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Numerical modelling of rutting performance of asphalt concrete pavement containing phase change material

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Abstract

This paper aims to propose a systematic numerical modelling framework to quantify the effects of phase change material (PCM) on the early-stage rutting performance of asphalt concrete pavement. Materials, structures, environments and traffic loads of the pavement system were characterized, modelled and implemented into a finite element model. Hourly variations in environmental and traffic conditions for the pavement structure were considered. The temperature profile in the pavement was calculated from a finite-difference heat transfer model, while the frequency distributions of traffic loads were obtained from the artificial intelligence-based finite element model updating. Based on the results, the accumulated rut depth in the asphalt concrete after the first week of pavement service was reduced by 4% by adding only 3% PEG/SiO2 PCM in the top sublayer of the asphalt concrete course. This study demonstrated that via advanced methodologies such as artificial intelligence and finite element simulation and model updating, the effectiveness of using PCM to control asphalt concrete pavement rutting performance can be evaluated.

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Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Deng Y, Luo X, Gu F, Zhang Y, Lytton RL (2019) 3D simulation of deflection basin of pavements under high-speed moving loads. Constr Build Mater 226:868–878

    Article  Google Scholar 

  2. Deng Y, Luo X, Zhang Y, Cai S, Huang K, Shi X, Lytton RL (2020) Determination of flexible pavement deterioration conditions using Long-Term Pavement Performance database and artificial intelligence-based finite element model updating. Struct Control Health Monit 28:2671

    Google Scholar 

  3. Cortes D, Shin H, Santamarina JC (2012) Numerical simulation of inverted pavement systems. J Transp Eng 138(12):1507–1519

    Article  Google Scholar 

  4. Kim M, Tutumluer E, Kwon J (2009) Nonlinear pavement foundation modeling for three-dimensional finite-element analysis of flexible pavements. Int J Geomech 9(5):195–208

    Article  Google Scholar 

  5. Deng Y, Luo X, Zhang Y, Lytton RL (2020) Determination of complex modulus gradients of flexible pavements using falling weight deflectometer and artificial intelligence. Mater Struct 53(4):1–17

    Article  Google Scholar 

  6. Deng Y, Zhang Y, Luo X, Lytton R (2021) Development of equivalent stationary dynamic loads for moving vehicular loads using artificial intelligence-based finite element updating. Eng Comput. https://doi.org/10.1007/s00366-021-01306-w

  7. Zhang Y, Birgisson B, Lytton RL (2016) Weak form equation–based finite-element modeling of viscoelastic asphalt mixtures. J Mater Civ Eng 28(2):04015115

    Article  Google Scholar 

  8. Luo X, Gu F, Ling M, Lytton RL (2018) Review of mechanistic-empirical modeling of top-down cracking in asphalt pavements. Constr Build Mater 191:1053–1070

    Article  Google Scholar 

  9. Deng Y, Zhang Y, Shi X, Hou S, Lytton R (2021) Stress-strain dependent rutting prediction models for multi-layer structures of asphalt mixtures. Int J Pavement Eng. https://doi.org/10.1080/10298436.2020.1869974

  10. Anupam B, Sahoo UC, Rath P (2020) Phase change materials for pavement applications: a review. Constr Build Mater 247:118553

    Article  Google Scholar 

  11. Chen J, Zhang W, Shi X, Yao C, Kuai C (2020) Use of PEG/SiO2 phase change composite to control porous asphalt concrete temperature. Constr Build Mater 245:118459

    Article  Google Scholar 

  12. Kuai C, Chen J, Shi X, Grasley Z (2021) Regulating porous asphalt concrete temperature using PEG/SiO2 phase change composite: Experiment and simulation. Constr Build Mater 273:122043

    Article  Google Scholar 

  13. Newcomb D (2002) Perpetual pavements-a synthesis

  14. Apeagyei AK, Diefenderfer SD (2011) Asphalt material design inputs for use with the mechanistic-empirical pavement design guide in Virginia. Virginia Center for Transportation Innovation and Research, Charlottesville

  15. Leiva-Villacorta F, Willis R (2017) High-modulus asphalt concrete (HMAC) mixtures for use as base course. National Center for Asphalt Technology at Auburn University Auburn, Alabama

    Google Scholar 

  16. Huang YH (2003) Pavement analysis and design, 2nd edn. Pearson Education, Inc., Upper Saddle River, NJ

  17. Kutay ME, Chatti K, Lei L (2011) Backcalculation of dynamic modulus mastercurve from falling weight deflectometer surface deflections. Transp Res Rec 2227(1):87–96

    Article  Google Scholar 

  18. Ma X, Dong Z, Chen F, Xiang H, Cao C, Sun J (2019) Airport asphalt pavement health monitoring system for mechanical model updating and distress evaluation under realistic random aircraft loads. Constr Build Mater 226:227–237

    Article  Google Scholar 

  19. Deng Y, Luo X, Zhang Y, Lytton RL (2020) Evaluation of flexible pavement deterioration conditions using deflection profiles under moving loads. Transp Geotechn 26:100434

    Article  Google Scholar 

  20. Ling M, Deng Y, Zhang Y, Luo X, Lytton RL (2020) Evaluation of complex Poisson’s ratio of aged asphalt field cores using direct tension test and finite element simulation. Constr Build Mater 261:120329

    Article  Google Scholar 

  21. Luo X, Li H, Deng Y, Zhang Y (2020) Energy-based kinetics approach for coupled viscoplasticity and viscofracture of asphalt mixtures. J Eng Mech 146(9):04020100

    Google Scholar 

  22. Abaqus G (2011) Abaqus 6.11, Dassault Systemes Simulia Corp Providence, RI, USA

  23. Boriack PC (2014) A laboratory study on the effect of high RAP and high asphalt binder content on the performance of asphalt concrete. Virginia Tech

  24. Shi X, Rew Y, Ivers E, Shon C-S, Stenger EM, Park P (2019) Effects of thermally modified asphalt concrete on pavement temperature. Int J Pavement Eng 20(6):669–681

    Article  Google Scholar 

  25. Mrawira DM, Luca J (1809) Thermal properties and transient temperature response of full-depth asphalt pavements. Transp Res Rec J Transp Res Board 2002:160–171

    Google Scholar 

  26. Gui J, Phelan PE, Kaloush KE, Golden JS (2007) Impact of pavement thermophysical properties on surface temperatures. J Mater Civ Eng 19(8):683–690

    Article  Google Scholar 

  27. Hermansson A (2001) Mathematical model for calculation of pavement temperatures: comparison of calculated and measured temperatures. Transp Res Rec J Transp Res Board 1764:180–188

    Article  Google Scholar 

  28. Yoshitake I, Nagai S, Tanimoto T, Hamada S (2002) Simple estimation of the ground water temperature and snow melting process. In: Proceedings of the 11th international road weather conference, Sapporo, Japan

  29. Kou Y, Wang S, Luo J, Sun K, Zhang J, Tan Z, Shi Q (2019) Thermal analysis and heat capacity study of polyethylene glycol (PEG) phase change materials for thermal energy storage applications. J Chem Thermodyn 128:259–274

    Article  Google Scholar 

  30. T.E. ToolBox (2003) Specific heat of solids. https://www.engineeringtoolbox.com/specific-heat-solids-d_154.html

  31. Hartlieb P, Toifl M, Kuchar F, Meisels R, Antretter T (2016) Thermo-physical properties of selected hard rocks and their relation to microwave-assisted comminution. Min Eng 91:34–41

    Article  Google Scholar 

  32. T.E. ToolBox (2003) Thermal conductivity of selected materials and gases. https://www.engineeringtoolbox.com/thermal-conductivity-d_429.html.

  33. T.E. ToolBox (2003) Specific heat of some common substances. https://www.engineeringtoolbox.com/specific-heat-capacity-d_391.html

  34. T.E. ToolBox (2009) Air—thermal conductivity. https://www.engineeringtoolbox.com/air-properties-viscosity-conductivity-heat-capacity-d_1509.html

  35. Hilsenrath J, Beckett C, Benedict W, Fano L, Hoge H, Masi J, Nuttall R, Touloukian Y, Woolley H (1955) Tables of thermal properties of gases. NBS Circ. 564, Nat. Bur. Stds., Washington, DC

  36. Thompson M, Dempsey B, Hill H, Vogel J (1987) Characterizing temperature effects for pavement analysis and design. Transp Res Rec (1121).

  37. Arora A, Sant G, Neithalath N (2017) Numerical simulations to quantify the influence of phase change materials (PCMs) on the early-and later-age thermal response of concrete pavements. Cem Concr Compos 81:11–24

    Article  Google Scholar 

  38. Wolfram S (1999) The MATHEMATICA® book, version 4. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  39. Al-Qadi IL, Xie W, Elseifi MA (2008) Frequency determination from vehicular loading time pulse to predict appropriate complex modulus in MEPDG. Asphalt Paving Technol Proc 77:739

    Google Scholar 

  40. Martin JD, Simpson TW (2005) Use of kriging models to approximate deterministic computer models. AIAA J 43(4):853–863

    Article  Google Scholar 

  41. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks, IEEE, 1995, pp 1942–1948

  42. Shabbir F, Omenzetter P (2015) Particle swarm optimization with sequential niche technique for dynamic finite element model updating. Comput Aided Civ Infrastruct Eng 30(5):359–375

    Article  Google Scholar 

  43. AASHTO (2003) Guide for mechanistic-empirical design of new and rehabilitated pavement structures

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Washington State University, Pullman, WA, 99164, USA

    Yong Deng

  2. Ingram School of Engineering, Texas State University, San Marcos, TX, 78666, USA

    Xijun Shi

  3. CCCC Second Highway Consultants Co., Ltd, Wuhan, 430052, Hubei, China

    Yazhou Zhang

  4. College of Civil and Transportation Engineering, Hohai University, Nanjing, 210098, Jiangsu, China

    Jun Chen

Authors
  1. Yong Deng
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  2. Xijun Shi
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  3. Yazhou Zhang
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  4. Jun Chen
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Correspondence to Xijun Shi.

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Deng, Y., Shi, X., Zhang, Y. et al. Numerical modelling of rutting performance of asphalt concrete pavement containing phase change material. Engineering with Computers 39, 1167–1182 (2023). https://doi.org/10.1007/s00366-021-01507-3

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  • DOI: https://doi.org/10.1007/s00366-021-01507-3

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