Deterioration trends of asphalt pavement friction and roughness from medium-term surveys on major Italian roads

To overcome the limitations of pavement skid resistance prediction using the friction coefficient, a Genetic-Algorithm-Improved Neural Network (GAI-NN) was developed in this study. First, three-dimensional (3D) point-cloud data of an asphalt pavement surface were obtained using a smart sensor (Gocator 3110). The friction coefficient of the pavement was then obtained using a pendulum friction tester. The 3D point-cloud dataset was then analyzed to recover missing data and perform denoising. In particular, these data were filled using cubic-spline interpolation. Parameters for texture characterization were defined, and methods for computing the parameters were developed. Finally, the GAI-NN model was developed via modification of the weights and thresholds. The test results indicated that using pavement surface texture 3D data, the GAI-NN was capable of predicting the pavement friction coefficient with sufficient accuracy, with an error of 12.1%.

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Multiscale power spectrum analysis of 3D surface texture for prediction of asphalt pavement friction

Construction and Building Materials ◽

10.1016/j.conbuildmat.2021.123506 ◽

2021 ◽

Vol 293 ◽

pp. 123506

Author(s):

Qiangsheng Deng ◽

You Zhan ◽

Cheng Liu ◽

Yanjun Qiu ◽

Allen Zhang

Keyword(s):

Power Spectrum ◽

Spectrum Analysis ◽

Surface Texture ◽

Asphalt Pavement ◽

Power Spectrum Analysis ◽

3D Surface ◽

3D Surface Texture ◽

Pavement Friction

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Using Shotblasting Treatment to Improve Asphalt Pavement Friction at the NCAT Test Track

Airfield and Highway Pavements 2021 ◽

10.1061/9780784483510.030 ◽

2021 ◽

Author(s):

Md. Tanvir Ahmed Sarkar ◽

Fan Gu ◽

Michael Heitzman ◽

Buzz Powell

Keyword(s):

Asphalt Pavement ◽

Test Track ◽

Pavement Friction

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Research on the Prediction Model of the Friction Coefficient of Asphalt Pavement Based on Tire-Pavement Coupling

Advances in Materials Science and Engineering ◽

10.1155/2021/6650525 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Miao Yu ◽

Xinquan Xu ◽

Chuanhai Wu ◽

Shanqiang Li ◽

Mingxia Li ◽

...

Keyword(s):

Friction Coefficient ◽

Prediction Model ◽

Dynamic Testing ◽

Asphalt Pavement ◽

Influence Factors ◽

Testing System ◽

Tire Pressure ◽

Coupling System ◽

Static Contact ◽

Pavement Friction

The correlations between pavement texture and tire pressure with the actual tire-road contact area were first investigated according to the tire-road static contact characteristics; on this basis, the influence mechanisms of speed and pavement texture on the pavement friction coefficient were systematically explored from the angle of tire-road coupling system dynamics via the self-developed dynamic testing system of tire-pavement friction. By integrating the above influence factors, the BP neural network method was applied to the regression of the prediction model for the asphalt pavement friction coefficient. Through the comparison between the model measured value and estimated value, their correlation coefficient R2 reached 0.73, indicating that this model is of satisfactory prediction accuracy and applicable to the antiskid design of asphalt pavement.

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