Increasingly tighter budgets, changes in government role/function, declines in staff resources, and demands for increased accountability in the transportation field have brought unprecedented challenges for state transportation officials at all management levels. Systematic methodologies for effective management of a specific type of infrastructure (e.g., pavement and bridges) as well as for holistically managing all types of infrastructure assets are being developed to approach these challenges. In particular, the intrinsic characteristics of highway system make the use of multi-objective optimization techniques particularly attractive for managing highway assets. Recognizing the need for effective tradeoff tools and the limitations of state-of-practice analytical models and tools in highway asset management, the main objective of this dissertation was to develop a performance-based asset management framework that uses multi-objective optimization techniques and consists of stand-alone but logically interconnected optimization models for different management levels. Based on a critical review of popular multi-objective optimization techniques and their applications in highway asset management, a synergistic integration of complementary multi-criteria optimization techniques is recommended for the development of practical and efficient decision-supporting tools. Accordingly, the dissertation first proposes and implements a probabilistic multi-objective model for performance-based pavement preservation programming that uses the weighting sum method and chance constraints. This model can handle multiple incommensurable and conflicting objectives while considering probabilistic constraints related to the available budget over the planning horizon, but is found more suitable to problems with small number of objective functions due to its computational intensity. To enhance the above model, a hybrid model that requires less computing time and systematically captures the decision maker's preferences on multiple objectives is developed by combining the analytic hierarchy process and goal programming. This model is further extended to also capture the relative importance existent within optimization constraints to be suitable for allocations of funding across multiple districts for a decentralized state department of transportation. Finally, as a continuation of the above proposed models for the succeeding management level, a project selection model capable of incorporating qualitative factors (e.g. equity, user satisfaction) into the decision making is developed. This model combines k-means clustering, analytic hierarchy process and integer linear programming. All the models are logically interconnected in a comprehensive resource allocation framework. Their feasibility, practicality and potential benefits are illustrated through various case studies and recommendations for further developments are provided. / Ph. D.

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. / Doctor of Philosophy / About 96% of roads are paved with asphalt mixture. Asphalt mixture consists of asphalt, aggregates, and additives. Asphalt mix design refers to the process to determine the proper proportion of aggregates, asphalt, and additives. Traditional asphalt mix design procedures in laboratories are empirical and cost much labor, material resources, and finance. Pavement distresses, for example, cracks are important indicators to assess pavement condition. With the development of data science, machine learning (ML) has been applied to various fields by predicting desired targets. The multi-objective optimization refers to determining the optimal solution of a multiple objectives problem. The study applied ML methods to predict asphalt mixture components' proportions and pavement distress with historical experimental data and pavement condition records from literature and an open-source database. Specifically, the main research content can be divided into three parts:1. Established ML models to predict the proportion of asphalt when aggregates are given; 2. Built ML models to predict pavement distress from pavement materials information, pavement structure, climate, and traffic; 3. Develop a digital asphalt mix design procedure by combining the pavement distress prediction models and a multi-objective optimization algorithm.

When a vehicle changes speed or direction, the interaction between the contacting surfaces of the tire and the pavement form frictional forces. The pavement's contribution to tire-pavement friction is referred to as skid resistance and is provided by pavement microtexture and macrotexture. The amount of skid resistance depreciates over time due to the polishing action of traffic, and for this reason, the skid resistance should be monitored with friction testing equipment. The equipment use one of four test methods to measure network-level friction: ASTM E 274 locked-wheel, ASTM E 2340 fixed-slip technique, ASTM E 1859 variable-slip technique, and sideways-force coefficient (SFC) technique. The fixed-slip, variable-slip, and SFC techniques are used in continuous friction measurement equipment (CFME). In the United States, skid resistance is traditionally measured with a locked-wheel skid trailer (LWST) equipped with either a ASTM E 501 ribbed or a ASTM E 524 smooth 'no tread' tire. Since the LWST fully-locks the test wheel to measure friction, it is only capable of spot testing tangent sections of roadway. By contrast, the remaining three test methods never lock their test wheels and, therefore, they can collect friction measurements continuously on all types of roadway, including curves and t-intersections. For this reason, highway agencies in the U.S. are interested in transitioning from using a LWST to using one of three continuous methods. This dissertation explores the use of continuous friction measurements, collected with a Sideways-force Coefficient Routine Investigation Machine (SCRIM), in a systemic highway safety management approach to reduce crashes that result in fatalities, injuries, and property damage only. The dissertation presents four manuscripts. In the first manuscript, orthogonal regression is used to develop models for converting between friction measurements with a SCRIM and LWST with both a ribbed and smooth tire. The results indicated that the LWST smooth tire measured friction with greater sensitivity to changes in macrotexture than the SCRIM and LWST ribbed tire. The SCRIM also had greater correlation to the LWST ribbed tire than the LWST smooth tire. The second investigation establishes the relationship between friction measured with a SCRIM and the risk of crashes on dry and wet pavement surfaces. The results of this showed that increasing friction decreases both dry and wet pavement crashes; however, friction was found to have greater impact in wet conditions. Due to the negative relationship between friction and crashes, eventually there will be a point where further losses in friction can result in a rapid increase in crash risk. This point can be identified with a friction threshold known as an investigatory level. When measured friction is at or below the investigatory level, an in- and out-of-field investigation is required to determine whether a countermeasure is necessary to improve safety. The third manuscript proposes a statistical regression approach for determining investigatory levels. Since this approach relies on statistical regression, the results are objective and should be the same for any analyst reviewing the same data. The investigatory levels can be used in a systemic approach that identifies locations where crashes can be reduced based on a benefit-cost analysis of surface treatments. Last, the forth manuscript demonstrates a benefit-cost analysis that selects surface treatments based on crash reductions predicted with continuous friction measurements. / Doctor of Philosophy / When a vehicle changes speed or direction, the tires slide over the pavement surface, creating friction that produces the traction that is necessary for the vehicle to change speed or direction. Friction can diminish when water, dust, and other contaminants are present, or over time due to traffic. Over time, the loss in friction causes the risk of a crash to increase. However, this relationship is non-linear, and therefore, eventually there will be a point where further losses in friction can cause a rapid increase in crash risk. For this reason, the pavement friction is monitored with equipment that slides a rubber tire with known properties over a pavement surface. Since friction is lowest when the pavement is wet, the equipment applies a film of water to the surface directly in front of the sliding tire. There are different types of equipment used to measure friction. The physical designs of the equipment and their method of testing may be different. For example, some devices measure friction by sliding a wheel that is angled away from the path of the vehicle, while others slide a wheel that is aligned with the vehicle but reduced in speed compared to the vehicle. The factors that make the equipment different can affect the quantity of friction that is measured, as well as the timing between each consecutive measurement. The advantages that some equipment offers can entice highway agencies to transition from a pre-existing system to a more advantageous system. Before transitioning, the measurements from the two types of equipment should be compared directly to determine their correlation. Statistical regression can also be used to develop models for converting the measurements from the new equipment to the units of the current, which can help engineers interpret the measurements, and to integrate them into an existing database. The presence of water on a pavement surface can result in a temporary loss of friction that can increase the risk of a crash beyond the normal, dry pavement state. This does not guarantee that dry pavements have sufficient friction as is suggested in most literature. In this dissertation, the relationship between friction and the risk of a crash on dry and wet pavements are evaluated together. The results show that increasing friction can decrease the crash risk on both dry and wet pavement surfaces. The amount of friction that is needed to maintain low crash risk is not the same for every section of road. Locations such as approaches to curves or intersections can increase the risk of a crash, and for that reason, some sections of roadway require more friction than others. Minimum levels of friction called investigatory levels can be established to trigger an in- and out-of-field investigation to determine whether improving friction can improve safety when the measured friction is at or below a specific value. This dissertation proposes a methodology for determining the investigatory levels of friction for different sections of roadway using a statistical regression approach. The investigatory levels are then used to identify locations where pavement surface treatments can reduce crashes based on a benefit-cost analysis. Last, the ability of a surface treatment to reduce crashes is evaluated using another statistical regression approach that predicts changes in crash risk using friction measurements. Since there are several treatment options, a treatment is selected based on estimated cost and benefit.

Crashes on wet pavements are responsible for 25% of all crashes and 13.5% of fatal crashes in the US (Harwood et al. 1988). This number represents a significant portion of all crashes. Current methods used by the Department of Transportations (DOTs) are based on wet over dry ratios and simplified approaches to estimate hydroplaning speeds. A fraction of all wet crashes is hydroplaning; although they are related, the difference between a "wet crash" and "hydroplaning" is a wet-crash hydrodynamic-based severity scale is less compared to hydroplaning where the driver loses control. This dissertation presents a new conceptual framework design to reduce wet- and hydroplaning-related crashes by identifying locations with a high risk of crashes using systemic, data-driven, risk-based approaches and available data. The first method is a robust systemic approach to identify areas with a high risk of wet crashes using a negative binomial regression to quantify the relationship between wet to dry ratio (WDR), traffic, and road characteristics. Results indicate that the estimates are more reliable than current methods of WDR used by DOTs. Two significant parameters are grade difference and its absolute value. The second method is a simplified approach to identify areas with a high risk of wet crashes with only crash counts by applying a spatial multiresolution analysis (SMA). Results indicate that SMA performs better than current hazardous-road segments identification (HRSI) methods based on crash counts by consistently identifying sites during several years for selected 0.1 km sections. A third method is a novel systemic approach to identify locations with a high risk of hydroplaning through a new risk-measuring parameter named performance margin, which considers road geometry, environmental condition, vehicle characteristics, and operational conditions. The performance margin can replace the traditional parameter of interest of hydroplaning speed. The hydroplaning risk depends on more factors than those identified in previous research that focuses solely on tire inflation pressure, tire footprint area, or wheel load. The braking and tire-tread parameters significantly affected the performance margin. Highway engineers now incorporate an enhanced tool for hydroplaning risk estimation that allows systemic analysis. Finally, a critical review was conducted to identify existing solutions to reduce the high potential of skidding or hydroplaning on wet pavement. The recommended strategies to help mitigate skidding and hydroplaning are presented to help in the decision process and resource allocation. Geometric design optimization provides a permanent impact on pavement runoff characteristics that reduces the water accumulation and water thickness on the lanes. Road surface modification provides a temporary impact on practical performance and non-engineering measures. / Doctor of Philosophy / Crashes on wet pavements are responsible for 25% of all crashes and 13.5% of fatal crashes in the US (Harwood et al. 1988). This number represents a significant portion of all crashes. Current procedures used by DOTs to identify locations with a high number of wet crashes and hydroplaning are too simple and might not represent actual risk. A fraction of all wet crashes is hydroplaning, although they are related to the difference between a "wet crash" and "hydroplaning" is a wet crash water-vehicle interaction is less compared to hydroplaning where the driver loses control. This dissertation presents a new procedure to evaluate the road network to identify locations with a high risk of wet crashes and hydroplaning. The risk estimation process uses data collected in the field to determine the risk at a particular location and, depending on the available data a transportation agency uses, will be the approach to apply. The first statistical method estimates the frequency of wet crashes at a location. This estimate is developed by using a statistical model, negative binomial regression. This model measures the frequency of dry crashes, wet crashes, traffic, and road characteristics to determine the total number of wet crashes at a location. Results indicate that this option is more reliable than the current methods used by DOTs. They divide the number of wet crashes by the number of dry crashes. Two elements identified to influence the results are the difference in road grade and its absolute value. The second statistical method to estimate wet crashes considers crash counts by applying a statistical process, spatial multiresolution analysis (SMA). Results indicate that SMA performs better than current processes based only on the crash counts. This option can identify the high-risk location for different years, called consistency. The more consistent the method is, the more accurate is the results. A third statistical method is a novel way to estimate hydroplaning risk. Hydroplaning risk is currently based on finding the maximum speed before hydroplaning occurs. A vehicle's performance related to the water-film thickness provides an estimation method developed by (Gallaway et al. 1971), which includes rainfall intensities, road characteristics, vehicle characteristics, and operating conditions. The hydroplaning risk depends on more aspects than tire inflation pressure, tire footprint area, or vehicle load on the wheel. The braking and tire tread affect the performance margin. Highway engineers can use this improved hydroplaning risk-estimation tool to analyze the road network. Finally, a critical review showed the available solutions to reduce the probability of having a wet crash or hydroplaning on wet pavement. The recommended strategies to mitigate wet crashes and hydroplaning provide information to allocate resources based on proven, practical strategies. Road geometry design can be optimized to remove water from the road. This geometry is a permanent modification of pavement characteristics to reduce water accumulation and water thickness on the road. Road surface treatments and non-engineering measures provide temporary measures to improve vehicle performance or driver operation.

Pavement structural condition plays a critical role in the rate of pavement deterioration, yet most state highway agencies' network-level decision-making processes are primarily based on surface distresses. Despite the limitations of the traditional structural condition measuring devices, some states have experimented with stationary deflection devices for network-level applications. Over the past decade, continuous deflection devices have become capable of measuring the network-level pavement structural condition information. However, since the traffic speed deflection devices use newer technology, there is a need for guidelines on how the state agencies could make use of this information for pavement management decision-making. This dissertation developed processes and enhanced tools to incorporate the pavement structural condition from the TSD into Virginia's network-level pavement management process This first part of the study developed pavement deterioration models for a subset of road networks in Virginia, to show that the pavement structural condition as measured by the TSD has an impact on the rate of deterioration of the surface condition. A structural condition matrix was then developed to augment the treatment selection process currently used by VDOT. Application of the augmented matrix on the tested Interstate network resulted in reducing the percentage of the network requiring CM and increasing the percentage requiring PM and RM. The second part of the study investigated the possibility of using pavement deflection measurements obtained from the TSD for network-level structural evaluation of pavements in Virginia. The study reported that the structural condition obtained with the TSD can replace the structural condition obtained from the FWD that is currently used in the VDOT PMS. The effective structural number (SNeff) calculated from the TSD and FWD had similar distribution, and the calculated consistency between the TSD SNeff and FWD SNeff was higher than the consistency between the SNeff from two repeated sets of FWD measurements. The third part of the study simulated the network level decision-making approaches based on both the structural condition parameter and the surface condition parameter, considering cases with and without the pavement treatment interval. The study reported that network-level decisions based on the pavement surface condition alone can result in significantly different treatment selection, compared to decisions based on the pavement structural condition. The study reported savings of 9% and 11% for cases with and without considering the pavement treatment intervals, using decision-making based on the structural condition. / Doctor of Philosophy / Pavement structural condition plays a critical role in the rate of pavement deterioration, yet most state highway agencies' network-level decision-making processes are primarily based on surface distresses. Despite the limitations of the traditional structural condition measuring devices, some states have experimented with stationary deflection devices for network-level applications. Over the past decade, continuous deflection devices have become capable of measuring the network-level pavement structural condition information. However, since the traffic speed deflection devices use newer technology, there is a need for guidelines on how the state agencies could make use of this information for pavement management decision-making. This dissertation developed processes and enhanced tools to incorporate the pavement structural condition from the TSD into Virginia's network-level pavement management process' This first part of the study developed pavement deterioration models for a subset of road networks in Virginia, to show that the pavement structural condition as measured by the TSD has an impact on the rate of deterioration of the surface condition. A structural condition matrix was then developed to augment the treatment selection process currently used by VDOT. Application of the augmented matrix on the tested Interstate network resulted in reducing the percentage of the network requiring CM and increasing the percentage requiring PM and RM. The second part of the study investigated the possibility of using pavement deflection measurements obtained from the TSD for network-level structural evaluation of pavements in Virginia. The study reported that the structural condition obtained with the TSD can replace the structural condition obtained from the FWD that is currently used in the VDOT PMS. The effective structural number (SNeff) calculated from the TSD and FWD had similar distribution, and the calculated consistency between the TSD SNeff and FWD SNeff was higher than the consistency between the SNeff from two repeated sets of FWD measurements. The third part of the study simulated the network level decision-making approaches based on both the structural condition parameter and the surface condition parameter, considering cases with and without the pavement treatment interval. The study reported that network-level decisions based on the pavement surface condition alone can result in significantly different treatment selection, compared to decisions based on the pavement structural condition. The study reported savings of 9% and 11% for cases with and without considering the pavement treatment intervals, using decision-making based on the structural condition.

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.

This thesis broadly addresses the issue of materials sustainability in the production of Portland cement concrete. Two methods are presented, both aimed at achieving more sustainable concrete through the use of waste and recycled materials. The first method involves utilizing reclaimed asphalt pavement (RAP) as an aggregate in structural concrete, and the second method involves utilizing waste quarry fines as partial replacement of Portland cement in concrete mixes. Many efforts have been made in recent years to justify the use of RAP aggregates in concrete. All previous efforts appear to unanimously report a reduction in concrete performance with varying proportions of RAP usage. The poor performance of RAP aggregates in concrete is attributed mainly to a larger, more porous interfacial transition zone (ITZ) and to the cohesive failure of the asphalt. It is hypothesized that the detrimental impact on the ITZ is attributable to organic compounds leached from the asphalt in the high pH pore solution. This study proves the presence of organic compounds in the pore solution and demonstrates that there is an apparent retardation of cement hydration. This study also attempted to pretreat the RAP in a sodium hydroxide (NaOH) solution to pre-leach the organic compounds. The pretreatment demonstrated that organic compounds were leached and that NaOH modified the asphalt surface chemistry. However, only a marginal improvement in compressive strength was observed by completing the pretreatment. Replacement of Portland cement by filler products is a practice aimed at reducing the carbon footprint of concrete, such as is common with Type IL Portland limestone cement. This study investigates the impact of replacing cement with seven different quarry fines materials. The quarry fines were used to replace cement at 5% to 20% by volume in either cement paste or mortar samples that were then analyzed for various physicochemical properties. It was found that all the quarry fines had detrimental impact on the hydration kinetics of cement pastes. The inclusion of quarry fines was also found to cause varying degrees of reduction in mortar compressive strength. While further analyses of the quarry fines are required, quarry fines 2, 5 and 7 did display encouraging signs to suggest the potential for use as a filler material in blended cements. / Master of Science / This thesis broadly addresses the issue of sustainability in the cement and concrete industry. Sustainability is a significant problem for the cement and concrete industry due to the large amount of carbon emissions produced in the manufacturing process of Portland cement. One method to reduce the carbon footprint of concrete is to use recycled aggregates, and reclaimed asphalt pavement (RAP) is investigated in this thesis as a recycled aggregate option. Previous studies have shown that the use of RAP in concrete results in poor mechanical performance when compared to conventional concrete. In this thesis, the RAP was pretreated by soaking it in sodium hydroxide (NaOH) to see if any improvement is noted. It was determined that the pretreatment resulted in marginal improvements in concrete performance. Another method to reduce the carbon footprint of concrete is through the use of substitutions of Portland cement. In this thesis, quarry fines from around Virginia were investigated for potential as substitutive material. Quarry fines are a by-product from quarrying operations and are often considered a waste material because they have limited applications. This study analyzed the performance of cementitious materials prepared with various substitutive percentages of quarry fines and found that, in general, the inclusion of quarry fines resulted in a decrease of mechanical performance. In total, seven quarry fines were tested and only two showed potential for use as a substitution in Portland cement concrete. These two investigations are essential in reaching the goal of reducing the carbon footprint of the cement and concrete industry.

Infrastructure systems are critical to sustaining and improving economical growth. Poor condition of infrastructure systems results in lost productivity and reduces the quality of life. Today's global economy forces governments to sustain and renew infrastructure systems already in place in order to remain competitive and productive (GAO, 2008). Therefore, civil engineers and policymakers have been quite interested in the overall quality of the highways and bridges throughout the US (Miller, 2007). Transportation networks are essential parts of the Nation's infrastructure systems. Deterioration due to age and use is the main threat to the level of service observed in surface transportation networks. Thus, highway agencies throughout the United States strive to maintain, repair and renew transportation systems already in place (Miller, 2007). A recent disaster, the collapse of the Minneapolis I-35 W Bridge, once again revealed the importance of infrastructure preservation programs and resulted in debates as to how state departments of transportation (DOTs) should and can preserve the existing infrastructure systems. Therefore, it is essential to establish effective maintenance programs to preserve aging infrastructure systems. The major challenge facing the state highway maintenance managers today is to preserve the road networks at an acceptable level of serviceability subject to the stringent yearly maintenance and rehabilitation (M&R) budgets. Maintenance managers must allocate such limited budgets among competing alternatives, which makes the situation even more challenging. Insufficient use of available smart decision-making tools impedes eliciting effective and efficient maintenance programs. Hence, this thesis presents the development and implementation of a network-level pavement maintenance optimization model which can be used by maintenance managers as a decision-making tool to address the maintenance budget allocation issue. The network-level optimization model is established with the application of the Linear Programming algorithm and is subject to budget constraints and the agencies' pavement performance goals in terms of total lane-miles in each pavement condition state. This tool is developed with Microsoft Office Excel. The tool can compute the optimal amount of investment for each pavement treatment type in a given funding period. Thus, the model enables maintenance managers in highway agencies to develop alternative network-level pavement maintenance strategies through an automated and optimized process rather than using what-if analysis. / Master of Science

Using geotextiles in secondary roads to stabilize weak subgrades has been a well accepted practice over the past thirty years. However, from an economical point of view, a complete life cycle cost analysis (LCCA), which includes not only costs to agencies but also costs to users, is urgently needed to assess the benefits of using geotextile in secondary road flexible pavement. In this study, a comprehensive life cycle cost analysis framework was developed and used to quantify the initial and the future cost of 25 representative design alternatives. A 50 year analysis cycle was used to compute the cost-effectiveness ratio for the design methods. Four flexible pavement design features were selected to test the degree of influence of the frame's variables. The analysis evaluated these variables and examined their impact on the results. The study concludes that the cost effectiveness ratio from the two design methods shows that the lowest cost-effectiveness ratio using Al-Qadi's design method is 1.7 and the highest is 3.2. The average is 2.6. For Perkins' design method, the lowest value is 1.01 and the highest value is 5.7. The average is 2.1. The study also shows when user costs are considered, the greater TBR value may not result in the most effective life-cycle cost. Hence, for an optimum secondary road flexible pavement design with geotextile incorporated in the system, a life cycle cost analysis that includes user cost must be performed. / Master of Science

Pavement macrotexture has been shown to influence a range of safety and comfort issues including wet weather friction, splash and spray, ambient and in-vehicle noise, tire wear, and rolling resistance. While devices and general guidance exist to measure macrotexture, the wide-scale collection and use of macrotexture is neither mandated nor is it typically employed in the United States. This work seeks to improve upon the methods used to calibrate, collect, pre-process, and distill macrotexture data into useful information that can be utilized by pavement managers. This is accomplished by 1. developing a methodology to evaluate and compare candidate data collection devices; 2. plans and procedures to evaluate the accuracy of high-speed network data collection devices with reference surfaces and measurements; 3. the development of a method to remove erroneous data from emerging 3-D macrotexture sensors; 4. development of a model to describe the change in macrotexture as a function of traffic; 5.finally, distillation of the final collected pavement surface profiles into parameters for the prediction of important pavement surface properties aforementioned. Various high-speed macrotexture measurement devices were shown to have good repeatability (between 0.06 to 0.09mm MPD) and interchangeability of single-spot laser dfevices was demonstrated via a limits of agreement analysis. The operational factors of speed and acceleration were shown to affect the resulting MPD of several devices and guidelines are given for vehicle speed and sensor exposure settings. Devices with single spot and line lasers were shown to reproduce reference waveforms on manufactured surfaces within predefined tolerances. A model was developed that predicts future macrotexture levels (as measured by RMS) for pavements prone to bleeding due to rich asphalt content. Finally, several previously published macrotexture parameters along with a suite of novel parameters were evaluated for their effectiveness in the prediction of wet weather friction and certain types of road noise. Many of the parameters evaluated outperformed the current metrics of MPD and RMS. / Doctor of Philosophy