1. Introduction
Previous studies [
1,
2,
3] have shown that pavements generally have good anti-skidding performance at the early stage after construction, yet with the increase of service time, the skid resistance performance demonstrates varying degrees of decline, even to the extent of failing to meet the requirements of safe driving. Surface texture is a vital factor affecting pavement skid resistance [
4,
5]. To be more specific, it is generally believed that the anti-skidding performance of vehicles at low speeds is mainly determined by pavement micro-texture, whereas its counterpart at high speeds tends to be significantly influenced by pavement macro-texture [
6,
7,
8]. Therefore, pavement surface texture could be used to effectively represent pavement anti-skidding performance [
9]. In addition, the anti-skidding performance is mainly derived from energy dissipation produced by the cutting effect of surface fluctuations on the tires, which could be expressed by pavement surface macro-texture [
10]. Field tests show a rapid decrease of the pavement skid resistance performance within the first two years after construction, before the decline grows stable. It should also be noted that there exists a minor increase before the rapid decrease due to asphalt wear, which can be difficult to observe. An effective relationship model between the pavement macro-texture and skid resistance performance cannot be well established due to many influencing factors of the anti-skidding performance, long service time, and lack of a suitable simulation method of driving conditions, etc. Therefore, it is necessary to carry out a long-term tracking of actual road sections to observe anti-skidding performance and surface macro-texture. In particular, high-precision test methods should be employed to accurately characterize the pavement macro-texture and to establish the relationship model between the pavement macro-texture and anti-skidding performance by various reasonable means. Then, a more accurate pavement anti-skidding performance model based on surface texture could be built, which could provide highway authorities with useful decision-making information on appropriate maintenance time with the help of high-precision texture measurement devices.
In recent years, scholars around the globe have investigated the attenuation law of pavement anti-skidding performance. In terms of laboratory simulation by accelerated loading devices, some scholars initially used the Wheel Tracking Tester to simulate the effect of vehicle loads on pavement. However, the obtained wheel track size appears to be too small to meet test requirements of skid resistance. Presently, most laboratory simulation devices are accelerated loading equipment or self-developed accelerated abrasion machines, which boast the advantages of higher efficiency and the ability to impose loads that are more consistent with actual loads [
11,
12,
13], whereas skid resistance attenuation in the laboratory is not consistent with that on road sections, which is far from the needs of pavement maintenance. With regards to anti-skidding attenuation, some have proposed prediction models, such as the asymptotic model, exponential model, logarithmic model, and Penn State model, and verified their effectiveness by testing the Dynamic Friction Coefficient (DFC), British Pendulum Number (BPN), or texture depth [
14,
15]. In this case, the measured skid resistance may differ from that predicted by the models when the environmental change or maintenance of the actual pavement appears. It is more convenient and flexible in practical use to establish a prediction model from the surface texture that can be measured when necessary.
In terms of the relationship between the macro-texture and skid resistance performance, studies have demonstrated that the correlation between the anti-skidding performance and traditional macro-texture evaluation indexes, such as Mean Profile Depth (MPD) and Mean Texture Depth (MTD), remain to be discussed. However, in recent years, great progress has been made in the following aspects, namely, the data acquisition of the macro- and micro-texture of pavement surface by high-precision and non-contact measurement methods, as well as their application in the investigation of the surface texture evolution and pavement anti-skidding performance prediction [
16,
17,
18,
19,
20]. This contributes to the fast acquisition and precise characterization of surface texture. Much research focuses on the surface evaluation from aspects of elevation data [
9,
10], and more attention should also be paid to the texture wavelength.
When establishing the relationship model between pavement surface texture and skid resistance, various methods can be used. Correlation analysis has always been employed in previous research [
21,
22], whereas only the correlation degree can be obtained in this way and quantitative prediction cannot be achieved. Research has also been conducted using the multiple linear regression (MLR) method to develop a model between anti-skidding performance and pavement surface texture [
23,
24]. In fact, it is still unknown whether the relationship between them is linear and nonlinear information may be lost when MLR is used. In recent years, nonlinear methods have been employed to explore the relationship model between pavement surface texture and skid resistance. The back propagation (BP) neural network has been demonstrated to exhibit excellent ability in predicting nonlinear and complicated models, so it has been utilized in many aspects in pavement engineering, such as pavement design and performance prediction [
8,
25,
26]. Hence, the BP neural network can be attempted to establish a relationship model between pavement surface texture and skid resistance.
In summary, the current laboratory simulation of vehicle loads on pavement surface mainly depends on the Wheel Tracking Tester, accelerated loading facilities, Wehner/Schulz polishing, self-developed equipment, etc. Yet, it has to be noted that laboratory simulation by accelerated abrasion/polish devices could only simulate the number of axle load actions by equivalent conversion. However, environmental factors, such as temperature, humidity, rain, and snow, cannot be completely recreated, making it difficult to establish a prediction model that fits the actual roads. Nowadays, the commonly used relationship model between skid resistance and macro-texture mainly relies on the traditional texture depth evaluation indexes, MTD and MPD, and other statistical indexes, and further research is still pending on in-depth characterization of pavement surface texture, especially from the view of wavelength, and the model establishment of its relationship with pavement anti-skidding performance.
This research aims to evaluate the surface macro-texture from aspects of both elevation data and wavelength based on 3D laser scanning technology, and to establish the relationship model between surface texture and skid resistance measured on real road sections and in the laboratory using MLR and BP neural network methods, and, finally, to recommend a suitable relationship model. Based on this, a more accurate pavement anti-skidding performance prediction model based on surface texture can be built, which could provide highway authorities and engineers with useful decision-making information on appropriate maintenance time by measuring surface texture when necessary.
In order to achieve the objective of this research, firstly, six road sections with different grades and surface types in Beijing were selected to conduct performance tracking for one and a half years. Their skid resistance was tested by DFT at three different service times, and core sampling was conducted on site after each DFT test. Secondly, in order to compare the relationship model between macro-texture and anti-skidding performance measured on site and in the laboratory, the Yangyan Road section was selected to carry out the laboratory accelerated loading test by the one-third-scale Model Mobile Load Simulator (MMLS3). Thirdly, 3D laser texture scanning technology was utilized to obtain surface information of the drilled core samples and laboratory specimens, and to rebuild the digital model, which was employed to calculate the macro-texture indexes from different aspects by importing into MATLAB software. Finally, the correlation degree of each macro-texture index and skid resistance performance was calculated, based on which, the relationship model between macro-texture and skid resistance at both low speeds and high speeds was established with two different methods, namely, multiple linear regression (MLR) and back propagation (BP) neural networks. The feasibility of evaluating pavement skid resistance performance from the aspect of macro-texture were explored. The flowchart of this research is shown in
Figure 1.
3. Pavement Macro-Texture Indexes
In order to characterize the macro-texture characteristics of the pavement surface accurately, the distribution of macro-texture was described from aspects of the elevation, wavelength, and geometry. The vertical and horizonal macro-texture refers to the vertical and horizontal irregularities, respectively, and pertains to 0.5–5 mm wavelengths. Evaluation indexes calculated based on surface elevation data in the vertical direction to describe vertical irregularities, as shown in
Figure 5, were named vertical macro-texture indexes. The evaluation indexes calculated based on wavelength data in the horizontal direction to describe horizontal irregularities, as shown in
Figure 5, were named horizontal macro-texture indexes [
34]. Vertical and horizontal irregularities and the geometric characteristics of the pavement surface were characterized. The schematic diagram of the vertical and horizontal macro-texture distribution is shown in
Figure 5.
The vertical macro-texture indexes mainly describe the texture distribution of pavement surface along the z axis, i.e., in the amplitude direction, as shown in
Figure 5. The indexes utilized in this research include arithmetic mean value (R
a), standard deviation (R
q), range (R
z), skewness (R
sk), and kurtosis (R
ku), which could describe the difference degree of texture depth, skew degree, kurtosis degree, and even the texture orientation [
35] of pavement surface, and reflect the changes of pavement vertical macro-texture under different conditions. They were calculated according to the equations reported in similar research [
36].
As demonstrated in
Figure 5, the horizontal macro-texture indexes were used to describe the horizontal distribution and distribution deviation, i.e., in the driving direction, while reflecting the changes of the horizontal macro-texture of actual roads under different conditions. The horizontal macro-texture indexes used include average wavelength, L
a, and root mean square of wavelength, L
q, of the profile. They were calculated according to a published a master dissertation [
34].
Geometric features refer to the shape characteristics of a certain object. In this research, geometric indexes, including the average slope, S
1, and the average curvature, C, were used to describe the contour shape of the outside edge of pavement surface profiles. They were calculated according to previous research [
36]. All the indexes involved are summarized in
Table 3.
5. Conclusions
In this research, the skid resistance of six roads was tested by the DFT three times, corresponding to three different service times. Moreover, core sampling was conducted to obtain the surface texture by 3D laser scanning. In the meantime, laboratory accelerated loading tests were conducted. BPN and surface texture were tested on specimens after different passes of loading. Based on the data, the relationship models between macro-texture and skid resistance at both high and low speeds were established using the MLR method and BP neural network method. In view of the test results and analysis, the following conclusions could be drawn.
The correlation between the vertical macro-texture of pavement surface and the skid resistance is higher than that of the horizontal macro-texture and geometric characteristics, indicating that pavement vertical macro-texture holds considerable influence over the pavement skid resistance.
The MLR model has low correlation and appears insufficient to fully characterize the relationship between macro-texture and pavement skid resistance. When the BP neural network is employed to establish the relationship model between macro-texture and skid resistance at high speeds, the correlation reaches 70.6%. Thus, it is recommended to use the BP neural network for the establishment of a relationship model between macro-texture and skid resistance.
The accuracy of the relationship model between macro-texture and skid resistance at low speeds characterized by F20 and BPN using the MLR and BP neural network is lower than that of the relationship model with F60 as the dependent variable, indicating that the correlation between the macro-texture and skid resistance at low speeds is low, and the macro-texture of pavement surface mainly affects pavement skid resistance at high speeds. The establishment of the relationship model between surface texture and the skid resistance in the full speed range requires a comprehensive consideration of both macro-texture and micro-texture.