Development of Prediction Models for Skid Resistance of Asphalt Pavements and Seal Coat

Abstract

Maintaining adequate level of skid resistance is essential for road safety. As part of their regular maintenance program, the departments of transportation collect skid data on roads to ensure sufficient traction between road surface and vehicle tires. This study developed models for skid resistance of asphalt pavements and seal coat surfaces. These models predict skid number over time as a function of aggregate gradation, aggregate shape properties (texture and angularity) and its resistance to abrasion and polishing, and traffic level. The researcher examined 70 test sections in this study, half of these test sections were asphalt pavements and other half was seal coat surfaces. Skid data were collected using a skid trailer while surface friction characteristics of the test sections were evaluated using a dynamic friction tester and circular texture meter. In addition, the change in aggregate shape properties due to abrasion and polishing was studied using the Micro-Deval test and Aggregate Image Measurement System. The results demonstrated that the developed models provide good correlation with the skid measurements in the field. This study produced a revised version of a utility called skid analysis of asphalt pavement (SAAP) that incorporate the new models for skid resistance of both asphalt pavements and seal coat. The SAAP can be used by the pavement engineers and contractors to predict skid resistance over time.

Keywords: Skid Resistance, Asphalt Mixtures, Seal Coat, Texture, Angularity, Micro-Deval, AIMS, SAAP, Abrasion, Polishing

Introduction

Skid resistance is a key component in road safety. Proving an adequate level of skid resistance reduces vehicle sliding and stopping distance, especially in wet conditions (1). The friction between pavement surface and vehicle tires is related to the macrotexture and microtexture of pavement surface. The macrotexture of asphalt pavement is dependent on aggregate gradation, while the microtexture is dependent on aggregate shape characteristics. Aggregates with angular shape and rough texture provide higher level of skid resistance compared to aggregates with smooth surface. In addition, pavement surfaces with high macrotexture provide higher skid resistance compared to those with low macrotexture (2, 3, 4). Hall et al. (5) indicated that microtexture locates the magnitude of skid resistance, while macrotexture controls the slope of the skid resistance reduction as the speed increases. It is well established that the skid resistance deceases with time due to the polishing effect of traffic on pavement surface. The polishing action affects both microtexture and macrotexture of pavement surface (6, 7).

Skid resistance has two mechanisms; adhesion and hysteresis. These two mechanisms are highly affected by pavement macrotexture and microtexture (8). The adhesion is developed due to the direct contact between the tires and pavement surface especially in areas with high local pressure (9). Pavement microtexture is significant to the adhesion component that originated from molecular bonds between stone and rubber. In addition, pavement macrotexture contributes to the hysteresis component of the friction (10). Hysteresis is developed due to energy dissipation caused by the deformation of the tire rubber around bulges and depressions in the pavement surface (9). Adhesion and microtexture affect the skid resistance at all speeds, and they have prevalent influence at speeds below 30 mph. Hysteresis and macrotexture have little significance at low speeds; however, macrotexture is an essential factor for safety in wet conditions as speed increases (11).

The seal coat or chip seal is widely used as preventive maintenance treatment and considered relatively inexpensive pavement surface treatment. It can be used effectively on roads with both high and low traffic levels (12) (TxDOT 2003). Similar to hot mix asphalt (HMA) surfaces, the macrotexture and microtexture of seal coat surface have significant contributions to the skid resistance. The macrotexture of pavement surface is affected by the aggregate size and its embedment into the binder. Immoderate embedment may reduce the skid resistance of seal coat (13, 14). In addition, aggregate polishing due to traffic reduces the skid resistance, and the rate of skid reduction depends on the aggregate shape characteristics (2, 15). The seal coat surface treatments (TxDOT Grade 3 and TxDOT Grade 4) provided higher skid resistance compared to asphalt concrete-surfaced pavements (TxDOT Type C), but skid resistance of the surface treatments may decrease significantly once its macrotexture decreases (2).

There are several attempts for developing prediction models for friction and skid resistance of asphalt pavements. Masad et al. (16) developed a new method to evaluate the change in the asphalt pavement skid resistance depending on aggregate texture, properties of mixtures, and environmental conditions. This method relies on the use Micro-Deval test and Aggregate Image Measurement System (AIMS) to evaluate the resistance of aggregate to polishing and abrasion. In the field, they examined nine pavement sections and they prepared laboratory test slabs using different aggregate types and different mixture types. The results showed that aggregate type has significant effect on skid resistance, while the mix type was not statistically significant.

Masad et al. (2) conducted a study that included measurements in the field and laboratory. In laboratory, several slabs with different asphalt mixtures and aggregate types were prepared and tested. A three-wheel polisher was used to polish the test slabs, and the measurements of the friction and mean profile depth were collected using the dynamic friction tester (DFT) and Circular Texture Meter (CTMeter) after different polishing cycles. The results demonstrated high correlation between the aggregate properties and the mixture frictional characteristics. Based on laboratory stage, Masad et al. (2) developed a model to predict the initial, terminal, and rate of change in international friction index (IFI) as a function of aggregate characteristics obtained from AIMS system and aggregate gradation parameters. The data collected in the laboratory were compared to skid values measured in the field for the same asphalt mixtures. Masad et al. (2) proposed a system to predict the skid number of asphalt mixtures as a function of traffic level. Input parameters required for this model included aggregate texture measured using AIMS before and after polishing in Micro-Deval, aggregate gradations, and traffic data.

Wu et al. (17) developed a new model to estimate the skid resistance based on 12 mixtures with various mix types and aggregate sources. The aggregates included sandstone and siliceous limestone and four mix types were evaluated. The model estimates the friction number at 60 km/hr. The researchers also demonstrated that aggregates with low skid resistance can be blended with good quality aggregates in order to achieve adequate skid resistance.

Kassem et al. (4) conducted a study to validate the IFI models developed by Masad et al. (2). Squared-shaped slabs were prepared in the laboratory using three different types of aggregates and different asphalt mixture designs were evaluated. Laboratory slabs were prepared in the laboratory and polished using a three-wheel polisher. The frictional characteristics of the slabs were recorded with polishing. The results demonstrated that the coarse mixtures had better friction compared to fine mixtures. The results demonstrated high correlation between the measured and predicted IFI after considering aggregate texture and angularity indices in the developed model.

In order to improve the safety on highway pavements, the researchers proposed test methods and models to predict tire-pavement friction and skid resistance as a function of aggregate characteristics, mix design and traffic level. These models need to be validated with additional data that cover a wide range of variables and parameters. In addition, these models should be extended or revised to predict the skid resistance of surface treatments such as seal coat. This study had two objectives:

• Investigate and examine surface and friction characteristics of test sections of asphalt mixtures and surface-treated roads in Texas. The test sections covered a wide range of mixtures and aggregate types used in Texas.
• Validate and revise the skid prediction model for HMA; develop a prediction model for skid resistance of seal coat surfaces; and incorporate an improved method of traffic analysis, lane distribution of traffic data, and the effect of the percentage of truck traffic.

Selection of the Field Sections

The researchers measured the frictional characteristics and skid number on a number of HMA and seal coat test sections in Texas. The researchers identified and selected 35 test sections of HMA along with 35 test sections of seal coat. Four seal coat test sections were excluded due to excessive bleeding. During the selection of test sections, the research team made an effort to include surfaces with wide varieties of mixture gradations, aggregate sources, and climatic zones of Texas. Focus was given to identify test sections with higher traffic levels so that the team can observe higher polishing within relatively short time. Another important criterion of test sections selection was to find existing sections with history of skid measurement under TxDOT’s annual network-level pavement evaluation program.  TxDOT does not collect the network-level skid data for all the roads every year. Typically, major highways (i.e., interstate highways) with higher traffic level are tested more frequently than other highways (i.e, farm-to-market roads). The annual skid testing frequency varies among different districts of TxDOT.

The test sections of asphalt mixtures included different mixture type (SMA-C, SMA-D, SMA-F, CMHB-F, Type C, Type D, TOM, PFC, CMHB-C, and CAM), aggregate type (Limestone, Gravel, Granite, Sandstone, Dolomite, Rhyolite, Traprock, and Quartzite), year of construction (2004 to 2013), and were distributed across Texas (ATL, AUS, BMT, BRY, ODA, SAT, YKM, HOU, LRD, PHR, and LFK districts of TxDOT). Also, the test sections of seal coat included different grade type (Grade 3, Grade 4, and Grade 5), aggregates (Limestone, Gravel, Traprock, Sandstone, Dolomite, Rhyolite, LRA, and Lightweight), coating conditions (pre-coated and virgin), year of construction (2009 to 2013), and also were distributed across Texas (ATL, BMT, ODA, SAT, YKM, LRD, PHR, LFK, BRY). Details about the HMA and seal coat test sections are provided in a research report by Chowdhury et al. (18).

Frictional Characteristic Measurements

Measuring Microtexture and macrotexture

Field testing primarily included measurements of friction using the dynamic friction tester (DFT), mean profile depth (MPD) using the circular texture meter (CTMeter), and skid number using the TxDOT’s skid trailer. Figure 1a shows a layout of the test section used by the researchers when taking DFT and CTMeter measurements in the field (Figures 1b and 1c). The CTMeter device was used to measure the MPD, while the DFT was used to measure the coefficient of friction at different speeds (20, 40, 60, and 80 km/hr). During testing, the CTMeter and DFT devices were always positioned in the left wheel path of the outside lane. Six locations were tested in each section. Two locations were at the shoulder, and four locations were in the outer lane. Two DFT and six CTMeter readings were performed at each location. In some cases, where there was no shoulder, the researchers took CTMeter and DFT measurements between the wheel path to represent the initial skid values. The shoulder had higher friction value compared to wheel path as the later experienced frequent polishing under traffic. Measurements of macrotexture and friction were conducted on the outer lane as the skid number was measured by the skid trailer at the outside lane (in case of multiple lanes) on the left wheel path. Also, the outer lane experiences most polishing rates because most of the trucks and other vehicles use this lane.