1. Introduction
As urbanization intensifies, the functionality and growth of cities increasingly hinge on road traffic efficiency. The surge in traffic volume, coupled with stringent environmental standards, has accentuated the need for diligent urban road functional health monitoring. This includes a thorough assessment and maintenance of road surface conditions, with a keen focus on safety and environmental attributes. Cement concrete pavement, a predominant choice for urban roads, plays a crucial role in this context, particularly due to its anti-skid and noise dampening properties [
1,
2,
3,
4]. Optimal skid resistance is vital for ensuring vehicular safety in adverse weather conditions like rain and snow [
5,
6]. Ensuring optimal skid resistance is paramount to vehicular safety, especially under adverse weather conditions such as rain or snow. Concurrently, the mitigation of road traffic noise, a significant source of urban pollution, is essential for enhancing the urban living experience [
7,
8].
Grooving techniques, esteemed for their cost-efficiency and simplicity, are prevalently employed to augment the performance of cement concrete pavements. Yet, the efficacy of these techniques in enhancing skid resistance and reducing tire/pavement noise is profoundly influenced by a multitude of factors including groove dimensions, spacing, and orientation, and vehicular attributes like tire patterns, weight, and velocity.
The relationship between pavement performance and its texture characteristics—both macro and micro—is well-established in the realm of skid resistance. Extensive studies leveraging traditional and advanced methodologies have scrutinized the influence of texture dimensions on skid resistance, utilizing tools ranging from the British Pendulum Tester (BPT) and Surface Friction Tester (SFT) to high-speed sideway force coefficient testing vehicles and high-resolution laser scanners. These investigations have elucidated that nuanced adjustments to texture parameters can markedly affect key metrics like the high-speed sideway force coefficient (SFC), mean profile depth (MPD), and British Pendulum Number (BPN). For instance, a texture depth of 2 mm and adequate surface area are pivotal for optimal pavement friction performance and longevity, with a texture wear rate beyond 40% significantly impairing braking skid resistance [
9,
10,
11,
12,
13,
14]. Innovative studies integrating optical scanning, image analyses, and advanced modeling techniques have further refined the precision in predicting skid resistance, offering novel insights into the intricate interplay between texture characteristics and skid resistance [
1,
15,
16,
17,
18,
19,
20]. These advancements provide invaluable technical and theoretical underpinnings for the design of effective anti-skid pavements. Globally, research institutions have rigorously evaluated a spectrum of surface textures on experimental road sections, striving to pinpoint the ideal anti-skid texture configuration. In the USA, the focus is on optimizing transverse grooves with parameters like a groove width of 2.3–3.2 mm, depth of 3.2–4 mm, and spacing of 12–25 mm that significantly boost friction coefficients and safety [
8,
21,
22,
23]. Denmark employs grooving or embossing techniques coupled with seasonal treatments to elevate skid resistance [
24]. Japan advocates for specific groove configurations on airport pavements to ensure safety, while in China, specific groove patterns are recommended to bolster lateral anti-skid performance [
25,
26,
27]. These comprehensive studies offer substantial practical insights and technical recommendations for the crafting of anti-skid pavement designs.
In noise reduction performance, the On-Board Sound Intensity (OBSI) method is commonly used for noise level assessments. Cement pavement noise levels increase with macro-texture depth, but under specific conditions, a shallower texture depth (e.g., 0.8 mm) can effectively reduce noise. Additionally, increased pavement texture complexity and International Roughness Index (IRI) also raise noise levels [
28,
29,
30]. New three-dimensional pavement texture metrics have proven superior in noise evaluation for unevenly textured surfaces. Moreover, texture-based noise models, such as the Statistical Energy Analysis (SEA) and Vibration Model (VM) algorithms, have enhanced our understanding of the relationship between texture level and noise frequency, with digital image processing revealing the critical role of micro-texture in noise reduction [
31,
32,
33]. Material-wise, rubberized pavements show significant potential in reducing high-frequency noise. Comparisons indicate that small-particle exposed-aggregate concrete pavements outperform transverse grooved surfaces in noise reduction, demonstrating that precise texture spacing and depth adjustments can effectively lower noise in specific frequency bands [
27,
34,
35,
36,
37,
38]. Regarding transverse grooves’ impact on noise, the spacing between grooves has the most significant correlation with noise levels. Uniformly spaced transverse grooves create harsh tonal peaks, whereas non-uniformly spaced grooves can effectively eliminate these peaks, although the overall noise may be the same or increased. Combining angled and varied spacing grooves can effectively eliminate tonal peaks and reduce overall noise, but angled grooves introduce construction challenges [
39,
40,
41,
42,
43,
44].
There remains a relatively narrow focus within the research community on the collective impact of pavement texture on anti-skid efficiency and noise reduction. Recent researches have utilized clustering analyses to classify pavement characteristics, advocating for benchmarks such as a minimum high-speed sideway force coefficient (SFC) of 50 for tunnel pavements to safeguard against skidding, and a noise threshold of 105 dB to preserve a comfortable environment [
45]. Investigations into assorted pavement designs have highlighted the benefits of wider groove spacing—extending up to 25 cm—not only for its efficacy in curtailing pumping noise but also in averting lateral vehicular slippage, thus endorsing it as a sound methodological choice for cement concrete pavements in tunnel sections of highways [
46]. Although there has been a concerted effort to unravel the interplay between pavement textures and their skid-resistant and noise-dampening properties, research that meticulously examines the balance between enhancing skid resistance and suppressing noise is sparse. This observation points to an existing gap in research—an oversight in the comprehensive optimization of pavement texture design, which is pivotal for advancing the field.
Leveraging the existing research, this study conducted an in-depth analysis of how textures affect pavement anti-skid properties and noise reduction capabilities.
Section 2 outlines the field experiments that examined both the grooving and burlap drag texture parameters, in addition to key pavement performance metrics such as mean profile depth, high-speed sideway force coefficient, and tire/pavement noise levels. In
Section 3, the focus shifts to delineating texture characteristics, followed by an exploration of the relationship between grooved and burlap drag textures and the pavement’s skid resistance.
Section 4 ventures into the realm of tire–pavement noise characteristics, analyzing the impact of various texture characteristics on noise levels associated with pavements. Utilizing the TOPSIS multi-objective optimization approach in
Section 5, the study assessed eight experimental scenarios on the test sections, balancing considerations of skid resistance and noise reduction, to identify optimal texture configurations for speeds of 60 km/h and 80 km/h. This research aimed to pinpoint the ideal pavement texture design that maximizes overall performance, thereby enhancing driving safety and minimizing noise pollution.
3. Pavement Texture Characteristics and Skid Resistance Analysis
3.1. Texture Characteristic Description
Different texture techniques generate distinct pavement surface profiles. Grooving results in undulations perpendicular to the travel direction, creating longitudinal texture contour curves. Conversely, burlap dragging yields textures parallel to the travel direction, resulting in transverse contour curves, as illustrated in
Figure 7. The interweaving of textures from burlap dragging and grooving sketches a grid-like design on the pavement. To conduct a comprehensive evaluation, this study identified three critical parameters, groove width, groove depth/height, and groove spacing, which were measured for various grooved texture types.
Construction process variability leads to fluctuations in actual groove width, depth, and spacing. Groove textures, produced through mechanical cutting, exhibit minimal variations. Therefore, after multiple point measurements, mean values were computed to represent each condition’s texture characteristic values.
Table 1 details the groove texture parameters for the evaluated pavement sites.
Due to the predominant formation of raised grooves in burlap dragging, this study utilized the groove height, width, and spacing as evaluation metrics, akin to the system used for grooved textures. Repeated measurements of the burlap drag texture in specific zones gathered extensive data to capture the variability of burlap drag textures under differing conditions.
Figure 8 displays the burlap drag textures on eight pavement types, with
Table 2 listing the burlap drag texture parameters at these sites.
3.2. Analysis of Mean Texture Depth
The Mean Texture Depth (MTD) serves as a critical measure of the pavement’s texture depth, illustrating the surface’s roughness and its substantial impact on the tire–pavement interactions and traction. Generally, a higher MTD signifies enhanced water drainage and improved skid resistance. The MTD values for different pavement texture sites are listed in
Table 3.
The observed variation in MTD values across test sites 1 to 8 (ranging from 0.535 mm to 0.955 mm) underscores the influence of texture parameters on the pavement’s water drainage and grip. Notably, broader and deeper grooves contributed to superior water displacement, likely resulting in increased MTD values. For instance, site 1, characterized by a deeper groove (2 mm) and wider width (4.0 mm), aligns with the highest MTD value (0.955 mm), suggesting effective water drainage and potential for enhanced skid resistance. Conversely, site 7 exhibited a lower MTD value (0.535 mm) due to its shallower groove depth (0.5 mm), despite a comparable groove width (4.0 mm), indicating reduced drainage efficiency. Additionally, the texturing technique, including equal and unequal transverse spacing, plays a role in determining MTD values, with uneven spacing potentially affecting water flow and, consequently, MTD values.
3.3. Analysis of High-Speed Sideway Force Coefficient
A greater SFC value denotes superior skid resistance, providing more effective lateral grip, minimizing vehicle lateral movement, and thereby boosting driving stability and safety. The SFC values were continuously assessed at each test site, where each segment of 100 m was delineated as an individual unit. Two such units were established, and their respective SFC values were computed as an average. The SFC values for the various sites are presented in
Table 4.
The findings suggest that, in grooved textures, an increased groove width or depth typically correlates with enhanced friction points and improved water removal capability, contributing to higher SFC values. Sites 1 and 5, with their wider and deeper grooves, exhibited higher SFC values, indicating strong lateral traction. Interestingly, site 4 showed the highest SFC despite site 6 having the densest texture, indicating that optimal groove spacing can enhance the contact area between the tires and pavement, thus boosting the SFC. Comparatively, sites with uniform texture spacing, like sites 4 and 8 or 3 and 7, demonstrated higher SFC values, presumably due to the consistent grip provided by even spacing, which helps stabilize the SFC values. Variable spacing may result in inconsistent lateral grip, potentially impacting SFC stability.
In the context of burlap drag textures, variations in texture parameters significantly affected the contact nuances between the pavement and tires. While wider grooves might reduce local contact areas, an optimal width can elevate friction edges, thus improving the SFC. Sites 4 and 5, with their broader grooves, are likely to offer enhanced edge friction, influencing their SFC values. Groove spacing impacts texture density and continuity; tighter spacing in burlap drag textures could provide more continuous friction edges, aiding in lateral grip. However, excessively close spacing might lead to friction saturation, failing to provide additional lateral traction.
3.4. Correlation Analysis
To quantify the strength of the association between the parameters of grooved and burlap drag textures and the skid resistance indicators of pavement, a correlation analysis was performed, relating these textures to MTD and SFC values.
Table 5 displays the correlation coefficients, and
Figure 9 illustrates the relationships between the various texture parameters and MTD and the SFC at 8 test sites.
The analysis indicated that groove textures significantly influenced MTD and the SFC, underscoring the importance of texture dimensions on pavement performance. The depth of the groove emerged as a critical factor affecting MTD, whereas the width predominantly influenced the SFC. The burlap drag textures exhibited a subtler impact, likely affecting the micro-texture characteristics rather than macro-texture attributes.
A strong positive correlation was observed between groove depth and MTD (0.5397), suggesting that deeper grooves result in greater texture depth, as depicted in
Figure 9b. Conversely, the relationship between groove depth and the SFC was minimal (0.0504), indicating that depth alone does not directly impact surface friction. Groove width negatively correlated with MTD (−0.0912), likely because wider grooves reduce the per-unit-area texture, diminishing the texture depth. Moreover, wider grooves decreased the effective contact area between pavement and tire, resulting in a notable negative correlation with the SFC (−0.5453). Groove spacing influenced texture continuity and density, leading to a negative correlation with MTD (−0.4835). As the spacing increased, the SFC first rose and then declined, suggesting that optimal spacing can enhance tire–pavement contact but after becoming excessively large, it causes uneven contact areas.
In burlap drag textures, the positive correlations of groove width, height, and spacing with MTD (0.0717, 0.2161, 0.2128) were relatively weak, as illustrated in
Table 5, reflecting the limited contribution of these smaller characteristic sizes to the overall texture depth. The positive correlations of groove width, height, and spacing with the SFC (0.2516, 0.1497, 0.3669) indicate that these parameters notably influence the SFC, possibly by affecting the texture density and distribution, thereby enhancing the micro-contact points between the tires and pavement.
5. Optimization of Skid Resistance and Noise Reduction Texture Using TOPSIS
5.1. Method Overview
This study’s evaluation of the high-speed sideway force coefficient and tire–pavement noise levels provide insights into the performance of various pavement textures. To devise an optimal strategy that boosts pavement skid resistance while mitigating tire/road noise, a multi-objective optimization analysis was undertaken. Given the small sample size, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was employed for a comprehensive assessment of each texture design [
48]. This technique, which does not rely on the distribution pattern of the data, is appropriate for the limited empirical data available. The core principle involves determining the performance of each option across multiple criteria, and then identifying the optimal and least optimal solutions. The TOPSIS approach selects the most suitable option by calculating the distance of each alternative from the optimal and least optimal solutions, favoring the choice nearest to the optimal while the farthest is identified as the least optimal.
TOPSIS Calculation Steps: (1) Construct the decision matrix: Assuming there are
evaluation objects and
criteria, form decision matrix
:
where
represents the performance value of the
-th option under the
-th criterion.
(2) Normalize to eliminate the impact of different units and magnitudes, with the normalized
as shown in Equation (3):
(3) Introduce weights
for each criterion, forming the weighted normalized decision matrix:
where
is the weight of the
-th criterion.
(4) Determine the ideal best (
) and worst (
) solutions:
where
is the set of beneficial criteria, and
is the set of non-beneficial criteria.
(5) Calculate the Euclidean distance of each option from
and
:
(6) Compute the relative closeness to the ideal solution for each option,
:
The closer is to 1, the closer the option is to the ideal solution.
5.2. Data Preprocessing
The dataset comprises texture parameters from 8 test sites and the corresponding performance metrics. The texture parameters were categorized into grooving and burlap drag, measured by width, depth, and spacing, as detailed in
Table 1 and
Table 2. The performance metrics included the pavement’s high-speed sideway force coefficient and noise levels at 60 km/h and 80 km/h, as listed in
Table 4 and
Table 6.
To mitigate the influence of diverse scales and magnitudes, all parameters and results were normalized using Z-score normalization, resulting in a dataset with a mean of 0 and a standard deviation of 1, as illustrated in
Table 7.
Given the potential variation in pavement texture’s impact on noise at different speeds, the data for 60 km/h and 80 km/h were analyzed separately to gauge the speed’s influence on the optimization outcomes. The absence of noise data for test sites 7 and 8 at 80 km/h necessitated their exclusion from the dataset for this speed segment.
5.3. Determination of Weights
5.3.1. Top-Level Weight
To elucidate the causal relationship between performance objectives and texture design, top-level weights were assigned to pavement performance indicators (anti-skid and noise reduction capabilities). The parameters for burlap drag and grooving textures were established as secondary weights, with their internal distribution reflecting the influence of various texture parameters on performance metrics.
The objective of this study was to identify an optimal texture design that enhances pavement skid resistance while minimizing tire/pavement noise. Consequently, equal importance was attributed to skid resistance and noise reduction, assigning a weight of 0.5 to each at the top level.
5.3.2. Internal Weight Setting for Grooved and Burlap Drag Textures
The behavior of textures in noise reduction varies with speed, necessitating a distinct discussion on internal weight assignment at 60 km/h and 80 km/h.
At 60 km/h, the grooved textures’ width negatively correlated with the SFC and has a smaller impact on noise, warranting a 15% weight. Groove depth, which was positively correlated with the SFC and significant in reducing low-frequency noise, was allocated a 30% weight, emphasizing its crucial role. Groove spacing received a 55% weight due to its significant noise control contribution, highlighting its pivotal role in noise reduction. The burlap drag textures’ width was assigned a 20% weight for its minor noise control role at low speeds. Groove depth was given a 30% weight for its impact on texture roughness and indirect effects on the SFC and noise, while spacing, crucial for noise control, was given a 50% weight to underline its central function.
At 80 km/h, the weight for groove depth decreased to 20% as its noise impact diminishes at higher speeds. Groove width was assigned a 15% weight, and spacing, still a key noise and SFC influencer, especially for high-frequency noise, was given a 65% weight. For burlap drag textures, the groove depth’s weight was reduced to 25%, width was given a 20% weight, and spacing, maintaining its significance for noise and the SFC, was allocated a 55% weight.
Table 8 presents the weight distribution across the different conditions and speeds.
5.4. Results
Utilizing normalized texture parameters and performance data, the relative closeness index was calculated for each test site, considering anti-skid and noise reduction.
Table 9 displays these indices across the varying speeds.
The results indicate that at 60 km/h, test sites 4 and 5 showed superior overall performance. Test site 4, with a groove width of 3.5 mm, depth of 1 mm, and spacing of 19 mm, offers a balanced configuration that may facilitate adequate water drainage while minimizing noise induced by excessively deep or narrow textures. For the burlap drag texture, test site 4’s larger groove width (3.1 mm) and relatively higher groove height (1.5 mm) may improve the contact pressure distribution, reducing high-frequency vibrations and noise.
At 80 km/h, the texture design of test site 1 align most closely with the ideal solution. Featuring a groove width of 4.0 mm, a depth of 2 mm, and a spacing of 25 mm, this configuration is likely to enhance water drainage and provide adequate friction at high speeds, while the greater spacing assists in mitigating noise generated by air vibration. The burlap drag texture at test site 1, characterized by a groove width of 1.5 mm, a height of 1.0 mm, and a spacing of 12.5 mm, is expected to maintain effective contact stability and contribute to noise reduction at elevated speeds.
In essence, improving pavement skid resistance typically necessitates an increase in surface roughness or texture depth, facilitating enhanced friction between the tires and pavement, thereby boosting vehicle stability and safety. Nevertheless, such modifications can result in heightened noise levels due to the increased surface vibrations and air compression fluctuations associated with rougher textures. Consequently, the design of pavement texture should be tailored to the specific conditions of road use and the requirements of the intended users. For example, in urban or heavy traffic areas, skid resistance may be a priority. Additionally, the design should take into account the diversity of vehicle types, traffic volumes, speed ranges, and load categories, as the needs for skid resistance and noise reduction can vary significantly between heavy-duty vehicles and light passenger cars. Along this vein, the objective of optimal pavement texture design is to strike an ideal balance between skid resistance and noise reduction, tailored to the particular application context.