Investigation on Acoustic Versus Functional Characteristics of Porous Asphalt

The theoretical background, standards, and contract requirements of pavement friction courses involve functional (e.g., permeability) and acoustic (e.g., resistivity) characteristics. Unfortunately, their relationship is partly unknown and uncertain. This affects the comprehensiveness and soundness of the mix design of asphalt pavements. Based on the issues above, the goals of this study were confined into the following ones: 1) to investigate the relationship between acoustic and functional properties of porous asphalts; 2) to investigate, through one-layer (1L) and two-layer (2L) models, the effectiveness of the estimates of acoustic input data through mixture volumetric- and permeability-related characteristics. Volumetric and acoustic tests were performed and simulations were carried out. Equations and strategies to support a comprehensive approach were derived. Results demonstrate that even if the measured resistivity is very important, permeability-based estimates of resistivity well explain acoustic spectra. Furthermore, the distance between observed and estimated peaks of the absorption spectrum emerges as the best error function.

Exploring Implicit Relationships between Pavement Surface Friction and Vehicle Crash Severity Using Interpretable Extreme Gradient Boosting Method

Pavement friction has been identified as crucial in traffic safety. Since the Highway Safety Manual prediction algorithm is often based on crash frequency, the crash severity distribution might be assumed unchanged before and after the countermeasure. However, pavement surface treatments can improve the friction to different levels, by which crash severity outcomes may vary greatly. To explore the implicit effects of pavement friction on vehicle crash severity, this paper first validates the extreme gradient boosting model performance and then the Shapley additive explanations interaction values are employed to interpret individual features and the nonlinear interactions among predictors. Under various scenarios, the XGBoost output probability is utilized to convert into dynamic crash severity distributions. Results also indicate that friction becomes more significant when the friction number is less than 38, and immediate corrective actions are needed when the friction number is below 20.

Pavement Friction Prediction Using 3D Texture Parameters

This article deals with the possibility of predicting skid resistance based on non-contact scanning of the road surface. The study is based on comparing pavement texture parameters with coefficients of friction measured on a wide variety of road surfaces, while other test conditions were the same and constant. The measurements of the coefficient of friction were performed using a pendulum tester. The pavement texture was measured using a static road scanner, and 85 different 3D texture parameters were calculated. The study shows that the determination of the friction using only single texture parameters is not sufficient. Based on this statement, the next part of the research analyzed the influence of the mutual combination of surface texture parameters. A linear regression model was chosen to determine the friction coefficient prediction formula based on the combination of texture parameters. Statistically, the most significant parameters in the prediction model proved to be the valley material portion, characterizing the microtexture, and the arithmetic mean curvature, characterizing the pavement macrotexture. The obtained regression model proved to be statistically significant with R2 = 0.81 for Pendulum Test Value prediction.