Feasibility Study for Gray Theory Based Pavement Smoothness Prediction Models

Li, Qiang; Wang, Kelvin C. P.; Elliott, Robert P; Hall, Kevin D.; Qiu, Yanjun

doi:10.1061/40799(213)7

Cited by 4 publications

(5 citation statements)

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“…Compared to other prediction models, the gray prediction models can obtain reasonable prediction accuracy only with limited data samples and can be easily implemented. However, the previous GMs [7][8][9] regard that each data point has the same contribution for predicting future data. This is not the case.…”

Section: Introductionmentioning

confidence: 99%

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Pavement Condition Index Prediction Using Fractional Order GM(1,1) Model

Cai

Lei

2021

IEEJ Transactions Elec Engng

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Non-member Dongge Lei, Non-member Asphalt pavement performance prediction is an important issue for pavement management system. However, it is a difficult problem because asphalt pavement performance are affected by many factors. In this paper, a new method based on fractional gray model is proposed to predict the asphalt pavement performance with a limited data. The proposed method adopts fractional accumulating generating operation (FAGO) to replace traditional accumulating generating operation (AGO), which can be regarded as a weighted AGO emphasizing different contribution of data point for future prediction. An efficient differential evolution algorithm is adopted to select the best order of FAGO. Experimental results show that the proposed method can achieve higher prediction accuracy than conventional gray prediction model.

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Section: Introductionmentioning

confidence: 99%

“…Jiang and Li adopted GM to estimate IRI values at different pavement ages and traffic volume [7]. Li and Wang study the feasibility of GM for smoothness prediction [8]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Pavement Condition Index Prediction Using Fractional Order GM(1,1) Model

Cai

Lei

2021

IEEJ Transactions Elec Engng

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“…In Ref. [9], Li et al studied the feasibility of using gray model to predict pavement smoothness. Jiang and Li used gray model to estimate IRI for the Indiana Department of Transportation (INDOT) [10].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang¹,

Ji²

2019

IJCIS

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Roughness is a comprehensive assessment indicator of pavement performance. Prediction of pavement roughness exhibits great difficulties by using traditional methods such as mechanistic-empirical method and regression method. Considering the fact that the value of international roughness index (IRI) varies in a fluctuant manner, in this paper, a new gray model based method is proposed to predict the roughness of pavement. The proposed method adopts GM(1,1|sin) model as the prediction model. In GM(1,1|sin) model, a sinusoidal term is added into GM(1,1) model, making it can fit fluctuant data more precisely than GM(1,1) model. A particle swarm optimization (PSO) algorithm is used to select the optimal parameter of GM(1,1|sin) model. Experimental results demonstrate its effectiveness of the proposed method. Furthermore, the proposed method only uses the history IRI data in prediction and leads to a large savings of collecting pavement condition data.

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“…Therefore, it is difficult to use regression techniques to analytically determine these relationships. Limitations of using traditional statistical analysis in pavement are discussed by Jiang and Li (2005) and Li et al (2006). In addition, wide variations between actual values and IRI predictions obtained from MEPDG models are observed (Wang and Li, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Jiang and Li (2005) adopts GT‐based approach to estimate the maximum, mean, and minimum IRI values at different pavement ages and traffic volume for the Indiana Department of Transportation (INDOT). Li et al (2006) presents an initial feasibility study on the use of GT methodologies in pavement smoothness prediction and concluded that the gray models derived better predictions. Using the Long Term Pavement Performance (LTPP) data of General Pavement Study (GPS) sections located in various traffic and environmental conditions, a further study was carried out by Wang and Li (2007) to develop pavement smoothness gray prediction models.…”

Section: Introductionmentioning

confidence: 99%

Pavement Smoothness Prediction Based on Fuzzy and Gray Theories

Wang¹,

Li²

2009

Computer-Aided Civil and Infrastructure Engineering

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Pavement smoothness has been recognized as one of the measures of pavement performance. In the Mechanistic-Empirical Pavement Design Guide (MEPDG), pavement smoothness indicated by the International Roughness Index (IRI) was predicted based on various distresses using traditional regression analysis approaches. Recognizing the limitations of linear regression method, a Gray Theory-based technique was previously proposed by the authors for the development of pavement smoothness prediction models. In this article, instead of using the conventional least squares method to determine the coefficients for gray prediction models, fuzzy regression method is proposed to solve this gray problem. With pavement IRI and distresses data exported from the Long Term Pavement Performance (LTPP) database, Fuzzy and Gray Model (FGM)-based smoothness predictions are established using influencing factors similar to those in MEPDG. Based on the comparisons among results originated from MEPDG models, conventional GM models, FGM models, and actual LTPP data, it is shown that the Gray Theory-based prediction methods with fuzzy regression for estimating model coefficients provide promising results and are useful for modeling pavement performance.

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Feasibility Study for Gray Theory Based Pavement Smoothness Prediction Models

Cited by 4 publications

References 0 publications

Pavement Condition Index Prediction Using Fractional Order GM(1,1) Model

Pavement Condition Index Prediction Using Fractional Order GM(1,1) Model

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

Pavement Smoothness Prediction Based on Fuzzy and Gray Theories

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