Development of Acoustical Prediction Model for Asphalt Pavements Using Grey System Approach

The rapid detection of asphalt content in asphalt mixture is of great significance to the quality evaluation of asphalt pavement. Based on the dielectric properties of an asphalt mixture, the prediction model of asphalt content is deduced theoretically using three types of dielectric models: Lichtenecker-Rother (L-R) model, Rayleigh model, and Bottcher equation. Under the condition of laboratory mixing at room temperature (about 20–25°C), a dielectric test of asphalt mixture is conducted to verify the applicability of the model. The test results indicate that the dielectric constant of the asphalt mixture is inversely proportional to the asphalt content and directly proportional to the aggregate size of the mixture. Among the models, the Rayleigh model has a wide range of applications and exhibits a high accuracy, with an average relative error of only 1.86%. The results provide a theoretical basis for the nondestructive testing of asphalt pavements using ground-penetrating radar.

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Wind Speed Prediction Based on Improved Grey Neural Network Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.548-549.1235 ◽

2014 ◽

Vol 548-549 ◽

pp. 1235-1240

Author(s):

Bin Zeng ◽

Jian Xiao Zou ◽

Kai Li ◽

Xiao Shuai Xin

Keyword(s):

Neural Network ◽

Wind Speed ◽

Prediction Model ◽

Data Storage ◽

Wind Farm ◽

Grey System Theory ◽

Grey System ◽

Wind Speed Prediction ◽

Neural Network Prediction ◽

Speed Prediction

Wind speed forecasting is an effective method to improve power stability of wind farm. Grey system theory have certain advantages in the study of poor information and uncertainty problems, it is suitable for the system with limited computing power and data storage capacity, such as wind turbine control system. In order to further improve the prediction accuracy of grey model, we combined GM (1, 1) model and BP neural network prediction model in this paper, and improved the combined model by background value optimizing and introducing genetic algorithm. Through analyzing the simulation results and comparing the forecasting results with the actual wind speed, it is clear that the improved combined prediction model is superior to pure grey forecasting model and it meets the needs of the wind power control.

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Application of a Fractional Grey Prediction Model Based on a Filtering Algorithm in Image Processing

Mathematical Problems in Engineering ◽

10.1155/2020/4170804 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18

Author(s):

Mingyu Tong ◽

Kailiang Shao ◽

Xilin Luo ◽

Huiming Duan

Keyword(s):

Image Processing ◽

Prediction Model ◽

Grey System Theory ◽

Grey Prediction ◽

Grey System ◽

Middle Point ◽

Filtering Algorithm ◽

Grey Correlation ◽

Grey Prediction Model ◽

Model Based

Image filtering can change or enhance an image by emphasizing or removing certain features of the image. An image is a system in which some information is known and some information is unknown. Grey system theory is an important method for dealing with this kind of system, and grey correlation analysis and grey prediction modeling are important components of this method. In this paper, a fractional grey prediction model based on a filtering algorithm by combining a grey correlation model and a fractional prediction model is proposed. In this model, first, noise points are identified by comparing the grey correlation and the threshold value of each pixel in the filter window, and then, through the resolution coefficient of the important factor in image processing, a variety of grey correlation methods are compared. Second, the image noise points are used as the original sequence by the filter pane. The grey level of the middle point is predicted by the values of the surrounding pixel points combined with the fractional prediction model, replacing the original noise value to effectively eliminate the noise. Finally, an empirical analysis shows that the PSNR and MSE of the new model are approximately 27 and 140, respectively; these values are better than those of the comparison models and achieve good processing effects.

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Prediction of Field Aging Gradient in Asphalt Pavements

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2507-03 ◽

2015 ◽

Vol 2507 (1) ◽

pp. 19-28 ◽

Cited By ~ 29

Author(s):

Xue Luo ◽

Fan Gu ◽

Robert L. Lytton

Keyword(s):

Activation Energy ◽

Prediction Model ◽

Prediction Models ◽

Fast Rate ◽

Chemical Properties ◽

Asphalt Pavements ◽

Model Parameters ◽

Void Content ◽

Constant Rate ◽

Rate Period

The aging of asphalt pavements is a key factor that influences pavement performance. Aging can be characterized by laboratory tests and prediction models. Common aging prediction models use the change of physical or chemical properties of asphalt binders based on regression techniques or aging reaction kinetics. The objective of this study was to develop a kinetics-based aging prediction model for the mixture modulus gradient in asphalt pavements to study long-term in-service aging. The proposed model was composed of three submodels for baseline modulus, surface modulus, and aging exponent to define the change of the mixture modulus with pavement depth. The model used kinetic parameters (aging activation energy and preexponential factor) of asphalt mixtures and combined the two reaction rate periods (fast-rate and constant-rate). Laboratory-measured modulus gradients of 29 field cores at different ages were used to determine the model parameters. The laboratory testing condition was converted to the field condition at a given age and corresponding temperature by introducing the rheological activation energy to quantify the temperature dependence of field cores at each age. The end of the fast-rate period or the beginning of the constant-rate period was accurately identified to model these two periods and to determine the associated parameters separately. The results showed that the predictions matched well with the measurements and the calculated model parameters were verified. The proposed aging prediction model took into account the major factors that affect field aging speed of an asphalt pavement, such as the binder type, aggregate type, air void content, pavement depth, aging temperature, and aging time.

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An Experimental Investigation on the Repairing Performance and Fatigue Life of Asphalt Pavement Potholes With an Inclined Interface Joint

Frontiers in Materials ◽

10.3389/fmats.2020.597523 ◽

2021 ◽

Vol 7 ◽

Author(s):

Linyu Li ◽

Yangquan Huang ◽

Zhutao Shao ◽

Dongya Ren

Keyword(s):

Fatigue Life ◽

Prediction Model ◽

Structural Damage ◽

Experimental Testing ◽

Asphalt Pavement ◽

Fatigue Behavior ◽

Asphalt Pavements ◽

Joint Interface ◽

Tack Coat ◽

Joint Shape

A pothole is a typical structural damage of asphalt pavements that significantly influence the life of asphalt pavements and driving safety. The durability of the existing pit repair methods is generally low. The existing studies in the context of pothole repair mainly focus on the selection and the amount of tack coat materials, nonetheless, very limited studies emphasize the effect of the joint interface shape. This study aims to investigate the influence of the interface joint shape on the service life of pothole repair by experimental testing. The strength and fatigue behavior of the joints were studied and the effectiveness of pothole repairs was evaluated under various conditions, including four temperature levels (5, 10, 15 and 25°C), four strain levels (750 με, 1,000 με, 1,250 με, and 1,500 με) and three loading frequencies (2, 5, and 10 Hz). The optimal interface joint shape was obtained by orthogonal tests. The results indicated that the bond strength and fatigue life of the high viscoelastic emulsified asphalt with an area density of 0.6 kg/m2 in the form of a 30° inclination joint was 473 and 80 times higher than those of traditional pothole repair (i.e., vertical joint form and no tack coat), respectively. Finally, a prediction model was proposed for the interface joint fatigue life considering external parameters through multiple regression analyses. This prediction model can provide a reference for the further study of asphalt pavement pothole repair.

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Main Driving Factors and Prediction Model of Total Health Expenditure in China: A Study Based on Grey System Theory

10.21203/rs.2.16071/v1 ◽

2019 ◽

Author(s):

Huanhuan Jia ◽

Xihe Yu ◽

Jianxing Yu ◽

Zhou Zheng ◽

Yingying Li ◽

...

Keyword(s):

System Theory ◽

Prediction Model ◽

Health Expenditure ◽

The Elderly ◽

Grey System Theory ◽

Driving Factors ◽

System Reform ◽

Total Health Expenditure ◽

Grey System ◽

Correlation Degree

Abstract Background: The continuous increase in total health expenditure has become a social issue of common concern in most countries. In China, the total health expenditure still maintains a fast growth trend which is much higher than the growth of the country’s economy, although the new health system reform had been going on for 8 years until 2017. The aims of the current study were thus to investigate the main driving factors affecting total health expenditure and to establish a prediction model. Methods: Gray system theory was employed to explore the correlation degree between total health expenditure and 13 hot spots from the fields of economy, population, health service utilization, and public policy using national data in China from 2009 to 2017. Besides, a prediction model was established using the main driving factors among the 13 hot spots. Results: The main driving factors related to the changes of total health expenditure were public policy (ranked first), health development, economics, and aging, which correlation degrees were more than 0.7. The average error of the GM(1,7) model was 3.17%, the correlation degree, β , between the predicted simulation sequence and the original sequence was 0.78, the variance ratio, C, was 0.138, and the probability of residuals, P, was 1.0000. Therefore, the prediction model of total health expenditure with 6 main driving factors was excellent. Conclusion: The paper finds that since the new health system reform in China, government policies and social invest have contributed greatly to reducing the burden of health expenditure. However, the development of economic and the increase in the elderly population, which are main driving factors, will increase the total health expenditure, so improving the efficiency of investment and providing the precautionary health care and nursing for the elderly are crucial. Besides, the grey system theory had a good application in the field of health economics and policy.

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Grey Prediction on Sheared Edge Quality in Precision Blanking Process for Micro IT Parts

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.266.122 ◽

2011 ◽

Vol 266 ◽

pp. 122-125

Author(s):

Bo Wang ◽

Jing Bo Chen

Keyword(s):

Prediction Model ◽

Product Quality ◽

Grey Prediction ◽

Grey System ◽

Time Increment ◽

Sheared Edge ◽

Edge Quality ◽

Burr Width ◽

Blanking Process

Sheared edge quality of micro IT parts is an important standard to evaluate product quality. In this paper, a prediction model of sheared edge quality based on grey prediction is studied. By mapping the stroke and the burr width to be the time increment and the eigenvalue of grey system, the grey prediction model was established. The dynamic regularity of burr in actual production was attained from the precision blanking experiment and the prediction of burr width was performed. The results show that the model can predict burr width accurately and needs less sampling data. Thus, it is fit for the requirement of manufacturing.

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