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Engineering, Civil
Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and for the development of management strategies employed by state highway agencies.
- pavement-friction rating
- network level
- road-safety attributes
- hybrid clustering
1. Introduction
Pavement friction plays a critical role in ensuring road safety by preventing vehicle tires from sliding or skidding on the road surface. Its primary purpose is to provide adequate traction, especially in wet conditions, between the tires and the pavement [1]. By facilitating traction, pavement friction helps drivers maintain control over their vehicles, significantly reducing the risk of accidents caused by skidding or hydroplaning. This is particularly important at critical locations such as curves, intersections, tunnel entrances, and downhill gradients. In addition, emergency vehicles, buses, heavy trucks, and motorcycles rely heavily on sufficient pavement friction for safe maneuvering. Various factors influence pavement friction, including the texture of the pavement surface, the type of surface material, the properties of the tires, the speed of the vehicle, and the prevailing weather conditions [2,3]. To ensure an acceptable level of pavement friction, regular maintenance and friction treatments are essential. It is also necessary to monitor the performance and conditions of friction, employing measures such as periodic assessments and friction testing [4,5,6]. These proactive measures contribute to maintaining sufficient pavement friction, thereby enhancing overall road safety.
Pavement-friction performance ratings play a crucial role in assessing the level of friction and the resulting safety provided by a road surface, especially during challenging weather conditions. These ratings are valuable tools for highway agencies as they assist in identifying areas in need of maintenance and repair. By incorporating these findings into pavement preservation, resurfacing, and overlay programs, optimal performance of the road surface can be ensured. In addition, many countries and roadway or airport authorities have established regulations and standards for pavement-friction performance that must be adhered to in order to ensure safety and prevent accidents [7,8,9,10]. Utilizing pavement-friction performance ratings aids in meeting these regulations and standards, guaranteeing compliance and enhancing overall safety.
2. Threshold-Based Rating Methods
Pavement-performance ratings encompass the evaluation of various factors, including surface distresses, ride quality, structural integrity, and friction, to determine the overall performance or serviceability of the pavement. This assessment aids in prioritizing maintenance and rehabilitation efforts. Presently, several methods are utilized to rate pavement performance, including visual inspection, automated data collection, and non-destructive testing (NDT) [18,19]. Different agencies and organizations may employ variations of these methods or develop customized approaches tailored to their specific requirements and available resources.
Pavement friction, a result of tire-pavement interaction, primarily varies with vehicle speed, tire characteristics, pavement surface texture, and the presence of water. Various methods can be employed to rate pavement friction, depending on the user’s specific needs. Friction-threshold rating methods typically involve measuring the friction coefficient, texture, or a combination of both. Friction-coefficient measurements are commonly made using devices such as the locked wheel skid tester (LWST) [20], the British pendulum tester (BPT) [21], or the dynamic friction tester (DFT) [22]. Texture measurements are typically the mean profile depth (MPD) of macrotexture [23] obtained using the sand patch test [24] or noncontact techniques such as the circular track meter (CTM) [25]. The International Friction Index (IFI) combines the friction coefficient and mean profile depth to provide a comprehensive rating of pavement friction, enabling comparisons between different pavements [26].
In the United States, state highway agencies commonly rely on the LWST to obtain friction measurements [2,27]. The threshold-based methods aim to identify friction threshold values to mitigate vehicle crashes on wet pavement. This simplifies the rating process by conducting field tests to measure friction and comparing the results against the threshold value. If the measured friction falls below the threshold value, appropriate actions may be required to restore adequate friction levels. Table 1 provides an overview of the friction threshold values recommended by different researchers. The table illustrates significant variations in the threshold values between researchers and highway agencies. These variations can be attributed to two primary factors. Firstly, some agencies utilize standard rib tires [28], while others use standard smooth tires [29]. Friction measurements with rib tires are considerably higher than those obtained with smooth tires. Secondly, researchers employ diverse datasets and consider various factors, resulting in substantial discrepancies in the recommended threshold values.
Table 1. Friction threshold values for remedial actions.
The above rating method and the like offer two advantages. Firstly, they provide a straightforward and measurable evaluation of pavement friction, based on predetermined engineering thresholds. Secondly, the threshold values establish a standardized criterion for assessing and comparing pavement friction, which ensures consistency across different sections of pavement and is essential for crash prevention, especially in adverse weather conditions. However, the arbitrary threshold values lack a robust scientific foundation and exhibit inconsistencies among different highway agencies. By adopting an arbitrary friction threshold, essential contextual factors such as vehicle speed, traffic volume, road geometry, weather conditions, and related costs may not be accurately evaluated. Moreover, pavement friction is a dynamic property that constantly changes due to weather, traffic, and various other factors. An arbitrary threshold may fail to account for these variations or provide a mechanism to adjust the threshold based on evolving conditions. Consequently, threshold-based rating methods fall short in delivering adequate warning or transition time to implement preventive measures, leading to missed opportunities for timely maintenance.
3. Multilevel-Based Rating Methods
Recently, a novel trend has surfaced in the evaluation of pavement friction, which involves the utilization of supervised learning techniques to assess pavement friction across multiple levels. Noteworthy contributions in this domain include the research endeavors of Zhan et al. [34] and Zhao et al. [35]. The former introduced an innovative approach employing a deep residual network (ResNets) to predict pavement friction using surface texture. On the other hand, the latter demonstrated the application of extreme gradient boosting (XGBoost) to establish a correlation between friction and safety. Given the subject matter of this paper, this section primarily provides a brief introduction to the work of Zhao et al. In their research work, the XGBoost model was utilized to classify crash severity, identify the contributing factors through the model outputs, and quantify the relationships between friction and crash severity. Their work yielded five pavement friction classes based on the friction numbers (FNs): FNS < 20, FNS ∈ (20, 25), FNS ∈ (25, 38), FNS ∈ (38, 70), and FNS > 70.
Evidently, the above multilevel classification method can offer a more comprehensive, informed, and systematic approach to assessing and managing pavement friction and aid in decision-making, planning, budgeting, and performance monitoring. Nevertheless, the classifications can often become problematic. An example is that within FNS ∈ (20, 25), a significant number of observations exhibit a lower probability of fatal or injury crashes. This is likely because there are several crucial safety attributes, such as the vehicle speed at the time of crashing and the pavement friction at the crash location that cannot be accessible or accurately determined. Although the analyzed datasets included friction, vehicle, and crash attributes, no class labels or target values were assigned to them. Employing supervised learning techniques such as the XGBoost model to ascertain the collective impact generated by these attributes is exceedingly challenging.
This entry is adapted from the peer-reviewed paper 10.3390/lubricants11070275
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