultaneously considering the ability of asphalt mixture to resist pavement distress, such as rutting, cracking, IRI (international roughness index), etc., is still the trend of future asphalt mix design. The service life of asphalt pavement mainly depends on the properties of the asphalt mixture. Whether asphalt mixture has good properties also depends on advanced asphalt mix design methods. Scientific mix design methods can improve engineering properties of asphalt mixture, further extending pavement life and preventing early distress of flexible pavement. Additionally, in traditional asphalt mix design procedures, the capability to resist pavement distress (rutting, IRI, and fatigue cracking) of a mixture is always evaluated based on laboratory performance tests (Hamburg wheel tracking device, Asphalt Pavement Analyzer, repeated flexural bending, etc.). However, there is an inevitable difference between laboratory tests and the real circumstance where asphalt mixture experiences because the pavement condition (traffic, climate, pavement structure) is varying and complex. The successful application examples of machine learning (ML) in all kinds of fields make it possible to establish the predictive models of pavement distress, with the inputs which contain asphalt concrete materials properties involved in the mix design process. Therefore, this study utilized historical data acquired from laboratory records, the LTPP dataset, and the NCHRP 1-37A report, data analytics and processing methods, as well as ML models to establish pavement distress predictive models, and then developed an automated and balanced mix design procedure, further lying a foundation to achieve an MGI mix design in the future. Specifically, the main research content can be divided into three parts:1. Established ML models to capture the relationship between properties of the binder, aggregates properties, gradation, asphalt content (effective and absorbed asphalt content), gyration numbers, and mixture volumetric properties for developing cost-saving Superpave and Marshall mix design methods; 2. Developed pavement distress (rutting, IRI, and fatigue cracking) predictive models, based on the inputs of asphalt concrete properties, other pavement materials information, pavement structure, climate, and traffic; 3. Proposed and verified an intelligent and balanced asphalt mix design procedure by combining the mixture properties prediction module, pavement distress predictive models and criteria, and non-dominated Sorting genetic algorithm-Ⅱ (NSGA-Ⅱ). It was discovered determining total asphalt content through predicting effective and absorbed asphalt content indirectly with ML models was more accurate than predicting total asphalt content directly with ML models; Pavement distress predictive models can achieve better predictive results than the calibrated prediction models of Mechanistic-Empirical Pavement Design Guide (MEPDG); The design results for an actual project of surface asphalt course suggested that compared to the traditional ones, the asphalt contents of the 12.5 mm and 19 mm Nominal Maximum Aggregate Size (NMAS) mixtures designed by the automated mix design procedure drop by 7.6% and 13.2%, respectively; the percent passing 2.36 mm sieve of the two types of mixtures designed by the proposed mix design procedure fall by 17.8% and 10.3%, respectively.
  • Thumbnail Image
    Binder Film Thickness Effect on Aggregate Contact Behavior
    Wang, Dong (Virginia Tech, 2007-08-02)
    This study presents a study on the binder film thickness effect on aggregate contact behavior. As a three-phase material composed of aggregates, asphalt binder and air voids, asphalt mixture could be considered as a visco-elastic material in the low stress level. Since the behavior of the mixture depends largely on the relationship of different components, a well developed contact model for micro-structural modeling is very important for understanding the deformation mechanism of the mixture. In this study, the contact modeling of asphalt mixture was reviewed and the numerical tools used to investigate the micromechanical behavior of asphalt mixture will also be introduced. By using the cabinet x-ray tomography system, the displacement and resistant force of a system of particles bonded by a thin layer binder are measured and recorded. Then, the results are compared with the theoretical solutions of a normal compliance model for a system comprised of two elastic particles bonded by a thin layer of visco-elastic binder. A closed-form time-dependent relationship between the contact forces and the relative particle/binder movements was developed. A reasonable agreement between experiments results and model predicted results is obtained combined with parametric analysis.
  • Thumbnail Image
    Characterization of Bitumen Micro-Mechanical Behaviors Using AFM, Phase Dynamics Theory and MD Simulation
    Hou, Yue; Wang, Linbing; Wang, Dawei; Guo, Meng; Liu, Pengfei; Yu, Jianxin (MDPI, 2017-02-21)
    Fundamental understanding of micro-mechanical behaviors in bitumen, including phase separation, micro-friction, micro-abrasion, etc., can help the pavement engineers better understand the bitumen mechanical performances at macroscale. Recent researches show that the microstructure evolution in bitumen will directly affect its surface structure and micro-mechanical performance. In this study, the bitumen microstructure and micro-mechanical behaviors are studied using Atomic Force Microscopy (AFM) experiments, Phase Dynamics Theory and Molecular Dynamics (MD) Simulation. The AFM experiment results show that different phase-structure will occur at the surface of the bitumen samples under certain thermodynamic conditions at microscale. The phenomenon can be explained using the phase dynamics theory, where the effects of stability parameter and temperature on bitumen microstructure and micro-mechanical behavior are studied combined with MD Simulation. Simulation results show that the saturates phase, in contrast to the naphthene aromatics phase, plays a major role in bitumen micro-mechanical behavior. A high stress zone occurs at the interface between the saturates phase and the naphthene aromatics phase, which may form discontinuities that further affect the bitumen frictional performance.
  • Thumbnail Image
    Characterization of High Porosity Drainage Layer Materials for M-E Pavement Design
    Zhang, Yinning (Virginia Tech, 2015-02-12)
    The objective of this study is to characterize the properties of typically adopted drainage layer materials in VA, OK, and ID. A series of laboratory tests have been conducted to quantify the volumetric properties, permeability and mechanical properties of the laboratory-compacted asphalt treated and cement treated permeable base specimens. The modified test protocols to determine the dynamic modulus of the drainage layer materials have been provided, which can be followed to determine the dynamic modulus of the drainage layers as level 1 input in Mechanistic-Empirical (M-E) pavement design. The measured dynamic moduli have been used to calibrate the original NCHRP 1-37A model to facilitate its application on drainage layer materials for prediction of the dynamic modulus as level 2 input. The compressive strength of the cement treated permeable base mixture of different air void contents has also been quantified in laboratory. Numerical simulations are conducted to investigate the location effects and the contribution of the drainage layer as a structural component within pavement. The optimal air void content of the drainage layer is recommended for Virginia, Oklahoma and Idaho based on the laboratory-determined permeability and the predicted pavement performances during 20-year service life.
  • Thumbnail Image
    Co-Location Decision Tree for Enhancing Decision-Making of Pavement Maintenance and Rehabilitation
    Zhou, Guoqing (Virginia Tech, 2011-01-17)
    A pavement management system (PMS) is a valuable tool and one of the critical elements of the highway transportation infrastructure. Since a vast amount of pavement data is frequently and continuously being collected, updated, and exchanged due to rapidly deteriorating road conditions, increased traffic loads, and shrinking funds, resulting in the rapid accumulation of a large pavement database, knowledge-based expert systems (KBESs) have therefore been developed to solve various transportation problems. This dissertation presents the development of theory and algorithm for a new decision tree induction method, called co-location-based decision tree (CL-DT.) This method will enhance the decision-making abilities of pavement maintenance personnel and their rehabilitation strategies. This idea stems from shortcomings in traditional decision tree induction algorithms, when applied in the pavement treatment strategies. The proposed algorithm utilizes the co-location (co-occurrence) characteristics of spatial attribute data in the pavement database. With the proposed algorithm, one distinct event occurrence can associate with two or multiple attribute values that occur simultaneously in spatial and temporal domains. This research dissertation describes the details of the proposed CL-DT algorithms and steps of realizing the proposed algorithm. First, the dissertation research describes the detailed colocation mining algorithm, including spatial attribute data selection in pavement databases, the determination of candidate co-locations, the determination of table instances of candidate colocations, pruning the non-prevalent co-locations, and induction of co-location rules. In this step, a hybrid constraint, i.e., spatial geometric distance constraint condition and a distinct event-type constraint condition, is developed. The spatial geometric distance constraint condition is a neighborhood relationship-based spatial joins of table instances for many prevalent co-locations with one prevalent co-location; and the distance event-type constraint condition is a Euclidean distance between a set of attributes and its corresponding clusters center of attributes. The dissertation research also developed the spatial feature pruning method using the multi-resolution pruning criterion. The cross-correlation criterion of spatial features is used to remove the nonprevalent co-locations from the candidate prevalent co-location set under a given threshold. The dissertation research focused on the development of the co-location decision tree (CL-DT) algorithm, which includes the non-spatial attribute data selection in the pavement management database, co-location algorithm modeling, node merging criteria, and co-location decision tree induction. In this step, co-location mining rules are used to guide the decision tree generation and induce decision rules. For each step, this dissertation gives detailed flowcharts, such as flowchart of co-location decision tree induction, co-location/co-occurrence decision tree algorithm, algorithm of colocation/co-occurrence decision tree (CL-DT), and outline of steps of SFS (Sequential Feature Selection) algorithm. Finally, this research used a pavement database covering four counties, which are provided by NCDOT (North Carolina Department of Transportation), to verify and test the proposed method. The comparison analyses of different rehabilitation treatments proposed by NCDOT, by the traditional DT induction algorithm and by the proposed new method are conducted. Findings and conclusions include: (1) traditional DT technology can make a consistent decision for road maintenance and rehabilitation strategy under the same road conditions, i.e., less interference from human factors; (2) the traditional DT technology can increase the speed of decision-making because the technology automatically generates a decision-tree and rules if the expert knowledge is given, which saves time and expenses for PMS; (3) integration of the DT and GIS can provide the PMS with the capabilities of graphically displaying treatment decisions, visualizing the attribute and non-attribute data, and linking data and information to the geographical coordinates. However, the traditional DT induction methods are not as quite intelligent as one's expectations. Thus, post-processing and refinement is necessary. Moreover, traditional DT induction methods for pavement M&R strategies only used the non-spatial attribute data. It has been demonstrated from this dissertation research that the spatial data is very useful for the improvement of decision-making processes for pavement treatment strategies. In addition, the decision trees are based on the knowledge acquired from pavement management engineers for strategy selection. Thus, different decision-trees can be built if the requirement changes.
  • Thumbnail Image
    A comparison of driving characteristics and environmental characteristics using factor analysis and k-means clustering algorithm
    Jung, Heejin (Virginia Tech, 2012-08-10)
    The dissertation aims to classify drivers based on driving and environmental behaviors. The research determined significant factors using factor analysis, identified different driver types using k-means clustering, and studied how the same drivers map in each classification domain. The research consists of two study cases. In the first study case, a new variable is proposed and then is used for classification. The drivers were divided into three groups. Two alternatives were designed to evaluate the environmental impact of driving behavior changes. In the second study case, two types of data sets were constructed: driving data and environmental data. The driving data represents driving behavior of individual drivers. The environmental data represents emissions and fuel consumption estimated by microscopic energy and emissions models. Significant factors were explored in each data set using factor analysis. A pair of factors was defined for each data set. Each pair of factors was used for each k-means clustering: driving clustering and environmental clustering. Then the factors were used to identify groups of drivers in each clustering domain. In the driving clustering, drivers were grouped into three clusters. In the environmental clustering, drivers were clustered into two groups. The groups from the driving clustering were compared to the groups from the environmental clustering in terms of emissions and fuel consumption. The three groups of drivers from the driving clustering were also mapped in the environmental domain. The results indicate that the differences in driving patterns among the three driver groups significantly influenced the emissions of HC, CO, and NOx. As a result, it was determined that the average target operating acceleration and braking did essentially influence the amount of emissions in terms of HC, CO, and NOx. Therefore, if drivers were to change their driving behavior to be more defensive, it is expected that emissions of HC, CO, and NOx would decrease. It was also found that spacing-based driving tended to produce less emissions but consumed more fuel than other groups, while speed-based driving produced relatively more emissions. On the other hand, the defensively moderate drivers consumed less fuel and produced fewer emissions.
  • Thumbnail Image
    Comparison of Potential Contribution of Typical Pavement Materials to Heat Island Effect
    Yang, Hailu; Yang, Kai; Miao, Yinghao; Wang, Linbing; Ye, Chen (MDPI, 2020-06-10)
    Pavement materials have significant influence on the urban heat island effect (UHIE). This paper presents a study on the potential effects of pavement materials on UHIE in a natural environment. Three typical pavement materials, including cement concrete, dense asphalt concrete and porous asphalt mixture, were selected to evaluate their anti-UHIE properties by testing. In this paper, heat island potential (HIP) is proposed as a new index to analyze the influence of pavement materials on UHIE. It is shown that the temperature inside a pavement distinctly depends on the depth, and varies, but is usually higher than the air temperature. Solar radiation in the daytime significantly contributes to the temperature of pavement surface and the upper part. The correlation becomes weak at the middle and the bottom of pavements. Among the three materials tested in this study, the anti-UHIE performance of cement concrete is significantly higher than that of the other asphalt mixtures, while the porous asphalt mixture is slightly better than the dense asphalt concrete in anti-UHIE.
  • Thumbnail Image
    Composite Pavements: A Technical and Economic Analysis During the Pavement Type Selection Process
    Núñez, Orlando (Virginia Tech, 2007-12-03)
    In most road infrastructure networks, the two prevalent types of pavements considered during the pavement type selection (PTS) process are flexible and rigid. Thus, these two structures are the most commonly constructed in the road industry. A consideration of a different pavement alternative is proposed in this study. Composite pavements, which are in essence a combination of a rigid base overlaid with a hot-mix asphalt (HMA) surface course, have the potential to meet the technical and economic requirements that are sought in the PTS process. For that reason, technical and economic evaluations were performed to justify the consideration of composite pavement systems in the PTS process. At the technical level, composite pavement design guidelines from various transportation agencies were obtained and followed to design their respective composite pavement structures. A mechanistic analysis based on the multi-layer linear elastic theory was performed on different composite structures to understand the behavior they present when compared to traditional pavements. In addition, distresses affecting composite pavements such as fatigue (bottom-up and top-down) cracking, rutting, and reflective cracking were modeled and investigated using sensitivity analyses. At the economic level, a deterministic life cycle cost analysis (LCCA) based on Virginia Department of Transportation (VDOT) guidelines was performed. This LCCA compared two proposed composite pavements (one with a cement-treated base [CTB] and the other with a continuously reinforced concrete pavement [CRCP] base) to traditional flexible and rigid pavement structures. Furthermore, sensitivity analyses involving discount rates and traffic volumes were performed to investigate their effect on the present worth (PW) computation of the four pavement alternatives. Results from this study suggest that composite pavements have both the technical and economic potential to be considered during the PTS process.
  • Thumbnail Image
    Computational Analysis of Asphalt Binder based on Phase Field Method
    Hou, Yue (Virginia Tech, 2014-04-29)
    The mechanical performance evaluation of asphalt binder has always been a challenging issue for pavement engineers. Recently, the Phase Field Method (PFM) has emerged as a powerful computational tool to simulate the microstructure evolution of asphalt binder. PFM analyzes the structure from the free energy aspect and can provide a view of the whole microstructure evolution process. In this dissertation, asphalt binder performance is analyzed by PFM in three aspects: first, the relationship between asphalt chemistry and performance is investigated. The components of asphalt are simplified to three: asphaltene, resin and oil. Simulation results show that phase separation will occur under certain thermal conditions and result in an uneven distribution of residual thermal stress. Second, asphalt cracking is analyzed by PFM. The traditional approach to analyze crack propagation is Classic Fracture Mechanics first proposed by Griffith, which needs to clearly depict the crack front conditions and may cause complex cracking topologies. PFM describes the microstructure using a phase-field variable which assumes positive one in the intact solid and negative one in the crack void. The fracture toughness is modeled as the surface energy stored in the diffuse interface between the intact solid and crack void. To account for the growth of cracks, a non-conserved Allen-Cahn equation is adopted to evolve the phase-field variable. The energy based formulation of the phase-field method handles the competition between the growth of surface energy and release of elastic energy in a natural way: the crack propagation is a result of the energy minimization in the direction of the steepest descent. Both the linear elasticity and phase-field equation are solved in a unified finite element frame work, which is implemented in the commercial software COMSOL. Different crack mode simulations are performed for validation. It was discovered that the onset of crack propagation agrees very well with the Griffith criterion and experimental results. Third, asphalt self-healing phenomenon is studied based on the Atomic Force Microscopy (AFM) technology. The self-healing mechanism is simulated in two ways: thermodynamic approach and mechanical approach. Cahn-Hilliard dynamics and Allen-Cahn dynamics are adopted, respectively.
  • Thumbnail Image
    A Computer Model to Estimate Commercial Aviation Fuel Consumption and Emissions in the Continental United States
    Zou, Zhihao (Virginia Tech, 2013-01-03)
    A comprehensive model is developed to estimate and predict the fuel consumption and emissions by domestic commercial aviation in the Continental United States. Most of the existing fuel consumption and emission models are limited in their ability to predict the annual fuel burn for air transportation at the national level. For example, those models either require real track data or are developed only to model single flight scenarios.  The model developed in this thesis is part of a software framework called the Transportation Systems Analysis Model (TSAM). The model has the capability to estimate fuel consumption and emissions for millions of domestic flights in a year in the continental U.S. TSAM is a nationwide, long-distance, multimodal travel demand forecast model developed at Virginia Tech. The model enables TSAM to quantify fuel and emission metrics for various modes of transportation. The EUROCONTROL Base of Aircraft Data (BADA) is employed as the Aircraft Performance Model to simulate individual flight profiles and calculate fuel burn rates. Fuel consumption on the ground (taxi mode) is estimated separately. Different operational conditions like wind states, terminal area detour, cruise altitude and airport elevation are considered in the model. Emissions of HC, CO, NOx and SOx are computed inside the Landing/Take-off (LTO) cycle based on the fuel consumption estimate, while greenhouse gas of CO2 is calculated for the complete flight cycle.
  • Thumbnail Image
    Condition Assessment of Civil Infrastructure and Materials Using Deep Learning
    Liu, Fangyu (Virginia Tech, 2022-08-24)
    The abilities of powerful regression and multi-type data processing allow deep learning to effectively and accurately complete multi-tasks, which is the need of civil engineering. More cases showed that deep learning has become a greatly powerful and increasingly popular tool for civil engineering. Based on these, this dissertation developed deep learning studies for the condition assessment of civil infrastructure and materials. This dissertation included five main works: (1) Deep learning and infrared thermography for asphalt pavement crack severity classification. This work focused on longitudinal or transverse cracking. This work first built a dataset with four severity levels (no, low-severity, medium-severity, and high-severity) and three image types (visible, infrared, and fusion). Then this work applied the convolutional neural network (CNN) to classify the crack severity based on two strategies deep learning from scratch and transfer learning). This work also investigated the effect of image types on the accuracy of these two strategies and on the classification of different severity levels. (2) Asphalt pavement crack detection based on convolutional neural network and infrared thermography. This work first built an open dataset with three image types (visible, infrared, and fusion) and different conditions (single, multi, thin, and thick cracks; clean, rough, light, and dark backgrounds) and periods (morning, noon, and dusk). Then this work evaluated the performance of the CNN model based on the accuracy and complexity (computational and model). (3) An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag powder. This work considered a total of 23 factors for predicting the tensile behavior of hybrid fiber reinforced concrete (HFRC), including fibers' characteristics, mechanical properties of plain concrete, and concrete composition. Then this work compared the performance of the artificial neural network (ANN) method and the traditional equation-based method in terms of predicting the tensile stress, tensile strength, and strain corresponding to tensile strength. (4) Deep transfer learning-based vehicle classification by asphalt pavement vibration. This work first applied the pavement vibration IoT monitoring system to collect raw vibration signals and performed the wavelet transform to obtain denoised vibration signals. Then this work represented the vibration signals in two different ways, including the time-domain graph and the time-frequency graph. Finally, this work proposed two deep transfer learning-based vehicle classification methods according to these two representations of vibration signals. (5) Physical-informed long short-term memory (PI-LSTM) network for data-driven structural response modeling. This work first applied the single-degree-of-freedom (SDOF) system to investigate the performance of the proposed PI-LSTM network compared with the existing methods. Then this work further investigated and validated the proposed PI-LSTM network in terms of the experimental results of one six-story building and the numerical simulation results of another six-story building.
  • Thumbnail Image
    Design of Wet Surface Traffic Signal Timing Change Intervals
    Li, Huan (Virginia Tech, 2011-02-02)
    Driver violations of traffic signals are a major cause of intersection vehicle crashes. The duration of yellow intervals is highly associated with driver yellow/red time stopping behavior. Rainy weather and wet pavement surface conditions may result in changes in both driver behavior and vehicle performance. The research presented in this thesis quantifies the impact of wet pavement surface and rainy weather conditions on driver perception-reaction times (PRTs) and deceleration levels, which are used in statistical models for the design of yellow intervals. A new dataset with a total of 648 stop-run records were collected as part of the research effort during rainy weather and wet pavement surface conditions at the Virginia Department of Transportation's Smart Road facility. This experiment was conducted at a 72.4 km/h (45 mi/h) approach speed where participant drivers encountered a yellow indication initiation. The participant drivers were randomly selected in different age groups (under 40 years old, 40 to 59 years old, and 60 years of age or older) and genders (female and male). Combined with an existing dataset that was collected by the same research group under clear weather conditions during the summer of 2008, statistical models for driver PRT and deceleration levels are developed, considering roadway surface and environmental parameters, driver attributes (age and gender), roadway grade, and time to the intersection at the onset of yellow. Using the state-of-the-practice procedures with the modeled PRT and deceleration levels, inclement weather yellow timings are then developed as a function of different factors (e.g., driver age/gender, roadway grade, speed limits, and precipitation levels). The results indicate that an increase in the duration of change interval is required for wet roadway surface and rainy weather conditions. Lookup tables are developed with different reliability levels to provide practical guidelines for the design of yellow signal timings in wet and rainy weather conditions. These recommended change durations can be integrated within the Vehicle Infrastructure Integration (VII) initiative to provide customizable driver warnings prior to a transition to a red indication.
  • Thumbnail Image
    Design, Modelling, and Test of an Electromagnetic Speed Bump Energy Harvester
    Todaria, Prakhar (Virginia Tech, 2016-04-29)
    Speed bump energy harvester, which aims to harvest energy from the passing by vehicles by absorbing their kinetic and potential energy, is designed, fabricated, simulated, and tested in this research. The proposed design is analyzed with a theoretical modelling which has then been validated with a ground test. Result reveals that the prototype has been able to produce up to 600 watts of peak power and around 150 watts of RMS power which is significant number. Further analysis has been done which theoretically suggests that the numbers could be increased up to 1 KW by optimizing the speed bump design and varying the system parameters such as electrical damping, mechanical damping, velocity and weight of the vehicles. Overall, system is able to increase the overall energy density by using Mechanical Motion Rectification (MMR) technology which would allow the increase in the power generation almost by double. Furthermore, different vehicle models have been used to analyze the effectiveness and accuracy of the harvester and also, the effect of harvester on the dynamics of the vehicle has been studied and analyzed in detail.