Prediction of pavement performance of rural roads based on improved grey models

Authors

• Zhao Jian, School of Transportation , Changsha University of Science & Technology , Changsha , Hunan 410114 , ChinaFollow
• Liu Zheng, School of Transportation , Changsha University of Science & Technology , Changsha , Hunan 410114 , China
• Chen Hongying, School of Transportation , Changsha University of Science & Technology , Changsha , Hunan 410114 , China
• Chen Zhichao, School of Transportation , Changsha University of Science & Technology , Changsha , Hunan 410114 , China
• Zhang Ligeng, School of Transportation , Changsha University of Science & Technology , Changsha , Hunan 410114 , China

Abstract

To improve the prediction accuracy of pavement performance of rural roads, in response to the practical problems of many factors influencing the pavement performance of rural roads, short time-series spans of monitoring data, and low prediction accuracy of traditional grey models, this paper adopted an entropy-weighted grey model to predict the pavement performance of rural roads, and employed the particle swarm algorithm and Markov model to correct the prediction results of the grey model to form an improved grey model. Based on the test data of cement pavement performance of rural roads in Hunan Province, the cement pavements of rural roads were classified into 6 categories A ‒ F considering the type of base course, the thickness of surface course, and the type of drainage facilities. The improved grey model was used to predict the pavement performance of the 6 categories of rural roads. The results show that compared to the traditional grey prediction model, the model improved by the Markov model and particle swarm algorithm significantly reduces the absolute relative error and the root mean square error between the predicted values and the measured values for roads of categories A ‒ F. The average reduction of the absolute relative error is 50.59%, and that of the root mean square error is 0.73, which indicates a significant improvement in prediction accuracy. The improved grey model can accurately predict the evolution laws of pavement performance for rural roads of categories A ‒ F, and provide a reliable theoretical basis and data support for rural road maintenance decision-making and performance prediction.

Publication Date

10-29-2026

DOI

10.14048/j.issn.1671-2579.2026.05.007

First Page

63

Last Page

71

Submission Date

October 2026

Recommended Citation

Jian, Zhao; Zheng, Liu; Hongying, Chen; Zhichao, Chen; and Ligeng, Zhang (2026) "Prediction of pavement performance of rural roads based on improved grey models," Journal of China & Foreign Highway: Vol. 46: Iss. 5, Article 7.
DOI: 10.14048/j.issn.1671-2579.2026.05.007
Available at: https://zwgl1980.csust.edu.cn/journal/vol46/iss5/7

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