mix it up
Latest Pavement Technologies Can Save Lives The National Highway Traffic Safety Administration (NHTSA) reports that the economic cost of the car accidents claiming more than 90 U.S. lives per day is approximately $230.6 billion, and states most of these crashes can be avoided if preventive measures are taken at the road’s surface. While many factors contribute to vehicular accidents, roadway infrastructure causes can be mitigated with proper pavement surface condition. The Little Book of Tire Pavement Friction uses a study conducted in Texas in 2009 to illustrate fewer car crashes happen along a road that has high friction than a road with lower friction. We also know that pavement distress results in a reduction in surface friction. Therefore, it is essential to monitor the pavement surface condition and improve surface friction if needed, both to save lives and money. According to P.G. Roe and R. Sinhal in their 1988 “The Polished Stone Value of Aggregates and In-service Skidding Resistance,” skid resistance on wet surfaces decreases as speed increases, but surfaces with greater macrotexture have better friction at a higher speed and the same low-speed friction. The Little Book teaches us the difference between peak and sliding friction on dry and wet roads depends on both tire properties and characteristics of road surfaces. Therefore, we must maintain the macrotexture of the roads to ensure an appropriate amount of friction between tires and surface. Degradation of this macrotexture is considered distress. As time goes on, even the best asphalt pavement can get distressed. Readers know there are many types of distress such as cracking, distortion, disintegration, skidding hazards and surface treatment distresses, and one kind of distress can lead to another. Hence, it’s important to invest not only in new infrastructure but also in the technology that detects and treats the distresses to achieve optimal friction between tires and road surfaces.
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DETECT DISTRESS
Detecting and treating different distresses is a complex endeavor. While many countries use manual visual inspection, this method is not the most efficient. Hence, researchers recommend using an automated inspection system. Automated distress detectors reduce the subjectivity of visual measurements, as detailed in Tom B.J. Coenen and Amir Golroo’s “A review on automated pavement distress detection methods” from 2017. Each type of distress is different, requiring unique detection and treatment strategies. For example, if automated technologies are being used, then detecting cracking that has a larger depth will require a different imaging technique and resolutions than when detecting rutting, which is shallow in depth. There are many technological options available to detect these distresses.
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Use of poor technology to detect and treat pavement distresses brings unsatisfactory and sometimes counterproductive results. Researchers around the world are trying to use innovative, economically viable and efficient technology to detect asphalt pavement distress. The pavement management system (PMS) works efficiently when high-quality data is being input, as explained by Laura Inzerillo et.al., in the “Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress” in 2018. Reports like “A Review of Three-Dimensional Imaging Technologies for Pavement Distress Detection and Measurements” from S. Mathavan et al. show studies involving 3D imaging technology to detect and prevent pavement distress have gained attention because of the high accuracy and fast results. Then reports like that from G. Loprencipe et al. in 2017 show the automated distress detection system may be highly accurate and less time-consuming, but it is expensive and not sustainable. The recent development in 3D technology and its effectiveness makes it a worthwhile technology, and researchers are trying to make this technology an economically sustainable option. In 1997, the first 3D technology was used to detect cracks and rutting using a double-sided mirror system that could project a laser on the affected surface and collect reflected light. This system reported a vertical accuracy of 0.5 millimeters, according to a J. Laurent et al., presentation to the International Conference on Recent Advances in 3-D Digital Imaging and Modeling in 1997. A more recent study conducted by R. Gui et al., took this 3D technology a step further. They proposed a 3D pavement components decomposition model (3D-PCDM) that can break down 3D pavement profiles into separate components and extract specific information about pavement distress. This technology showcases an efficiency of 92.75%, according to their abstract at https://doi.org/10.3390/s18072294 This technology follows three main steps. 1. In the first step, the frequency characteristics of pavement distress and performance indicators are analyzed using 3D pavement profiles. 2. During the second step, high pass filter separates low-frequency components (f) from the profile data. 3. In the last step, total variation of de-noising technique separates sparse component (x) and vibration component (t), and a pavement performance indicator is used to verify the validity of these components. The data collection system contains a sensor head and controller. The sensor head collects data using a 3D camera and line laser, where the 3D camera is installed at an angle of 6-8 degrees to the laser so that it can obtain the profile. Whereas the sensor controller receives and pre-processes the profile data and uploads it to the host computer. This technology can provide accurate measurement of length, width and elevation of marked asphalt surfaces. When compared with real measured data, the difference is always between 1 mm, profiles obtained from decomposed sparse components are more stable and comprehensive than the actual measured data, mostly due to the unpredictable impact of pavement texture depth. Potholes may be the most well-known type of pavement distress. For this discussion, we can turn to B. Kang and S. Choi’s presentation to the 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN) in Milan. Believe it or not, classification of pot-
