Skip to main content
Log in

Prediction of highway asphalt pavement performance based on Markov chain and artificial neural network approach

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In order to study the problems of inadequate maintenance measures, inappropriate maintenance time, and unreasonable use of funds in asphalt pavement maintenance of Highway in China, the maintenance of highway pavement is taken as the research object in this study, and a prediction model is established for preventive maintenance performance of highway by using neural network. Firstly, the performance of pavement is evaluated. The pavement performance prediction model is studied, and some mature prediction models are introduced. It is concluded that for the early built highways, the models are used when the acceptance of maintenance and preventive maintenance concepts is poor and the pavement performance shows a decreasing trend, but for the existing maintenance and preventive maintenance sections, the pavement performance detection shows a wave. The dynamic descent section is not suitable. The results show that the forecasting model proposed in this study is consistent with the development trend of the measured results, and can be used to predict the pavement performance under this model. Therefore, a theoretical basis is provided for the investment of highway maintenance funds and the scientific selection of maintenance schemes. The study has important guiding significance for the future highway management units in the selection of maintenance measures, the determination of maintenance timing, and the size of capital investment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Shi X, Hansen G, Mills M et al (2016) Preserving the value of highway maintenance equipment against roadway deicers: a case study and preliminary cost benefit analysis. Anti-Corros Methods Mater 63(1):1–8

    Article  Google Scholar 

  2. Zhao J, Fu X, Zhang Y (2016) Research on risk assessment and safety management of highway maintenance project. Procedia Eng 137:434–441

    Article  Google Scholar 

  3. Wu D, Yuan C, Kumfer W et al (2016) A life-cycle optimization model using semi-Markov process for highway bridge maintenance. Appl Math Model 43:9

    MathSciNet  MATH  Google Scholar 

  4. Liu Y, Wang Y, Dong A (2018) Life-cycle CO2 emissions and influential factors for asphalt highway construction and maintenance activities in China. Int J Sustain Transp 12(6):1–13

    Google Scholar 

  5. France-Mensah J, O’Brien WJ, Khwaja N et al (2017) GIS-based visualization of integrated highway maintenance and construction planning: a case study of Fort Worth, Texas. Vis Eng 5(1):7

    Article  Google Scholar 

  6. Hermawan, Suprapto M, Setyawan A (2017) The use of International Roughness Index and structural number for rehabilitation and maintenance policy of local highway. IOP Conf Ser Mater Sci Eng 176(1):012031

    Article  Google Scholar 

  7. Lu Z, Qiang M (2018) Impacts of pavement deterioration and maintenance cost on Pareto-efficient contracts for highway franchising. Transp Res Part E Logist Transp Rev 113:1–21

    Article  Google Scholar 

  8. Shah R, Mcmann O, Borthwick F (2017) Challenges and prospects of applying asset management principles to highway maintenance: a case study of the UK. Transp Res Part A 97:231–243

    Google Scholar 

  9. Fan X, Gao F, He T et al (2017) Establishment of an evaluation model for asphalt pavement preventive maintenance based on improved EW-AHP. J Highw Transp Res Dev (Engl Edn) 11(3):48–53

    Article  Google Scholar 

  10. Seo J, Hatfield G, Kimn JH (2016) Probabilistic structural integrity evaluation of a highway steel bridge under unknown trucks. J Struct Integr Maint 1(2):65–72

    Google Scholar 

  11. Wheat P (2017) Scale, quality and efficiency in road maintenance: evidence for English local authorities. Transp Policy 59:46–53

    Article  Google Scholar 

  12. Liu ZH, Wang XF, Sheng LI et al (2016) Influences of thermal and drying shrinkages on longitudinal reinforcement in continuously reinforced concrete pavement. China J Highw Transp 29(11):1–9

    Google Scholar 

  13. Abaza KA (2016) Back-calculation of transition probabilities for Markovian-based pavement performance prediction models. Int J Pavement Eng 17(3):253–264

    Article  Google Scholar 

  14. Lee KWW, Wilson K, Hassan SA (2017) Prediction of performance and evaluation of flexible pavement rehabilitation strategies. J Traffic Transp Eng 4(2):178–184

    Google Scholar 

  15. Premkumar L, Vavrik WR (2016) Enhancing pavement performance prediction models for the Illinois Tollway System. Int J Pavement Res Technol 9(1):14–19

    Article  Google Scholar 

  16. Yang YH, Jiang YH, Wang XC (2016) Pavement performance prediction methods and maintenance cost based on the structure load. Procedia Eng 137:41–48

    Article  Google Scholar 

  17. Mazari M, Rodriguez DD (2016) Prediction of pavement roughness using a hybrid gene expression programming-neural network technique. J Traffic Transp Eng 3(5):448–455

    Google Scholar 

  18. Ozer H, Al-Qadi IL, Singhvi P et al (2018) Prediction of pavement fatigue cracking at an accelerated testing section using asphalt mixture performance tests. Int J Pavement Eng 19(3):264–278

    Article  Google Scholar 

  19. Abaza KA (2016) Simplified staged-homogenous Markov model for flexible pavement performance prediction. Road Mater Pavement Des 17(2):17

    Article  Google Scholar 

  20. Ghasemi P, Aslani M, Rollins DK et al (2019) Principal component analysis-based predictive modeling and optimization of permanent deformation in asphalt pavement: elimination of correlated inputs and extrapolation in modeling. Struct Multidiscip Optim 59(4):1335–1353

    Article  Google Scholar 

  21. Ghanizadeh AR, Rahrovan M, Bafghi KB (2018) The effect of cement and reclaimed asphalt pavement on the mechanical properties of stabilized base via full-depth reclamation. Constr Build Mater 161:165–174

    Article  Google Scholar 

  22. Zhao Y, Ying G, Yan S et al (2018) New method for making and selecting the compaction plan of asphalt pavement based on compaction quality and carbon emissions. J Clean Prod 181:385–398

    Article  Google Scholar 

  23. Shirzad S, Aguirre MA, Bonilla L et al (2018) Mechanistic-empirical pavement performance of asphalt mixtures with recycled asphalt shingles. Constr Build Mater 160:687–697

    Article  Google Scholar 

  24. Chen Z, Wang H, Xu Y et al (2018) A combinational prediction model for transverse crack of asphalt pavement. KSCE J Civ Eng 22(6):2109–2117

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhichen Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Guo, N., Wang, S. et al. Prediction of highway asphalt pavement performance based on Markov chain and artificial neural network approach. J Supercomput 77, 1354–1376 (2021). https://doi.org/10.1007/s11227-020-03329-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03329-4

Keywords

Navigation