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Roadway surface friction along the West Virginia Division of Highways' roadway network is key to the safety of all traveling motorists. Being geographically located in the rugged Appalachian Mountains, the West Virginia Division of Highways' roadway network is flooded with innumerable geometric and design challenges, causing drivers to have to exercise the most care and attention when navigating the network. This dissertation introduces the concept of roadway surface friction management to this network. For decades, roadway surface friction has only been tested and checked on an as-needed basis at crash sites and intersections, in legal situations, and pavement acceptance on construction projects. It also seeks to use the acquired data through a case study to insure proper methodology of roadway surface friction management, to develop sample safety performance functions and best crash estimates, and to apply this decision-making data to provide assistance and guidance in the selection of projects in the West Virginia Highway Safety Improvement Program. This dissertation follows the manuscript format and is composed of three papers. The first chapter of the dissertation examines the usage of Method 3 of the AASHTO Guide for Pavement Friction and the modifications to this method to collect existing roadway surface friction data along the District Ten portion of the network. The second chapter of the dissertation discusses the development of sample safety performance functions to estimate the average number of crashes along each of the tested roadway categories: Interstate Routes, United States Routes, and West Virginia Routes. It also discussed the development of best crash estimates using the Empirical Bayes Method. This is essential to be able to forecast how crash counts should improve, given the application of various roadway improvements. The third and final chapter of the dissertation develops the case study based on the District Ten portion of the network and shows how to enhance project selection in the West Virginia Highway Safety Improvement Program. This is completed by applying the safety performance functions and best crash estimates from the second chapter to arrive at real friction numbers for the network and their project impacts. / Doctor of Philosophy / Roadway surface friction along the West Virginia Division of Highways' roadway network is key to the safety of all traveling motorists. Being geographically located in the rugged Appalachian Mountains, the West Virginia Division of Highways' roadway network is flooded with innumerable geometric and design challenges, causing drivers to have to exercise the most care and attention when navigating the network. This dissertation introduces the concept of roadway surface friction management to this network. For decades, roadway surface friction has only been tested and checked on an as-needed basis at crash sites and intersections, in legal situations, and pavement acceptance on construction projects. It also seeks to use the acquired data through a case study to insure proper methodology of roadway surface friction management, to develop sample safety performance functions and best crash estimates, and to apply this decision-making data to provide assistance and guidance in the selection of projects in the West Virginia Highway Safety Improvement Program. This dissertation follows the manuscript format and is composed of three papers. The first chapter of the dissertation examines the usage of Method 3 of the AASHTO Guide for Pavement Friction and the modifications to this method to collect existing roadway surface friction data along the District Ten portion of the network. The second chapter of the dissertation discusses the development of sample safety performance functions to estimate the average number of crashes along each of the tested roadway categories: Interstate Routes, United States Routes, and West Virginia Routes. It also discussed the development of best crash estimates using the Empirical Bayes Method. This is essential to be able to forecast how crash counts should improve, given the application of various roadway improvements. The third and final chapter of the dissertation develops the case study based on the District Ten portion of the network and shows how to enhance project selection in the West Virginia Highway Safety Improvement Program. This is completed by applying the safety performance functions and best crash estimates from the second chapter to arrive at real friction numbers for the network and their project impacts.

Skidding contributes to up to 35% of wet pavement accidents. Pavement surface friction therefore is an important component of highway safety. The skid resistance also varies seasonally and reduces over time due to surface polishing. These leave the pavement in a state of increased risk of skidding accidents. An adequate surface friction that accommodates the seasonal and long term variations is essential for safety over the pavement surface service life. The resistance to skidding, however, depends on surface microtexture and macrotexture. Alternatively, increased texture aimed at increased and durable surface friction may affect the noise generated on the road. In fact, traffic noise is a growing problem throughout the world. Noise barriers, traditionally used for noise reduction, are expensive and inefficient in some cases. As the pavement surface characteristics play a key role in noise generation and propagation, it provides a window for noise reduction by altering the pavement surface. The challenge, however, is to provide a smooth, quiet, long-lasting, and economic pavement with adequate and durable surface friction. This research has been directed to address this challenge and to provide a realistic guideline. The tire-pavement noise, sound absorption, and skid resistance performances of various flexible and rigid pavement surfaces have been examined using the field and laboratory test data. Models for the prediction of pavement skid resistance including the seasonal and long term variations have also been developed correlating the influencing factors. A value engineering approach has been proposed to accommodate the construction and maintenance costs, longevity, smoothness, safety and noise in the selection of pavement surfaces.

Skidding contributes to up to 35% of wet pavement accidents. Pavement surface friction therefore is an important component of highway safety. The skid resistance also varies seasonally and reduces over time due to surface polishing. These leave the pavement in a state of increased risk of skidding accidents. An adequate surface friction that accommodates the seasonal and long term variations is essential for safety over the pavement surface service life. The resistance to skidding, however, depends on surface microtexture and macrotexture. Alternatively, increased texture aimed at increased and durable surface friction may affect the noise generated on the road. In fact, traffic noise is a growing problem throughout the world. Noise barriers, traditionally used for noise reduction, are expensive and inefficient in some cases. As the pavement surface characteristics play a key role in noise generation and propagation, it provides a window for noise reduction by altering the pavement surface. The challenge, however, is to provide a smooth, quiet, long-lasting, and economic pavement with adequate and durable surface friction. This research has been directed to address this challenge and to provide a realistic guideline. The tire-pavement noise, sound absorption, and skid resistance performances of various flexible and rigid pavement surfaces have been examined using the field and laboratory test data. Models for the prediction of pavement skid resistance including the seasonal and long term variations have also been developed correlating the influencing factors. A value engineering approach has been proposed to accommodate the construction and maintenance costs, longevity, smoothness, safety and noise in the selection of pavement surfaces.

<p dir="ltr">Pavement friction is fundamental to the safety of road networks. A precise assessment of friction levels is essential for the strategic development of maintenance practices and policies by state highway agencies. Typically, assessments of pavement friction have been conducted individually, focusing on particular segments of roadways. Nevertheless, this approach does not offer a thorough evaluation of roadway friction conditions at the network level. This study combines the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the Gaussian Mixture Model (GMM) to evaluate the ratings of pavement friction throughout the entire state’s road system. A dataset oriented towards safety, serving as input for clustering models across various data dimensions, has been established. Through comparative and statistical analyses, six friction performance ratings have been identified and subsequently validated. The findings not only facilitate a deeper comprehension of the interrelations among friction levels, crash impact, and additional factors impacting safety, but also provide substantial insights for the advancement of road safety, management, and development.</p><p dir="ltr">Pavement markings play an essential role in regulating traffic flow and improving traffic safety. Beyond facilitating road safety via visual cues to drivers, the frictional properties of pavement marking surfaces are a pivotal element in safeguarding roadway safety. However, the friction characteristics of pavement marking surfaces have not been sufficiently investigated. Additionally, the integration of glass beads or other particles with pavement markings to enhance reflectivity and retroreflectivity complicates the study of their friction properties compared to bare pavements. To tackle these problems, this research utilizes the British pendulum tester (BPT), the circular track meter (CTM), the dynamic friction tester (DFT), and the three-wheel polishing device (TWPD) to evaluate the friction performance of various pavement markings. Eighteen specimen groups, comprising six types of markings (i.e., waterborne paint, preformed tape, epoxy paint, polyurea paint, MMA paint, and thermoplastics) with various glass beads and particles, were investigated to assess their impact on dry and wet friction, mean profile depth (MPD), and durability. The outcomes of this study serve as valuable resources for advancing safety measures and providing insights into emerging traffic management technologies.</p><p dir="ltr">Currently, there is an absence of established standards or methods for assessing and evaluating the friction characteristics of road markings. This lack of standardization has a pronounced impact on vulnerable road users-motorcyclists, bicyclists, and pedestrians-due to the potential for inadequate friction from road markings. To address the problem, this study has developed five friction levels based on the wet British pendulum number (BPN). Leveraging international standards and practical considerations, a tentative BPN range is advocated for crosswalks, symbols, and letters to enhance the safety of pedestrians and other susceptible road users.</p><p dir="ltr">Friction metrics, like MPD and friction number (FN), have been central to enhancing quality assurance and control (QA/QC) processes in chip seals. These metrics evaluate chip seal performance by examining problems such as aggregate shedding or significant bleeding, potentially leading to lower friction values or surface textures. However, instead of leading to slippery conditions, the loss of aggregate-particularly as a consequence of snow-plow operations-may result in the formation of uneven surface textures. The relationship between increased MPD or FN and enhanced chip seal quality is complex and not easily defined. This study introduces a groundbreaking method utilizing machine learning techniques, designed to improve the QC procedure for chip seals. A hybrid anomaly detection approach was applied to a dataset consisting of 183,794 MPD measurements, each representing the average mean segment depth (MSD) over 20-meter segments, gathered from real-world chip seal projects throughout the six districts managed by INDOT. A two-phase QC process, specifically tailored for chip seal quality assessment, has been developed. Validation analysis performed on four chip seal projects shows a strong concordance between field inspection, friction measurements, and the results predicted using the introduced approach. The developed method sets a foundational chip seal QC procedure, augmenting efficiency in acceptance processes and overall safety through data-driven techniques, while reducing the practitioners' time on site.</p><p dir="ltr">Surpassing the constraints of traditional approaches, this paper develops a series of scientific methodologies for evaluating friction on pavement and pavement marking surfaces through extensive in-field and laboratory experiments. Additionally, it establishes rational and efficient quality control procedures for chip-seal applications. The methodologies and conclusions presented in this paper can assist engineers in Departments of Transportation (DOTs) with ensuring the safety of all stakeholders, including road users, engineers, and construction practitioners. Furthermore, they offer valuable insights for the timely execution of road maintenance activities.</p>

Draft tube is very important part of hydraulic turbines. Only optimum work together with turbine can bring highest performance of this machine set. Hence it is necessary to deal with character of flow in the draft tube for different operating conditions. Efficiency of the draft tube depends on many phenomena of flow. Observing these phenomena and finding their relation with energetic transformation in the draft tube is a suitable tool to judge quality of draft tube performance. Incorrect design of the draft tube can sometimes cause lower efficiency of whole machine set. The goal of this thesis is finding the main reasons causing draft tube efficiency drop for given operating conditions.