Asphalt Pavement Roughness Prediction Based on Gray GM(1,1|sin) Model

Zhang, Xiuli; Ji, Chuan

doi:10.2991/ijcis.d.190808.002

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…The soft computing model shows better prediction accuracy than traditional multivariate regression methods. Zhang and Ji [30] also use ANN-GA to back-calculate the flexible pavement layer modulus from the FWD test with certain advantages, such as elimination of seed modulus and consideration of complex material properties. More importantly, the back-calculated pavement layer parameters can be directly used in the mechanical-empirical design of pavement overlays.…”

Section: Related Workmentioning

confidence: 99%

CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

Shi

Cao

et al. 2022

Sci. China Technol. Sci.

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The use of non-destructive testing (NDT) equipment, such as the falling weight deflectometer (FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale field accelerated pavement testing track named RIOHTrack (Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm (CPSO) and a segmented regression strategy are proposed in this paper to optimize the XGBoost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and 1.59. The experiments demonstrate the superiority of the XGBoost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Significantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.

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Section: Related Workmentioning

confidence: 99%

CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

Shi

Cao

et al. 2022

Sci. China Technol. Sci.

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show abstract

“…The gray prediction model has been widely used in various prediction applications [7]. Some examples include asphalt pavement roughness prediction [8], oil price forecasting [9], heat supply prediction [10], remaining service life prediction of lithium‐ion batteries [11], reliability prediction [12], PID controller parameter tuning [13], shale gas prediction [14], short‐term prediction of COVID‐19 spread [15], air quality prediction [16], and sales forecasting of new energy vehicles [17]. These applications have turned out to be very successful, which proves the effectiveness of the gray model.…”

Section: Introductionmentioning

confidence: 99%

Combining Fourier Fractional GM(1,1|sin) Model with Rat Swarm Optimizer for Employment Rate Prediction

Cai,

Lei,

et al. 2024

IEEJ Transactions Elec Engng

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Accurate prediction of employment rate of graduated students can greatly help education authorities to make informed decisions as well as for universities to adjust their teaching plans. Unfortunately, prediction of the employment rate of graduated students is still a difficult problem because the historical employment rate data exhibits fluctuations. In this paper, a new method is proposed for employment rate prediction using fractional gray GM(1,1sin) model, which aims to alleviate the effect of data fluctuation on prediction accuracy and increase the contribution of new data in the prediction procedure. To further decrease prediction error, a Fourier series is adopted to model the residual series. The proposed model, called Fourier Fractional GM(1,1sin) Model (FFGMsin), is used to predict the employment rate of graduated students of Yanshan University from 2010 to 2019. Results show that the proposed method can obtain more accurate prediction results than GM(1,1) model and its variants. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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“…Du [6], Wang and Li [7], and Zhang and Ji [8] used this model to predict pavement smoothness and rutting. Peng et al [9] applied Weibull distribution to pavement performance prediction and obtained ideal results.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Zhao

Fang

et al. 2020

Journal of Advanced Transportation

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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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Asphalt Pavement Roughness Prediction Based on Gray GM(1,1|sin) Model

Cited by 9 publications

References 10 publications

CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

Combining Fourier Fractional GM(1,1|sin) Model with Rat Swarm Optimizer for Employment Rate Prediction

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

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