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

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.

scholarly journals Asphalt Pavement Friction Coefficient Prediction Method Based on Genetic-Algorithm-Improved Neural Network(GAI-NN) Model

2021 ◽  
Author(s):  
Zhaoyun Sun ◽  
Xueli Hao ◽  
Wei Li ◽  
Ju Huyan ◽  
Hongchao Sun
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Friction Coefficient ◽  
Point Cloud ◽  
Spline Interpolation ◽  
Asphalt Pavement ◽  
3D Point Cloud ◽  
Cloud Data ◽  
Pavement Surface ◽  
Pavement Friction

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%.

A Quantitative Theory for the Computation of Tire Friction Under Severe Conditions of Sliding

1986 ◽  
Vol 14 (1) ◽  
pp. 44-72 ◽  
Author(s):  
C. M. Mc C. Ettles
Keyword(s):  
Friction Coefficient ◽  
Flash Temperature ◽  
Contact Conditions ◽  
Quantitative Form ◽  
Heating Effects ◽  
Rubber Friction ◽  
Viscoelastic Effects ◽  
Tire Friction ◽  
Pavement Friction ◽  
Metal Polymer

Abstract It is proposed that tire-pavement friction is controlled by thermal rather than by hysteresis and viscoelastic effects. A numerical model of heating effects in sliding is described in which the friction coefficient emerges as a dependent variable. The overall results of the model can be expressed in a closed form using Blok's flash temperature theory. This allows the factors controlling rubber friction to be recognized directly. The model can be applied in quantitative form to metal-polymer-ice contacts. Several examples of correlation are given. The difficulties of characterizing the contact conditions in tire-pavement friction reduce the model to qualitative form. Each of the governing parameters is examined in detail. The attainment of higher friction by small, discrete particles of aluminum filler is discussed.

Effect of aggregate properties on asphalt pavement friction based on random forest analysis

2021 ◽  
Vol 292 ◽  
pp. 123467
Author(s):  
You Zhan ◽  
Joshua Qiang Li ◽  
Cheng Liu ◽  
Kelvin C.P. Wang ◽  
Dominique M. Pittenger ◽  
...  
Keyword(s):  
Random Forest ◽  
Asphalt Pavement ◽  
Random Forest Analysis ◽  
Aggregate Properties ◽  
Pavement Friction

scholarly journals Effect of a dual tire pressure on the design parameters of thick asphalt pavements using finite element freeware

DYNA ◽  
2016 ◽  
Vol 83 (196) ◽  
pp. 194-203
Author(s):  
Myriam Rocío Pallares Muñoz ◽  
Julián Andrés Pulecio-Díaz
Keyword(s):  
Finite Element ◽  
Analytical Methods ◽  
Asphalt Pavement ◽  
Field Tests ◽  
Asphalt Pavements ◽  
Design Parameters ◽  
Future Research ◽  
Tire Pressure ◽  
The Cost ◽  
Cost Of Construction

<p>The effect of a dual tire pressure on the design parameters of thick asphalt pavements using finite element freeware EverStressFE©1.0 is evaluated. This is trying to represent more adjusted the footprint shape and intensity of stress generated by the tires of vehicles. To validate the elastic multilayer EverStress©5.0 software was used. The results of the deformations can be concluded that the asphalt pavement designs made with analytical methods may be slightly oversized and consequently increase the cost of construction of pavements. This study marks a route to analyze the sensitivity of various factors that may affect the design of asphalt pavements. Future research is expected to integrate dynamic conditions by introducing results of field tests to full scale.</p>

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

scholarly journals Analysis of Nonlinear Dynamic Characteristics of a Mechanical-Electromagnetic Vibration System with Rubbing

2019 ◽  
Vol 9 (21) ◽  
pp. 4612
Author(s):  
Yiming Li ◽  
Zhilong Huang ◽  
He Li ◽  
Guiqiu Song
Keyword(s):  
Friction Coefficient ◽  
Dynamic Characteristics ◽  
Chaotic Motion ◽  
Nonlinear Dynamic ◽  
Response Amplitude ◽  
Jump Discontinuity ◽  
Excitation Current ◽  
Radial Stiffness ◽  
Chaotic Region ◽  
Coupling System

In this study, a rotor-bearing-runner system (RBRS) considering multiple nonlinear factors is established, and the complex nonlinear dynamic behavior of the coupling system is studied. The effects of excitation current, radial stiffness, and friction coefficient on dynamic characteristics are analyzed by numerical simulation. The research results show that the dynamic properties of the coupling system caused by different nonlinear factors are interactional. With the changes of different parameters, the RBRS presents multiple motion states, including periodic-n, quasi-periodic, and chaotic motion. The increase of the excitation current Ij has a certain inhibitory effect on the response amplitude of the system and makes the motion state of the system more complex, the chaotic motion wider, and the jump discontinuity enhanced. With the increase of radial stiffness kr, the motion complexity of the coupling system increases, the chaotic region increases, the response amplitude increases, and the vibration intensity increases. With the increase of the friction coefficient μ, the chaotic region increases first and decreases, the different motions alternate frequently, and the response amplitude gradually increases. This study can not only help to understand the dynamic characteristics of RBRS, but also help the stable operation of the generator set.

scholarly journals A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xuancang Wang ◽  
Jing Zhao ◽  
Qiqi Li ◽  
Naren Fang ◽  
Peicheng Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Performance Prediction ◽  
Asphalt Pavement ◽  
Pavement Performance ◽  
Support Vector ◽  
Grey Relation Analysis ◽  
Relation Analysis ◽  
Pavement Performance Prediction ◽  
Grey Relation

Pavement performance prediction is a crucial issue in big data maintenance. This paper develops a hybrid grey relation analysis (GRA) and support vector machine regression (SVR) technique to predict pavement performance. The prediction model can solve the shortcomings of the traditional model including a single consideration factor, a short prediction period, and easy overfitting. GAR is employed in selecting the main factors affecting the performance of asphalt pavement. The SVR is performed to predict the performance. Finally, the data collected from the weather station installed on Guangyun Expressway were adopted to verify the validity of the GRA-SVR model. Meanwhile, the contrast with the grey model (GM (1, 1)), genetic algorithm optimization BP[[parms resize(1),pos(50,50),size(200,200),bgcol(156)]]081%, −0.823%, 1.270%, and −4.569%, respectively. The study concluded that the nonlinear and multivariate prediction model established by GRA-SVR has higher precision and operability, which can be used in long-period pavement performance prediction.

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