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.

Safety performance is a crucial component of highway network performance evaluation. Besides their devastating impact on roadway users, traffic crashes lead to substantial economic losses on both personal and societal levels. Due to the complexity of crash events and the unique conditions in each country and state, empirical local calibration for the correlation between attributes of interest and the safety performance is always recommended. Limited studies have established a procedure to analyze the impact of pavement condition on traffic safety in a risk analysis scheme. This study presents a thorough analysis of some roadway departure crashes which occurred in Iowa between 2006 and 2016. All crash records were mapped onto one-mile segments with known traffic volume (i.e., AADT), posted speed limits (SL), skid numbers (SN), ride qualities (IRI), and rut depths (RD) in a geographic information system (GIS) database. The crash records were correlated to the pavement surface condition (i.e., SN, IRI, and RD) using negative binomial regression models. Moreover, a novel risk analysis framework is introduced to perform crash risk assessment and evaluate the possible consequences for a given combination of events. The analysis shows a significant impact of pavement skid resistance on roadway-departure crashes under all accident conditions and severities. Risk analysis will facilitate coordination between the pavement management system and safety management system in the future, which will help with optimizing the overall highway network performance.

Pavement roughness is a key parameter for controlling pavement construction processes and for assessing ride quality during the life of a pavement system. This paper describes algorithms used in processing three-dimensional (3D) stationary terrestrial laser scanning (STLS) point clouds to obtain surface maps of point wise indices that characterise pavement roughness. The backbone of the analysis is a quarter-car model simulation over a spatial 3D mesh grid representing the pavement surface. With the rich data-set obtained by 3D scanning, the algorithms identify several dynamic responses and inferences (suspension, acceleration and jerk) at each point in the domain. Variability in the indices is compared for a ‘rough’ pavement and a ‘smooth’ pavement in the spatial domain for different speed simulations of the quarter-car model. Results show high spatial variability in the various roughness indices both longitudinally and transversely (i.e. different wheel path positions). It is proposed that pavement roughness characterisation using a spatial framework coupled with univariate statistics provides more details on the severity and location of pavement roughness features compared to the (1D) one-dimensional methods. This paper describes approaches that provide an algorithmic framework for others collecting similar STLS 3D spatial data to be used in advanced pavement roughness characterisation.

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).

It is universally accepted that curling and warping of jointed plain concrete pavements (JPCP) can influence pavement performance. Recent studies indicated that long-term increases in roughness for pavements could also be largely related to curling and warping changes. The objective of this study is to investigate behaviours of idealised curling profile and resulting values of parameter pseudo-strain gradient (PSG) in the frequency domain from profiles acquired from 30 sections to offer supplementary information on the characteristics of curling profile of interest. Advanced wavelet techniques, including both continuous wavelet transform (CWT) and discrete wavelet transform (DWT), were utilised to decompose profiles into different layers of details in corresponding frequency bands for analysis. A quarter-car (QC) model was applied to simulate each decomposed layer, then frequency-domain changes in PSG and International Roughness Index (IRI) were compared, with results indicating that roughness resulting from curling profile could be strongly correlated to PSG in the frequency domain. The standard deviation for the level of details $D_6$ from DWT also shows a better correlation to PSG than IRI in the spatial domain. The dominated frequency band for curling profile was identified to be 0.12–1.25 circle/m. Differences between CWT and DWT for profile filtering were also discussed.