Convolutional Neural Network–Based Friction Model Using Pavement Texture Data

Yang, Guangwei; Li, Joshua; Zhan, You; Fei, Yue; Zhang, Aonan

doi:10.1061/(asce)cp.1943-5487.0000797

Cited by 48 publications

(20 citation statements)

References 27 publications

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“…The Lugre friction model is a typical friction model for servo systems and can accurately describe the frictional characteristics of the system during motion [ 7 ]. As the parameter identification of the Lugre friction model involves both static and dynamic parameter identification [ 8 ], it is a harmonious combination of static and kinematic characteristics, and the improved genetic algorithm can effectively improve the accuracy of identification by taking into account both static characteristics and dynamic factors and prevent the problem of falling into local optimality instead of global optimality in the process of identification [ 9 – 12 ].…”

Section: Introductionmentioning

confidence: 99%

The Use of Deep Learning Model for Effect Analysis of Conventional Friction Power Confinement

Liu

Wang

2022

Computational and Mathematical Methods in Medicine

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Nonlinear friction could affect the high-precision motion system, resulting in poor tracking accuracy in the end. This is due to the fact that the Lugre friction model’s parameter identification process comprises both static and dynamic parameter identification. The convolutional neural network (CNN) model is used in this study to create the friction identification system. We suggest a hybrid methodology that combines the CNN method and the classic least-squares technique. The convolutional layer (CONV), which is defined by a convolutional kernel, analyzes and extracts features from an input image. In terms of accuracy and convergence, the results reveal that the upgraded CNN friction model outperforms the original CNN friction model. You may successfully reduce the influence of friction on your system while improving its performance by applying the feedforward correction.

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Section: Introductionmentioning

confidence: 99%

The Use of Deep Learning Model for Effect Analysis of Conventional Friction Power Confinement

Liu

Wang

2022

Computational and Mathematical Methods in Medicine

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“…The model developed by the authors showed a precision of 0.98 hence, being a potential tool to be used in infrastructure projects. Yang et al (2018) built a prediction model using ANN for the coef icient of friction based on data about the pavement texture, and as a consequence, better understand the relation between these two parameters. Yao et al (2019), in their turn, elaborated models to predict the deterioration of the pavement conditions, among which the coef icient of friction is highlighted and the Coef icient of Determination (R 2 ) was of 86.1% during the testing phase.…”

Section: Ann Applica On In the Management Of Pavementsmentioning

confidence: 99%

A prediction model of the coefficient of friction for runway using artificial neural network

2021

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As condições superficiais de uma pista de pouso e decolagem (PPD) são fundamentais para a garantia da segurança das operações das aeronaves que a utilizam. Nesse sentido, operadores de aeródromos devem manter atenção especial ao coeficiente de atrito e à macrotextura, para que possam promover uma PPD segura, planejar estratégias de manutenção e reabilitação em momentos oportunos, à medida que esses parâmetros se deterioram. Dessa forma, com o intuito de auxiliar operadores de aeródromo e a agência reguladora na tomada de decisão acerca do monitoramento e dos serviços de conservação de pavimentos aeroportuários, este trabalho tem o objetivo de desenvolver um modelo de previsão do coeficiente de atrito medido numa PPD, por meio de Redes Neurais Artificiais. Os resultados apresentaram-se satisfatórios e, assim, tem-se potencial de aplicação do modelo para contribuir na tomada de decisão no contexto de um Sistema de Gerência de Pavimentos Aeroportuários.

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“…Then the multiple quadratic polynomial model between DF 60 and M 2 is established. The regression analysis results are shown in Tables 6-8, and the multiple quadratic multinomial regression equation and the parameters A 2 , B 2 and C 2 are shown in Equation (13). In Table 6, R 2 is as high as 0.9255 and root MES is only 0.0251.…”

Section: The Model Analysis Based On 3d Texture Datamentioning

confidence: 99%

“…The comparison between Equations (12) and (13) indicates that the independent variable Y appears in DF 60 model but not in BPN model. The results show that compared with DF 60 , the requirements of mineral aggregate classification for BPN model is relative loose.…”

Section: The Model Analysis Based On 3d Texture Datamentioning

confidence: 99%

“…And the correlation between texture and skid resistance was further established by convolutional neural network and stepwise regression. The results show that there is a strong link between texture and skid resistance [13,14]. Rezaei et al studied the decay model of skid resistance and found that the aggregate characteristics and gradation also has a significant impact on skid resistance [15].…”

Section: Introductionmentioning

confidence: 98%

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Evaluation of Pavement Skid Resistance Using Surface Three-Dimensional Texture Data

et al. 2020

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Pavement skid resistance is an important guarantee for driving safety. However, it is very difficult to determine the exact friction in a field environment. In order to overcome the limitations of traditional evaluation methods, the effect mechanism of surface 3D (three-dimensional) texture on skid resistance was firstly analyzed. Then the surface 3D texture of pavement was acquired through an improved binocular reconstruction method. Additionally, the relationship between friction coefficient and 3D texture was also analyzed. Subsequently, under the concept of IFI (international friction index) used to harmonize different detection methods of skid resistance, the evaluation model of skid resistance based 3D texture was further established. The results showed that the multiple quadratic multinomial regression model can well describe the relationship between skid resistance and texture indicators. The establishment of an improved evaluation model is simple to operate and implement. It can directly evaluate the skid resistance on pavement surface once the aggregates' type and 3D texture are known. This evaluation model not only overcomes the challenges of friction coefficient with a strong conditional restriction, but also provides a harmonious approach for different detection methods in the evaluation of pavement skid resistance.

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Convolutional Neural Network–Based Friction Model Using Pavement Texture Data

Cited by 48 publications

References 27 publications

The Use of Deep Learning Model for Effect Analysis of Conventional Friction Power Confinement

The Use of Deep Learning Model for Effect Analysis of Conventional Friction Power Confinement

A prediction model of the coefficient of friction for runway using artificial neural network

Evaluation of Pavement Skid Resistance Using Surface Three-Dimensional Texture Data

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