<b>Enhancing Highway Safety and Construction Quality Control Through Friction-Based Approaches</b>
Creators: name: Bao, Jieyi
Publisher: name: Purdue University Graduate School
Year: 2024
Subjects: classificationCode: 330202
subject: Building construction management and project planning
classificationCode: 350908
subject: Road transportation and freight services
classificationCode: 330201
subject: Automation and technology in building and construction

description: 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.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.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.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.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.