Spatiotemporal evolution analysis of pavement texture depth on RIOH track using statistical and rescaled range approaches

Abstract

The spatial and temporal variability of road surface texture with the environment is closely related to the management and maintenance of pavement performance. To understand the spatiotemporal evolution, accelerated loading test was carried out during a four-year period on Research Institute of Highway (RIOH) track. Statistical and rescaled range (R/S) analysis were introduced to analyze the texture depth data collected using an automatic detection vehicle. As a result, even though a high linear correlation, there are significant differences statistically between the two indexes mean profile depth (MPD) and sensor measured texture depth (SMTD). Further, the spatial variability of texture depth, generally between 5% ∼ 15%, does not change obviously with service environments. However, the variation range of texture depth with time is as low as 0.2 mm and as high as 0.75 mm. The climate-loading effect increases the variation range of MPD more than the climate effect. Through R/S analysis, the Hurst exponent of the MPD sequence is<0.5, indicating the evolution of texture depth is an anti-persistent alternating process between increase and decrease. In addition, the average cycle period, representing the past duration of events affecting future trend, is generally<8 months. These studies took some new insights into the evolution behaviors of texture depth, providing the groundwork for estimating and setting texture level.