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.
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References
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
Zhao J, Fu X, Zhang Y (2016) Research on risk assessment and safety management of highway maintenance project. Procedia Eng 137:434–441
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
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
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
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
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
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
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
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
Wheat P (2017) Scale, quality and efficiency in road maintenance: evidence for English local authorities. Transp Policy 59:46–53
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
Abaza KA (2016) Back-calculation of transition probabilities for Markovian-based pavement performance prediction models. Int J Pavement Eng 17(3):253–264
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
Premkumar L, Vavrik WR (2016) Enhancing pavement performance prediction models for the Illinois Tollway System. Int J Pavement Res Technol 9(1):14–19
Yang YH, Jiang YH, Wang XC (2016) Pavement performance prediction methods and maintenance cost based on the structure load. Procedia Eng 137:41–48
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
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
Abaza KA (2016) Simplified staged-homogenous Markov model for flexible pavement performance prediction. Road Mater Pavement Des 17(2):17
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
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
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
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
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
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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
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DOI: https://doi.org/10.1007/s11227-020-03329-4