Monitoring Pavement Surface Macrotexture and Friction

Abstract

The amount of available friction and texture depth are primary concerns in the evaluation of pavement surface safety characteristics. The two components of microtexture and macrotexture provide pavement surface friction. Macrotexture properties are collected with laser-based devices at highway speeds, but a proper evaluation of microtexture is available only with stationary devices or laboratory experimental methods. The industry is trying to develop practical methods for monitoring microtexture at the network level. Pertinent quantification for both friction components is valued because the common locked-wheel testing of skid resistance depends on surface temperature, presence of dirt and oil on the road surface, and vehicle speed. This case study examined the relationship between laser-based mean profile depth (MPD) and the friction number (FN) collected with a locked-wheel skid trailer on Louisiana highways. The analysis showed that quantified microtexture data were required in conjunction with the macrotexture to establish a significant correlation with friction measurements. However, the correlation between MPD and FN appeared to be significantly stronger at higher speeds of friction testing. Lower and higher threshold values were determined from the first and third quartiles of MPD and FN data. At speeds of about 50 mph, 71% of the pavement sections with MPD of less than the lower texture threshold also had FN values of less than the lower friction threshold. This lower MPD threshold of 0.036 in. can be implemented in network-level monitoring operations to determine areas of concern that require friction testing.

Skid Resistance Performance Assessment by a PLS Regression-Based Predictive Model with Non-Standard Texture Parameters

“…These models were developed by using traditional texture indicator MTD and/or MPD for texture-friction relationship establishment. The obtained R 2 values vary from no significant correlation for simple models [26][27][28] to moderate or even strong correlation for models [21][22][23][24][25]. By observing the obtained R 2 values for complex models, it can be seen that they generally showed more consistency in friction prediction in comparison to simple models with only one or two predictors.…”

“…Simple models [21][22][23][24][25][26][27] were derived from extensive pavement texture and friction measurements, performed by traditional measurement procedures defined in relevant European standards for texture measurements [5,8] and friction measurements [10,11]. Complex models [28][29][30][31][32] were derived in cases where influential parameters other than texture, such as friction measurement speed, environmental effects, and different dataset sizes, were considered in the model establishment.…”

“…The predictive strength of empirical models from previous research, expressed as Pearson's coefficient of determination R 2 , is given in Figure 1. An overview of empirical friction prediction models established by traditional texture and friction indicators; blue color marks simple predictive models accounting for one or two influencing parameters and orange color marks the complex models accounting for more than two influencing parameters [21][22][23][24][25][26][27][28][29][30][31][32][33]. Different versions of the models developed by same authors are marked with an asterisk and described in parentheses.…”

Establishment and Analysis of the Relationship Model between Macro-Texture and Skid Resistance Performance of Asphalt Pavement

“…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 macroand 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.…”