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Construction and simulation of a decision model for anti-slip restoration technology of asphalt pavement surface based on vehicle dynamics

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Taking real-time and effective preventive maintenance measures on in-service roads can reduce traffic congestion, eliminate potential road safety hazards, and greatly reduce road maintenance costs. Therefore, based on the Persson friction theory, this study first determines the evaluation indicators for the anti slip ability of asphalt pavement. And based on vehicle dynamics and contact friction between tires and road surface, the anti-skid thresholds of road surfaces for different road conditions and vehicle models are solved through simulation. This study utilizes Python and neural network algorithms to establish a decision-making model for anti-slip recovery technology of asphalt pavement. The experiment shows that the model trained by Back Propagation neural network has high accuracy. The training accuracy of the model is stable at around 0.90, and the training loss value is around 0.34, which can be used for decision-making in anti slip recovery technology. When the speed is less than 60 km/h, the increase in the threshold of dynamic friction coefficient is significant. The maximum difference in growth rate is 47.9%. When the speed exceeds 60 km/h, the increase in the threshold of dynamic friction coefficient gradually slows down. Therefore, at lower speeds, it is more essential to consider the variable value of the dynamic friction coefficient. When the speed is high, more consideration needs to be given to its reference value. This study provides a scientific basis for ensuring that the anti slip ability of the road surface always meets the requirements of driving safety, and has important engineering practical value, economic and social benefits.
Rocznik
Strony
57--69
Opis fizyczny
Bibliogr. 20 poz., il., tab.
Twórcy
autor
  • Heilongjiang Transportation Planning and Design Research Institute Group Co., Ltd., Harbin, China
autor
  • School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, China
autor
  • Heilongjiang Jiaotou Highway Construction Investment Co., Ltd., Harbin, China
Bibliografia
  • [1] S. Purohit, M. Panda, and D.A. Kumar, “Performance of waste polyethylene modified bituminous paving mixes containing reclaimed asphalt pavement and recycled concrete aggregate”, Construction and Building Materials, vol. 348, pp. 1-16, 2022, doi: 10.1016/j.conbuildmat.2022.128677.
  • [2] Z.X. Li, X.L. Shi, J.D. Cao, X.D.Wang, and W. Huang, “CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction”, Science China Technological Sciences, vol. 65, pp. 1470-1481, 2022, doi: 10.1007/s11431-021-1972-7.
  • [3] G. Mazurek, P. Buczyński, and M. Iwański, “Stiffness modulus prediction against basic physical and mechanical characteristics of recycled base course with foamed bitumen and emulsified bitumen”, Archives of Civil Engineering, vol. 69, no. 3, pp. 95-112, 2023, doi: 10.24425/ace.2023.146069.
  • [4] K. Yang and R. Li, “Characterization of bonding property in asphalt pavement interlayer: A review”, Journal of Traffic and Transportation Engineering (English Edition), vol. 8, no. 3, pp. 374-387, 2021, doi: 10.1016/j.jtte.2020.10.005.
  • [5] Z. Chen, “Research on internet security situation awareness prediction technology based on improved RBF neural network algorithm”, Journal of Computational and Cognitive Engineering, vol. 1, no. 3, pp. 103-108, 2022, doi: 10.47852/bonviewJCCE149145205514.
  • [6] X.Wang, L. Gu, M.M. Dong, and X.L. Li, “State estimation of tire-road friction and suspension system coupling dynamic in braking process and change detection of road adhesive ability”, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 236, no. 6, pp. 1170-1187, 2022, doi: 10.1177/09544070211035298.
  • [7] J. Hu, S. Rakheja, and Y. Zhang, “Tire-road friction coefficient estimation based on designed braking pressure pulse”, Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering, vol. 235, no. 7, pp. 1876-1891, 2021, doi: 10.1177/0954407020983580.
  • [8] M. Furlan and G. Mavros, “A neural network approach for roughness-dependent update of tyre friction”, Simulation Modelling Practice and Theory, vol. 116, art. no. 102484, 2022, doi: 10.1016/j.simpat.2021.102484.
  • [9] H. Pérez-Acebo, H. Gonzalo-Orden, D.J. Findley, and E. Rojí, “A skid resistance prediction model for an entire road network”, Construction and Building Materials, vol. 262, art. no. 120041, 2020, doi: 10.1016/j.conbuildmat.2020.120041.
  • [10] M.G. Correia, T. de Oliveira e Bonates, B. de Athayde Prata, and E.F. Nobre Júnior, “An integer linear programming approach for pavement maintenance and rehabilitation optimization”, International Journal of Pavement Engineering, vol. 23, no. 8, pp. 2710-2727, 2022, doi: 10.1080/10298436.2020.1869736.
  • [11] M. Mahmood, U. Anuraj, S. Mathavan, and M. Rahman, “A unified artificial neural network model for asphalt pavement condition prediction”, Proceedings of the Institution of Civil Engineers. Transport, vol. 176, no. 1, pp. 14-24, 2023, doi: 10.1680/jtran.19.00111.
  • [12] H. Jing, P. Yao, L. Song, J. Zhang, Y. Zhao, and Z. Zhang, “Study of highway asphalt pavement recycling maintenance scheme decision system and decision method”, Journal of Intelligent and Fuzzy Systems, vol. 43, no. 6, pp. 1-10, 2021.
  • [13] X. Wei, J. Chen, H. Gong, and Y. Sun, “Mesoscopic asphalt pavement response analysis using a random aggregate generation-based concurrent multiscale method”, Construction and Building Materials, vol. 321, art. no. 126404, 2022, doi: 10.1016/j.conbuildmat.2022.126404.
  • [14] A. Emami, S. Khaleghian, and S. Taheri, “Asperity-based modification on theory of contact mechanics and rubber friction for self-affine fractal surfaces”, Friction, vol. 9, pp. 1707-1725, 2021, doi: 10.1007/s40544-021-0485-5.
  • [15] S.H. Dong, S. Han, Q.X. Zhang, X. Han, Z. Zhang, and T.F. Yao, “Three-dimensional evaluation method for asphalt pavement texture characteristics”, Construction and Building Materials, vol. 287, art. no. 122966, 2021, doi: 10.1016/j.conbuildmat.2021.122966.
  • [16] R. de Souze Sales, F.H.L. de Oliveira, and L. de Albuquerque Prado, “Performance of tire-asphalt pavement adherence according to rubber removal on runways”, International Journal of Pavement Engineering, vol. 23, no. 10, pp. 3566-3576, 2022, doi: 10.1080/10298436.2021.1907577.
  • [17] A.J. Golrokh, X. Gu, and Y. Lu, “Real-time thermal imaging-based system for asphalt pavement surface distress inspection and 3D crack profiling”, Journal of Performance of Constructed Facilities, vol. 35, no. 1, art. no. 04020143, 2021, doi: 10.1061/(ASCE)CF.1943-5509.0001557.
  • [18] C. Guo, X. Wang, L. Su, and Y. Wang, “Safety distance model for longitudinal collision avoidance of logistics vehicles considering slope and road adhesion coefficient”, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 235, no. 2-3, pp. 498-512, 2021, doi: 10.1177/0954407020959744.
  • [19] X. Deng, Y. Zhang, and Y. Tang, “Investigation on slope rainfall threshold surface based on failure probablolity”, The Chinese Journal of Geological Hazard And Control, vol. 32, no. 3, pp. 70-75, 2021, doi: 10.16031/j.cnki.issn.1003-8035.2021.03-09.
  • [20] L.J. Jobst, C. Heine, M. Auerswald, and M. Moshagen, “Effects of multivariate non-normality and missing data on the root mean square error of approximation”, Structural Equation Modeling: A Multidisciplinary Journal, vol. 28, no. 6, pp. 851-858, 2021, doi: 10.1080/10705511.2021.1933987.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d2cdf227-0aa8-4f3a-a6ed-f7524d5bdfde
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