Performance Decay Model and Preventive Maintenance of Highway Asphalt Pavement

Guanghui Zhao ( Suning County City Management Comprehensive Administrative law enforcement Bureau, Cangzhou city, Hebei Province, 061000, China. )


This work aims to study the law of performance decay of highway asphalt pavement to facilitate preventive highway maintenance. By studying the performance decay law of asphalt pavement, this work proposes to optimize the Back Propagation Neural Network (BPNN) model with the Mind Evolutionary Algorithm, and apply the optimized model to the performance prediction of highway asphalt pavement. Some sections of Xi'an city are selected as sample data to train the neural network. A new type of asphalt material emulsified asphalt is proposed for pavement construction. Finally, the maintenance suggestions of asphalt pavement are given. The results show that the optimized BPNN model has a better fitting effect. The comprehensive evaluation indexes Root Mean Squared Error, Relative Root Mean Squared Error and Mean Absolute Error of the model prediction performance are lower than those of the model before optimization, and the decline rates are 62.46%, 62.46% and 62.71%, respectively. However, the values of Nash-Sutcliffe Efficiency and R2 increase by different degrees, with the increasing rates of 55.92% and 15.42%, respectively. It reveals that the optimized BPNN has more advantages in predicting road pavement performance, with higher accuracy and lower error rate. It provides a new idea for studying the maintenance of highway asphalt pavement. The performance of new asphalt materials is better than that of ordinary asphalt materials.


Asphalt pavement; Road maintenance; Decay law of asphalt performance; Back propagation neural network; Emulsified asphalt

Full Text



1.Wang, Y. D., Keshavarzi, B., & Kim, Y. R. (2018). Fatigue performance prediction of asphalt pavements with FlexPAVETM, the S-VECD model, and DR failure criterion. Transportation Research Record, 2672(40), 217-227.
2.Abed, A., Thom, N., & Neves, L. (2019). Probabilistic prediction of asphalt pavement performance. Road Materials and Pavement Design, 20(sup1), S247-S264.
3.Chen, A., Zhao, Y., Li, P., Li, Y., Mohammed, M., & Guo, P. (2020). Crack propagation prediction of asphalt pavement after maintenance as a function of initial cracks distribution. Construction and Building Materials, 231, 117157.
4.Wang, Z., Guo, N., Wang, S., & Xu, Y. (2021). Prediction of highway asphalt pavement performance based on Markov chain and artificial neural network approach. The Journal of Supercomputing, 77(2), 1354-1376.
5.Vyas, V., Singh, A. P., & Srivastava, A. (2021). Prediction of asphalt pavement condition using FWD deflection basin parameters and artificial neural networks. Road Materials and Pavement Design, 22(12), 2748-2766.
6.Deng, Q., Zhan, Y., Liu, C., Qiu, Y., & Zhang, A. (2021). Multiscale power spectrum analysis of 3D surface texture for prediction of asphalt pavement friction. Construction and Building Materials, 293, 123506.
7.Adwan, I., Milad, A., Memon, Z. A., Widyatmoko, I., Ahmat Zanuri, N., Memon, N. A., & Yusoff, N. I. M. (2021). Asphalt pavement temperature prediction models: A review. Applied Sciences, 11(9), 3794.
8.Ma, J., Sun, G., Sun, D., Yu, F., Hu, M., & Lu, T. (2021). Application of gel permeation chromatography technology in asphalt materials: A review. Construction and Building Materials, 278, 122386.
9.Li, Y., Liu, L., & Sun, L. (2018). Temperature predictions for asphalt pavement with thick asphalt layer. Construction and Building Materials, 160, 802-809.
10.Cho, S., Lee, K., Mahboub, K. C., Jeon, J., & Kim, Y. R. (2019). Evaluation of fatigue cracking performance in a debonded asphalt pavement. International Journal of Pavement Research and Technology, 12(4), 388-395.
11.Wang, Z. X., Li, J. G., & Chen, C.P. (2021). Grey Prediction of Asphalt Pavement Performance Based on Variable Weight Evaluation. Journal of Chongqing Jiaotong University (Natural Science), 40(05), 95.

Copyright © 2022 Guanghui Zhao Creative Commons License Publishing time:2022-07-08
This work is licensed under a Creative Commons Attribution 4.0 International License