Influence of Aggregate Properties on Skid Resistance of Pavement Surface Treatments

Abstract

Skid resistance is a critical aspect for traffic safety since it significantly influences vehicle control and minimizes the distance required for emergency braking. The surface characteristics of pavements play a pivotal role in determining skid resistance. To achieve the optimal skid resistance performance, the pavement must sustain a specific level of friction. Thus, it is advantageous to apply surface treatments in areas that require enhanced friction. This study investigate the impact of factors such as the aggregate source, size, morphological properties, and abrasion levels on the skid resistance and frictional characteristics of a high-friction surface treatment (HFST). A complete investigation was conducted on HFST samples by analyzing the aggregate morphology using the Aggregate Image Measurement System and performing Micro-Deval abrasion testing. The skid resistance was evaluated with the British Pendulum Tester (BPT). The findings revealed that different aggregates and sizes exhibited varying behaviors post-polishing. Notably, fine-sized aggregates demonstrated higher British pendulum number (BPN) values, which indicate superior frictional performance. Models that predicted skid numbers based on the average texture and angularity indices initially demonstrated the balanced influences of both morphological properties before polishing. However, after polishing, the surface texture emerged as the primary determinant of the skid resistance, which overshadowed the angularity’s impact.

1. Introduction

Skid resistance refers to the force that prevents tires from sliding on pavement surfaces by creating an opposing force at the tire–pavement contact area. This aspect is crucial for traffic safety, as it plays a vital role in vehicle control and reduces stopping distances during emergency braking scenarios [1,2,3]. The pavement skid resistance is a multifaceted phenomenon driven by two key components: adhesion and hysteresis. Adhesion results from the small-scale bonding and interlocking between a vehicle’s tire rubber and the pavement surface, which depends on the shear strength of the interface and the contact area between the tire and the road. Hysteresis, on the other hand, arises from energy loss within the tire as it moves over the pavement’s texture. The adhesion force is mainly influenced by the micro-level asperities or micro-texture of the aggregate particles within the pavement, while the hysteresis force is largely affected by the macro-level asperities or macro-texture formed on the pavement surface. Previous studies consistently indicate that both the micro-texture and macro-texture of a pavement surface significantly influence its skid resistance [4,5]. During the polishing and abrasion process, traffic loads cause changes in the morphological properties of aggregates, including their form, angularity, and texture, due to the gradual removal of mineral components [6,7]. Different devices were utilized in earlier studies to determine the polishing and abrasion resistances of aggregates. These devices include the Los Angeles abrasion (LAA) machine, Micro-Deval (MD), polished stone value (PSV), Aachen Polishing Machine (APM), Wehner/Schulze (W/S) machine, and road test machine (RTM) [8].

The MD test assesses the abrasion resistance and durability of mineral aggregates by replicating abrasive actions with steel balls in water. This test is particularly valuable because it closely resembles the real-world conditions where aggregates face abrasive forces in the presence of moisture. In real-world conditions, water can infiltrate the interface between aggregates and asphalt in the pavement, which results in adhesion loss and the potential failure of asphalt concrete. Therefore, it is crucial to consider the moist conditions caused by rain, snow, and ice melt throughout the pavement’s service life. The Aggregate Image Measurement System (AIMS), which was created at Texas A&M University, complements the MD test by evaluating the physical attributes of aggregates, like the shape, angularity, and surface texture, both pre- and post-polishing during the MD test [9]. It quantifies the aggregate shape using two-dimensional forms, assesses the angularity by analyzing surface irregularities in black and white images, and examines the surface texture through a wavelet analysis of grayscale images. Mahmoud and Masad [10] utilized a scanning electron microscope (SEM) to analyze aggregate particles before and after the MD test and highlighted the changes in the micro-texture over varying test durations. Similarly, Xue et al. [11] investigated aggregates from different sources pre- and post-MD abrasion tests using aggregate imaging systems (AIMSs), and they concluded that the aggregate morphology significantly impacted the resistance to polishing. Roshan and Abdelrahman discussed how MD test abrasion values are affected by factors like the magnesium sulfate soundness, water absorption, and LAA values of the aggregate. They demonstrated that employing multiple test methods could provide a more reliable assessment of the aggregate durability and abrasion [12].

The British Pendulum (BP) is used to evaluate the polishing resistance of aggregates both before and after being subjected to a polishing process with the British accelerated polishing machine (also known as the British wheel). This test indirectly measures the micro-texture of the aggregate [13].

Bowen Guan et al. observed that there is a crucial threshold in the polishing cycles. When the number of polishing cycles surpasses this threshold, micro-texture becomes the dominant factor in determining the skid resistance of aggregates, thus replacing the role of the macro-texture. Therefore, it can be stated that as traffic continuously traverses the pavement surface, the micro-texture gradually deteriorates [14]. The capacity of the aggregate to preserve its micro-texture is essential for maintaining skid resistance over the lifespan of the pavement. This ability is determined by the aggregate’s resistance to polishing, which is typically measured by the PSV. According to the specification, the initial polish value and the value after 10 h of polishing are recorded for the curved aggregate samples using the BP device [15,16,17].

Studies showed that the friction evolution of aggregates is mainly driven by the micro-texture changes that result from the differential removal of mineral components. Polishing primarily affects the convex areas of the surface texture. In igneous and metamorphic rocks, the variation in hardness of the mineralogical composition and the proportion of minerals with lower hardness are critical factors for maintaining skid resistance [18,19].

To ensure optimal skid resistance performance, the pavement must maintain a certain level of friction. Therefore, applying surface treatments in areas that require increased friction is beneficial, and selecting the right aggregate for these treatments—one that balances texture and durability—is crucial for sustaining the long-term pavement performance. High-friction surface treatment (HFST) has demonstrated its effectiveness and cost-efficiency in improving the pavement friction and significantly reducing crash rates under various traffic conditions worldwide [20]. The HFST consists of a polishing-resistant aggregate applied over a thin layer of polymer binder. This combination enhances the frictional properties of the pavement surface, reduces skidding and accidents, and provides a durable solution to improve road safety. In the United States, epoxy resins are used as the binder, and calcined bauxite (CB) serves as the aggregate in this surface treatment. Previous studies were predominantly focused on specific aspects of pavement skid resistance characteristics and were often limited to a narrow range of aggregate types and sizes [21]. This study aimed to expand the investigation into the skid resistance of surface treatments, like HFST, by examining a wide range of factors. It explored various types and sizes of aggregates and assessed their morphological properties across different polishing levels. The goal was to reveal the relationships between these characteristics and their effects on both the polishing behavior and skid resistance.

This approach allowed for the identification of key determinants and potential interactions between different aggregate properties, such as the texture and angularity. As a result, it provided a more comprehensive understanding of pavement skid resistance under varying conditions of polishing and abrasion, ultimately aiding in more informed decision making for selecting surface treatments.

2. Materials and Methods

2.1. Aggregate

For this study, four types of aggregates commonly used in road pavement surface treatment were selected: calcined bauxite (CB), rhyolite, Meramec, and flint. The selection process was guided by

Table 1. Received aggregate sizes and sources information [22].

Aggregate Type	Commercial Names and Received Sizes	Source	Notes
CB	3/8″ × #3: (MAS) (3/8″), #3 × 0: MAS (#3), and GRIP Grain: MAS (#4).	Great Lakes Minerals, LLC in Wurtland, KY, USA	The GRIP grain CB is exclusively manufactured for high-friction surface treatments (HFSTs).
Rhyolite	1/2″ × 0: MAS (1/2″), and #6 × #16: MAS (#6).	New Frontier Materials in Maryland Heights, MO, USA	The common utilization of this aggregate includes road and railroad construction, as well chip seal.
Meramec	Torpedo Gravel: MAS (1/2″), and Coarse Manufactured Sand: MAS (3/8″)	Winter Brothers Material Company in MO, USA	Meramec serves a dual purpose as both a road and concrete aggregate.
Flint	#6 × #16: NMAS (#6)	Williams Diversified Materials in Baxter Springs, KS, USA	Flint is exclusively manufactured for enhancing friction in surface treatments.

, which provides detailed information on the received sizes, maximum aggregate sizes (MASs), nominal maximum aggregate sizes (NMASs), sources, and additional notes. The gradations and characteristics of these aggregates are outlined in

Table 2. Aggregate properties.

Properties	CB	Rhyolite	Meramec	Flint
Bulk specific gravity	3.25	2.56	2.45	2.51
Water absorption (%)	2.5	0.9	2.6	2.2
Los Angeles abrasion (%)	Grade D	D	D	D
	16	17	15	19
Micro-Deval value (%)	105 min	105 min	105 min	105 min
	5	3.8	1.8	3.8
Aggregate gradation	Coarse size		Fine size
3/8″ (9.5 mm)	100%		na
1/4″ (6.30 mm)	97%		na
No. 4 (4.75 mm)	2%		100%
No. 6 (3.35 mm)	1%		95%–100%
No. 16 (1.18 mm)	na		0%–5%
No. 30 (0.59 mm)	na		0%–0.2%

Note: na—not applicable.

and visually depicted in Figure 1 and Figure 2. Two aggregate gradations were assessed: the smaller size gradation, which was referred to as “fine”, and the larger size gradation, which was denoted as “coarse”. This differentiation was used for the purpose of this research study.

Refractory grade calcined bauxite (CB), according to the AASHTO MP 41 specification, was chosen as the high-friction surface treatment (HFST) aggregate for its superior abrasive properties, high hardness, and long-lasting frictional resistance. Rhyolite, which is described by the Geological Survey as very dense and fine-grained, is resistant to weathering and known as “Traprock” by stone producers [20]. Meramec, which is derived from the Meramec River, is used for both road and concrete construction. Flint is specifically produced to improve the friction in surface treatments.

2.2. Binder

In this research, to prepare the curved samples (coupons), a two-component epoxy resin binder that was suitable for both mechanical and manual applications was used. The two components are recommended to be mixed in a 1:1 volume ratio, with a suggested mixing temperature between 15 °C and 35 °C. The cure time for the epoxy resin binder ranges from 2.5 to 6 h, which depends on the ambient temperature conditions. The epoxy resin has a gel time of 10 min and an ultimate tensile strength of 22 MPa. These details met AASHTO MP 41-22 [23] specifications for epoxy resin binders designed for HFST use.

2.3. Los Angeles Abrasion Test

Conducted in accordance with ASTM C131/C131M-20 and AASHTO T 96 [24,25], this test evaluates the quality, hardness, and durability of aggregates against impact and abrasion. The results offer insights into the aggregate toughness and degradation under heavy weights during compaction and traffic conditions. Grading D was used, as specified in Table 2, with specific parameters determined by the ASTM specifications.

2.4. Micro-Deval Abrasion Test

The aggregates were tested for their degradation and polish resistance using the Micro-Deval (MD) apparatus. This test aimed to assess the aggregates’ durability and resistance to polishing, abrasion, and grinding in the presence of water, as indicated in studies by Li et al. and Wilson and Mukhopadhyay [26,27]. The MD test for coarse aggregates followed ASTM D6928-17 on aggregate sizes ranging from 3/8″ to #4, as outlined in Table 2. The test was conducted for a duration of 105 min [28].

2.5. Aggregate Image Measurement System (AIMS)

Samples of all aggregates were tested using the Aggregate Image Measurement System (AIMS) alongside before and after MD abrasion samples. The AIMS analyzed two sizes (3/8″–1/4″ and 1/4″–#4) for each aggregate to observe changes in the texture indices and angularity indices before and after polishing in an MD device. The angularity indices were determined based on the surface irregularities using black and white images, while the texture indices were derived from grayscale images using the wavelet analysis method [29].

2.6. British Pendulum Test (BPT)

The British Pendulum Tester (BPT) is a widely recognized device used across various research disciplines. Its application is regulated by the standards outlined in ASTM E303 and AASHTO T 278 [30,31]. The BPT primarily measures the frictional forces that arise when a rubber pad slides over a sample surface. It simulates a speed equivalent to 10 km/h or 6 mph to measure micro-texture characteristics [32]. Initially, each sample underwent at least five tests to determine the baseline British pendulum number (BPN) values. After these preliminary tests, the aggregates on the samples were polished using the British wheel. Post-polishing, the BPN values were measured again using the British Pendulum device to obtain the post-polish BPN values.

2.7. Accelerated Polishing of Aggregates Using the British Wheel

The aggregates on the coupons were polished using the British wheel, as outlined in AASHTO T 279-18 [33], after initial testing with the British Pendulum (BP) device. This method simulates the natural polishing that aggregates undergo in the field. In each test run, 14 aggregate coupons were securely clamped around the wheel’s periphery, as shown in Figure 3. The wheel was maintained at a speed of 320 ± 5 rpm, and a pneumatic-tired wheel was lowered to press against the aggregate coupons with a total load of 391.44 ± 4.45 N. This setup allowed the aggregates to undergo a controlled polishing process that replicated the wear patterns they would experience under real road conditions.

2.8. Aggregate Coupons Preparation

The preparation of the coupons (curved specimens) for this research involved several steps:

• Coating the mold bottom: the bottom of the molds was coated with ready-mix plaster, which ensured a smooth and even surface.
• Embedding aggregates: the aggregates were embedded into the plaster within the molds with care to ensure a proper distribution and compaction for uniformity and optimal performance of the samples.
• Pouring the binder: the binders were mixed according to the standard ratio and poured over the embedded aggregates to ensure an even coverage and strong bonding.
• Curing process: the coupons were allowed to cure to enable the binders to set and form a strong bond with the aggregates.

3. Results and Discussion

The experimental data underwent detailed analysis to assess how different aggregate sizes and abrasion levels affected their morphological properties and skid resistances. The British Pendulum Tester (BPT) was used to measure the contact area before and after polishing. Furthermore, the Aggregate Image Measurement System (AIMS) was employed to evaluate how the abrasion impacted the texture and angularity of the various aggregates. The study revealed that variations in the abrasion duration affected the morphological properties of each aggregate, and the differences in aggregate sizes led to varying results.

3.1. Effect of Aggregate Size on Texture and Angularity Indices

Figure 4 and Figure 5 illustrate the texture and angularity indices measured by the AIMS for aggregates of two different sizes, specifically 3/8″–1/4″ and 1/4″–#4, at various stages of polishing: before the Micro-Deval (BMD), and after 105, 180, and 240 min of Micro-Deval polishing (AMD 105, AMD 180, and AMD 240). Decreasing the aggregate size from 3/8″–1/4″ to 1/4″–#4 resulted in reduced texture indices across all phases: BMD, AMD 105, AMD 180, and AMD 240. However, the calcined bauxite demonstrated higher texture indices in the AMD 240 phase within the 1/4″–#4 size category compared with 3/8″–1/4″.

The calcined bauxite, rhyolite, and flint exhibited consistent decreases in their texture indices with increased polishing time for the aggregates sized 3/8″–1/4″. In contrast, for the aggregates sized 1/4″–#4, these aggregates initially showed a decline in texture, followed by an increase in the AMD 240 phase. Meramec, on the other hand, demonstrated an initial rise in the texture indices for both aggregate sizes after polishing to AMD 105, followed by a subsequent decrease. The calcined bauxite showed similar texture indices between the two sizes, while its angularity indices increased progressively for the 105, 180, and 240 min Micro-Deval polishing intervals as the aggregate size decreased. The rise in the texture and angularity indices of the aggregates following the Micro-Deval (MD) polishing can be explained by the fact that some particles within the aggregates broke during the MD polishing, which exposed their internal surfaces. This increased surface roughness could contribute to a higher texture and angularity. Also, the MD polishing may have revealed textured surfaces that were previously obscured by smoother layers. This exposure of previously concealed textures could result in an increase in both the texture and angularity indices as the rougher surfaces became more prominent.

3.2. Relationships between Micro-Deval Polishing Times and Texture and Angularity Indices

Figure 6 depicts how the MD abrasion times related to the average AIMS texture indices for each type of aggregate. It was observed that the calcined bauxite, rhyolite, and flint showed exponential decay patterns in their relationships. On the other hand, the Meramec displayed a polynomial trend rather than exponential decay. The fitted curve formulas for each aggregate type, along with their corresponding R-squared values and sum of squared errors (SSEs), are displayed in the plots. These formulas were derived to model the relationships between the Micro-Deval abrasion times and the average AIMS texture indices, which provide insights into how each aggregate type responds to different polishing durations.

Figure 7 illustrates the relationships between the angularity indices and Micro-Deval (MD) polishing times for the different aggregates. The calcined bauxite and rhyolite showed exponential decay patterns, whereas the Meramec displayed a polynomial trend. Each aggregate type was fitted with a curve formula, and the corresponding plots include metrics such as R-squared values and SSEs to describe the goodness of fit for the models. The higher SSE values were due to some data points being further from the fitted curve line, which increased the overall error.

The Meramec exhibited the lowest R-squared value, which was primarily attributed to its angularity fluctuating during the polishing process. Initially, the angularity decreased, followed by an increase with longer polishing times. This phenomenon likely resulted from particle breakage during the abrasion, which exposed surfaces with higher angularity as the polishing progressed through various phases. Five BPN measurements were conducted for each aggregate coupon before and after the polishing process, and the average BPN was then calculated.

3.3. Effect of Different Aggregate Size on the British Pendulum Number

Figure 8 illustrates the BPN values measured before and after the 10 h polishing cycles on the British wheel for various aggregates, where it compares both the fine and coarse sizes. The calcined bauxite consistently exhibited the highest BPN values among all aggregates and sizes, both before and after polishing, followed by the flint, Meramec, and rhyolite, which showed comparable results. The fine-sized samples demonstrated higher BPN values in all the aggregates except the flint. This increased frictional performance could be attributed to several factors. First, the geometric characteristics of the aggregate particles, specifically their higher angularity and rougher surface texture, as shown in Figure 5, where angularity increased with smaller sizes, especially in the calcined bauxite and rhyolite. Additionally, the preparation method for the fine-sized test coupons may have contributed. Due to their small size, fine aggregate particles could not be individually placed in the mold like coarse particles were. Instead, these smaller particles were evenly distributed across the bottom of the mold to form a uniform layer before the epoxy was poured over them to secure them in place. This method likely resulted in greater variability in the fine-sized samples, which led to higher BPN values.

3.4. Relationship between Aggregate Image Measurement System and British Pendulum Test Results

Figure 9 and Figure 10 provide a comparison of the average Aggregate Image Measurement System (AIMS) texture and angularity indices for the aggregate sizes 3/8″–1/4″ and 1/4″–#4 with the average British pendulum number (BPN) values across different aggregate types and sizes. The average pre-polish BPN values were compared with the average AIMS texture and angularity indices before the Micro-Deval polishing (BMD). Meanwhile, the average post-polish BPN values were contrasted with the average AIMS texture and angularity indices after 240 min of Micro-Deval polishing (AMD 240). The fit curve formula, along with the R-squared values and SSEs, is provided on each plot for both the pre- and post-polishing to evaluate the relationship between the texture and angularity indices and the BPN values.

Figure 9 shows relationships characterized by second-degree polynomials between the average texture and average BPN results before polishing and third-degree polynomials after polishing, which demonstrated the highest R-squared values and lowest SSEs among the different aggregates. This trend highlights the variability in results across different aggregates. Upon analyzing these variations, it is evident that the Meramec showed the lowest BPN and texture values before polishing, whereas the calcined bauxite consistently displayed the highest BPN values and the second-highest texture values both before and after polishing. The same relationship was observed in Figure 10, where second-degree polynomials were noted between the average angularity and average BPN results before polishing and third-degree polynomials were observed after polishing. The CB exhibited the highest BPN values, while the rhyolite showed the highest angularity both before and after polishing. The nonlinear polynomial curve provided a more precise fit and accurately predicted the BPN values based on the angularity, as evidenced by the ideal SSE and R-squared metrics.

3.5. Skid Number Modeling Based on Texture and Angularity Indices

Based on the fitted curve formulas between the average BPN values and texture index, as well as the angularity index before and after polishing, the relationship between the combined texture and angularity indices to BPN values before and after polishing could be determined. To integrate these two formulas into a unified equation for the skid number (SN) based on the BPN values that accounted for both the texture index and angularity index, it was important to recognize their combined impact on the overall skid resistance. Using the actual data from testing, a straightforward optimization method was employed to identify the optimal weighting factor that balanced the influence of the texture and angularity on the SN.

In Python, the SciPy minimize_scalar function was employed to determine the optimal weights of the texture and angularity that affected the BPN, with the aim to minimize the error. Brent’s method was utilized within minimize_scalar, which iteratively tested various weight combinations for texture and angularity, by evaluating the mean squared error (MSE) at each step to narrow down the optimal weight within the specified range [34].

Based on the analysis, the combined formula derived from the texture and angularity provided the model prediction for SN according to Equation (1):

SN = −0.001282t² + 0.5594t + 0.0000406a² − 0.2491a + 380.9195

(1)

where “SN” represents the skid number before polishing, “t” represents the average texture index before polishing, and “a” denotes the average angularity index before polishing.

Table 3. Actual and predicted SNs based on model.

Actual SN	Predicted SN	Mean Squared Error (MSE)
71.5	65.35	18.34
58	62.54
61	62.96
58	60.83

presents the actual and predicted SNs based on the provided model.

A similar procedure was employed for the BPN values, texture, and angularity after polishing to establish a model for the SN based on the fitted formulas in Figure 9 and Figure 10.

Based on the analysis, the combined formula derived from the texture and angularity after polishing (AMD 240) provided the model’s prediction for SN, as described by Equation (2):

SN = −0.000057t³ + 0.0309t² − 5.33t + 347.26 + 0.000004236a² − 0.01116a + 9.6962

(2)

In Equation (2), “SN” denotes the skid number after polishing, “t” represents the average texture index after polishing, and “a” denotes the average angularity index after polishing.

Table 4. Actual and predicted SNs based on model.

Actual SN	Predicted SN	Mean Squared Error (MSE)
61	57.80	11.31
48.50	51.88
50.50	51.97
56	54.86

displays the actual and predicted SN values based on the provided model.

In Equation (1), the optimal weight (approximately 0.4206) was found to represent a balanced influence between the texture and angularity on the SN. In Equation (2), it was determined through the optimization process that the texture index was significantly more influential than the angularity index in predicting the skid number. This suggests that after polishing, the dominant factor in determining the skid resistance was the surface texture, while the effect of angularity was much smaller.

4. Conclusions

Based on the findings of this study on the impact of different aggregate types and sizes under varying polishing and abrasion levels on the skid number (SN), several conclusions could be drawn:

The increases in the texture and angularity indices of the aggregates after the MD abrasion were attributed to the breakage of particles during the process, which exposed the rougher internal surfaces. This phenomenon enhanced the surface roughness, which contributed to higher texture and angularity indices in some aggregates. Furthermore, the MD polishing revealed textured surfaces that were previously obscured by smoother layers, which influenced the observed texture and angularity changes.

Different aggregates and sizes exhibited varied behaviors post-polishing. For instance, the calcined bauxite (CB) showed an increase in texture in the fine-sized aggregates after 240 min of polishing, while the Meramec displayed an initial rise in the texture indices for both aggregate sizes after polishing to AMD 105, followed by subsequent decreases.

The fine-sized samples demonstrated higher BPN values, which were indicative of improved frictional performance. This could be attributed to the geometric characteristics of the aggregate particles, particularly their higher angularity and rougher surface texture. The preparation method for fine-sized test coupons, where particles were uniformly distributed and secured with epoxy, could have contributed to the observed higher BPN values.

For the fine-sized aggregates, the highest BPN value was associated with the calcined bauxite at 73, while the lowest was related to the rhyolite at 58. The flint exhibited the smallest decrease in BPN after polishing at 10%, followed by the Meramec and calcined bauxite, with the highest decrease seen in the rhyolite at 17.24%.

For the coarse-sized aggregates, the highest BPN value was associated with the calcined bauxite at 70, while the lowest was related to the Meramec at 46. The flint again had the lowest percentage decrease in the BPN after polishing at 6.45%, followed by the calcined bauxite and Meramec, while the rhyolite showed the highest decrease of 15.52%.

The models that predicted the SN based on average texture and angularity indices revealed the optimal weight (approximately 0.4206) that provided a balanced influence between the texture and angularity on the SN before polishing. However, after polishing, the surface texture became the predominant factor that determined the skid resistance, which overshadowed the effect of the angularity.

In conclusion, this study underscored the complex interplay between the aggregate type, size, polishing conditions, and their effects on the surface properties relevant to the skid resistance. Understanding these dynamics is crucial for optimizing the pavement performance and safety in various road conditions.