No abstract

Pavement frictional behavior affects pavement performance in terms of vehicle safety, fuel consumption, and tire wear. Comprehending and interpreting pavement friction measurements is a challenging task, because of friction sensitivity to several uncontrollable factors. These factors include: pavement surface conditions, such as the type and thickness of contaminants and fluids on the surface and their interaction with friction forces; and the device operating conditions, such as sliding speed, material properties and geometry of the rubber slider used, and operating temperature. Despite the efforts to describe and quantify the impact of varying conditions on pavement friction, which ultimately will allow for a better harmonization of friction measurements, there is a need to better understand the link between the surface texture and physical friction measurements. In this paper, Persson’s friction model is used to analyze and understand the impact of surface texture on frictional behavior of dry pavement surfaces. The model was used to analyze 18 test locations, which were compared with the dry kinetic coefficients of friction (COF) estimated using a British pendulum tester (BPT). The results show that Persson’s friction model could predict the COF estimated from the BPT results with relatively high accuracy. In addition, the model could provide a profound explanation of the frictional forces mechanism. Finally, it was found that the mean profile depth (MPD) cannot provide a full picture of the frictional behavior. However, combining MPD with the Hurst exponent, texture measurements can potentially provide a full physical explanation of the frictional behavior for road surfaces.

This paper describes the process and outcome of deterioration modeling for three different pavement types (asphalt, concrete, and composite) in the state of Iowa. Pavement condition data is collected by the Iowa Department of Transportation (DOT) and stored in a Pavement-Management Information System (PMIS). In the state of Iowa, the overall pavement condition is quantified using the Pavement Condition Index (PCI), which is a weighted average of indices representing different types of distress, roughness, and deflection. Deterioration models of PCI as a function of time were developed for the different pavement types using two modeling approaches. The first approach is the long/short-term memory (LSTM), a subset of a recurrent neural network. The second approach, used by the Iowa DOT, is developing individual regression models for each section of the different pavement types. A comparison is made between the two approaches to assess the accuracy of each model. The results show that the LSTM model achieved a higher prediction accuracy over time for all different pavement types.

No abstract

Control and characterization of pavement roughness is a major quality assurance requirement. With emerging technologies in real‐time monitoring and increasingly stringent requirements to minimize localized roughness features, there is an opportunity to improve upon the traditional quarter‐car (QC) algorithm used to qualify roughness. Current methods suffer from phase lag that mislocates roughness features and require relatively long profiles to achieve high accuracy. In this study, continuous and discrete wavelet bases were modified in the frequency domain to design 116 new QC‐wavelet filters in the spatial domain that were used to analyze 30 road profiles. QC‐wavelet filters were compared to the currently used finite difference algorithm and filtering in the frequency domain. QC‐wavelet filters design based on a Daubechies and nonanalytic Morlet (i.e., db21 and morl0) wavelets outperformed the other filters and algorithms in terms of characterizing overall profiles and accurately quantifying localized features. The major advantages of the new approach include accurately estimating the position and severity of localized feature, and accurately analyzing short profile segments (i.e., <7.62 m).