Abstract: Owing to the variation of vehicle load and environmental conditions, there exist huge difference between the actual working state and the static state in the material properties of asphalt pavement. Hence, it is necessary to investigate the dynamic characteristics of the asphalt pavement under dynamic load. The dynamic modulus of asphalt mixture is one of the important input parameters in asphalt pavement design. Usually, the master curve of asphalt mixture, fitted by the dynamic modulus of different temperatures and frequencies, can be used to accurately predict the dynamic modulus of asphalt mixture under different temperature, frequency and extreme conditions. Nevertheless, the complexity and high cost of indoor tests have hindered the application of modulus test of asphalt mixture. Therefore, exploring a more convenient way to obtain or predicate the dynamic modulus of asphalt mixture has become a research hot spot in recent years. There has been numerous studies on dynamic modulus prediction model. Several classical models like Witczak 1-37A, NCHRP 1-40D and Hirsch have been proposed. Although the three models are fitted on the basis of a large number of experimental data, the applicability of them are different because of the differences of materials, test methods and environment conditions in various regions of the world. Besides, with the development of computer technology, researchers endeavor to build simulation models in microscopic view to predicate the dynamic modulus of asphalt mixture in recent years. Specially, discrete element method (DEM) shows great advantages and has been widely used to simulate the asphalt mixture materials. In terms of applicability of the typical prediction models, a large number of studies on dynamic modulus prediction of asphalt mixture have been carried out by foreign scholars. The applicability of the three typical prediction models are systematically analyzed, and specific revision models have been put forward. However, the researches of the prediction model only restrict to few provinces and regions in China. It lacks nationwide comprehensive research on the dynamic modulus of asphalt mixture, which leads to the insufficient data base for revising the prediction model. All in all, there is an urgent need for further investigating the dynamic modulus prediction of asphalt mixture in different regions of China. For the microscopic simulation model of asphalt mixture, the DEM with CT technology can create a virtual specimen model, in which the aggregate shape, distribution and void features are in consistent with the actual situation, and the simulation test can be carried out accurately. This article summarizes the status quo of dynamic modulus prediction model of asphalt mixture. The three classical dynamic modulus prediction model and their applicability in different regions are introduced and analyzed. Besides, the discrete element me-thod (DEM) that used to predict the dynamic modulus of asphalt mixture is elaborated with emphases. At last, the current drawbacks existing in this technology are pointed out and the future research trends are proposed, which expected to provide a reference for the research and application of the dynamic modulus prediction model of asphalt mixture.
1 Wei J C, Cui S P, Hu J B. Research on dynamic modulus of asphalt mixtures[J].Journal of Building Materials,2008,11(6):657(in Chinese). 韦金城,崔世萍,胡家波.沥青混合料动态模量试验研究[J].建筑材料学报,2008,11(6):657. 2 Sun Jian. The research of asphalt mixture dynamic modulus[D].Xi’an: Chang’an University,2007(in Chinese). 孙建.沥青混合料动态模量研究[D].西安:长安大学,2007. 3 Yang Ming. Research on the dynamic modulus of asphalt mixture[D].Changsha: Changsha University of Science and Technology,2007(in Chinese). 羊明.沥青混合料动态模量研究[D].长沙:长沙理工大学,2007. 4 Fan X Y, Luo R, Feng G L, et al. Validation and analysis of dyna-mic modulus prediction model of asphalt mixture[J].Journal of Wuhan University of Technology(Transportation Science ＆ Enginee-ring),2017,41(2):298(in Chinese). 樊向阳,罗蓉,冯光乐,等.沥青混合料动态模量预测模型的验证与分析研究[J].武汉理工大学学报(交通科学与工程版),2017,41(2):298. 5 Georgouli K, Loizos A, Plati C. Calibration of dynamic modulus predictive model[J].Construction and Building Materials,2016,102:65. 6 Li J, Zofka A, Yut I. Evaluation of dynamic modulus of typical asphalt mixtures in Northeast US region[J].Road Materials and Pavement Design,2012,13(2):249. 7 交通部公路规划设计院.公路沥青路面设计规范[M].北京:人民交通出版社,2017. 8 Luo S, Qian Z D, Harvey J. Research on dynamic modulus for epoxy asphalt mixtures and its master curve[J].China Journal of Highway & Transport,2010,23(6):16(in Chinese). 罗桑,钱振东,Harvey J.环氧沥青混合料动态模量及其主曲线研究[J].中国公路学报,2010,23(6):16. 9 Witczak M, Fonseca O. Revised predictive model for dynamic (complex) modulus of asphalt mixtures[J].Transportation Research Record,1996,1540(1):15. 10 Bari J, Witczak M W. Development of a new revised version of the Witczak E predictive model for hot mix asphalt mixtures[J].Journal of the Association of Asphalt Paving Technologists,2006,75:381. 11 Witczak M, El-Basyouny M, El-Badawy S. Incorporation of the new (2005) E* predictive model in the MEPDG,NCHRP 1-40D final report[R].Tempe, AZ:Arizona State University,2007. 12 Jr D W C, Pellinen T, Bonaquist R F. Hirsch model for estimating the modulus of asphalt concrete[J].Asphalt Paving Technology: Association of Asphalt Paving Technologists—Proceedings of the Technical Sessions,2003,72:97. 13 Liu P. Pavement material properties research based on gene expression programming algorithm[D].Changsha: Hunan University,2015(in Chinese). 刘沛.基于基因表达式算法的路面材料性能预测[D].长沙:湖南大学,2015. 14 Esfandiarpour S, Shalaby A. Local calibration of creep compliance models of asphalt concrete[J].Construction and Building Materials,2017,132:313. 15 Far S. Development of new dynamic modulus (|E*|) predictive models for hot mix asphalt mixtures[D].Raleigh, North Carolina: North Carolina State University,2011. 16 Singh D, Zaman M, Commuri S. Artificial neural network modeling for dynamic modulus of hot mix asphalt using aggregate shape pro-perties[J].Journal of Materials in Civil Engineering,2013,25(1):54. 17 Ceylan H, Gopalakrishnan K, Kim S. Advanced approaches to hot-mix asphalt dynamic modulus prediction[J].Canadian Journal of Civil Engineering,2008,35(7):699. 18 You Z, Buttlar W. Micromechanical modeling approach to predict compressive dynamic moduli of asphalt mixtures using the distinct element method[J].Transportation Research Record Journal of the Transportation Research Board,2006,1970(1):73. 19 Zhanping You, Shu Wei Goh, Qingli Dai, et al. Dynamic moduli for M-E design of asphalt pavements[C]∥Plan, Build, and Manage Transportation Infrastructure in China. Reston, VA,2008. 20 Azari H, Al-Khateeb G, Shenoy A, et al. Comparison of simple performance test |E*| of accelerated loading facility mixtures and prediction |E*|: Use of NCHRP 1-37A and Witczak’s new equations[J].Transportation Research Record,2007,1:689. 21 Khattab A M, El-Badawy S M, Elmwafi M, et al. Comparison of Witczak NCHRP 1-40D & Hirsh dynamic modulus models based on different binder characterization methods: A case study[C]∥International Conference on Advances in Sustainable Construction Mate-rials & Civil Engineering Systems. London, United Kingdom,2017:07003. 22 Christensen D W, Bonaquist R. Improved Hirsch model for estimating the modulus of hot-mix asphalt[J].Road Materials and Pavement Design,2015,16(s2):254. 23 唐启义.DPS数据处理系统[M].北京:科学出版社,2013. 24 Tian Y, Zhang C, Mao X R, et al. Research on abnormal behavior of power consumption based on BP neural network with PCA[J].Journal of Chongqing University of Technology(Natural Science),2017,31(8):125(in Chinese). 田野,张程,毛昕儒,等.运用PCA改进BP神经网络的用电异常行为检测[J].重庆理工大学学报(自然科学版),2017,31(8):125. 25 Ceylan H, Kim S, Gopalakrishnan K. Hot mix asphalt dynamic modulus prediction models using neural networks approach[M].USA:ASME Press,2007. 26 Ceylan H,Schwartz C W,Kim S, et al. Accuracy of predictive mo-dels for dynamic modulus of hot-mix asphalt[J].Journal of Materials in Civil Engineering,2009,21(6):286. 27 You Z, Goh S W, Dong J. Predictive models for dynamic modulus using weighted least square nonlinear multiple regression model[J].Canadian Journal of Civil Engineering,2012,39(5):589. 28 El-Badawy S M, Bayomy F M. Evaluation of the MEPDG dynamic modulus prediction models for asphalt concrete mixtures[J].T&DI Congress,2011,2011:576. 29 Singh D, Zaman M, Commuri S. Evaluation of predictive models for estimating dynamic modulus of hot-mix asphalt in Oklahoma[J].Transportation Research Record: Journal of the Transportation Research Board,2011,2210:57. 30 El-Badawy S, Bayomy F, Awed A. Performance of MEPDG dynamic modulus predictive models for asphalt concrete mixtures: Local Calibration for Idaho[J].Journal of Materials in Civil Engineering,2012,24(11):1412. 31 Biligiri K P, Way G B. Predicted E* dynamic moduli of the Arizona mixes using asphalt binders placed over a 25-year period[J].Construction and Building Materials,2014,54:520. 32 Sakhaeifar M S, Richard Kim Y, Kabir P. New predictive models for the dynamic modulus of hot mix asphalt[J].Construction and Buil-ding Materials,2015,76:221. 33 Shen S, Yu H, Willoughby K, et al. Local practice of assessing dynamic modulus properties for Washington state mixtures[J].Transportation Research Record: Journal of the Transportation Research Board,2013,2373:89. 34 Robbins M, Timm D. Evaluation of dynamic modulus predictive equations for Southeastern United States asphalt mixtures[J].Transportation Research Record: Journal of the Transportation Research Board,2011,2210:122. 35 Cho Y H, Park D W, Hwang S D. A predictive equation for dynamic modulus of asphalt mixtures used in Korea[J].Construction and Building Materials,2010,24(4):513. 36 Yousefdoost S, Vuong B, Rickards I, et al. Evaluation of dynamic modulus predictive models for typical Australian asphalt mixes[J].AAPA International Flexible Pavements Conference. Brisbane, Queensland, Australia,2013. 37 Khattab A M, El-Badawy S M, Al Hazmi A A, et al. Evaluation of Witczak E* predictive models for the implementation of AASHTOWare-Pavement ME design in the Kingdom of Saudi Arabia[J].Construction and Building Materials,2014,64:360. 38 Tan Y Q, Fu X G, Ma S J, et al. Experimental study on dynamic modulus of asphalt mixture based on free-free resonant test[J].China Civil Engineering Journal,2015(12):116(in Chinese). 谭忆秋,傅锡光,马韶军,等.基于无约束共振法沥青混合料动态模量试验研究[J].土木工程学报,2015(12):116. 39 华南理工大学道路研究所.沥青混合料动态模量参数研究[R].广州:华南理工大学,2007. 40 Zhao Y Q, Pan Y Q, Huang D X. Verification of Hirsch model in predicting dynamic moduli of asphalt mixtures[J].Highway,2007(11):196(in Chinese). 赵延庆,潘有强,黄大喜.沥青混合料动态模量Hirsch预测模型的验证研究[J].公路,2007(11):196. 41 Ma X, Ni F J, Chen R S. Dynamic modulus test of asphalt mixture and prediction model[J].China Journal of Highway & Transport,2008,21(3):35(in Chinese). 马翔,倪富健,陈荣生.沥青混合料动态模量试验及模型预估[J].中国公路学报,2008,21(3):35. 42 Yan Z L, Hu X G, Xiao Z R. Dynamic modulus test of asphalt mixture and prediction model[J].Highway,2008(1):175(in Chinese). 闫振林,胡霞光,肖昭然.沥青混合料动态模量预估模型研究[J].公路,2008(1):175. 43 Ma S J, Fu J C, Wei J C, et al. Study on dynamic modulus prediction model of large stone porous asphalt mixture[J].Journal of Highway and Transportation Research and Development,2010(5):36(in Chinese). 马士杰,付建村,韦金城,等.大粒径透水性沥青混合料动态模量预估模型研究[J].公路交通科技,2010(5):36. 44 Fu Xiguang. Experimental research on dynamic modulus of asphalt mixture based on unconstrained resonant test[D].Harbin: Harbin Institute of Technology,2015(in Chinese). 傅锡光.基于无约束共振法沥青混合料动态模量试验研究[D].哈尔滨:哈尔滨工业大学,2015. 45 Jing R X, Wang D S, Feng D C. Research on master curve of common asphalt mixtures in Liaoning province[J].Journal of Liaoning Technical University (Natural Science Edition),2013(9):1246(in Chinese). 荆儒鑫,王东升,冯德成.辽宁省典型沥青混合料动态模量[J].辽宁工程技术大学学报(自然科学版),2013(9):1246. 46 Haopeng W, Jun Y, Wenzhang Z, et al. Assessing dynamic modulus properties for typical asphalt mixtures in Jiangsu[J].Journal of Southeast University(English Edition),2016,32(1):99. 47 You Yuanjian. The research on the dynamic modulus of asphalt mixture[D].Jinan: Shandong Jianzhu University,2017(in Chinese). 尤远见.沥青混合料动态模量的研究[D].济南:山东建筑大学,2017. 48 Guo N S, Zhao Y H. Dynamic modulus prediction of asphalt mixtures based on micromechanics[J].Engineering Mechanics,2012(10):13(in Chinese). 郭乃胜,赵颖华.基于细观力学的沥青混合料动态模量预测[J].工程力学,2012(10):13. 49 Fan Zepeng. The micromechanical model of asphalt mixture consi-dering inter-particle interaction[D].Xi’an: Chang’an University,2016(in Chinese). 樊泽鹏.考虑颗粒相互作用的沥青混合料细观力学模型[D].西安:长安大学,2016. 50 Huang X Y. Study on forecasting methods for dynamic modulus of asphalt mixtuce[J].Technology of Highway & Transport,2015(4):32(in Chinese). 黄晓勇.沥青混合料动态模量预测方法研究[J].公路交通技术,2015(4):32. 51 Zhao Y, Bai L, Liu H. Implementation of a triaxial dynamic modulus master curve in finite-element modeling of asphalt pavements[J].Journal of Materials in Civil Engineering,2014,26(3):491. 52 Ying H, Elseifi M A, Mohammad L N, et al. Heterogeneous finite-element modeling of the dynamic complex modulus test of asphalt mixture using X-ray computed tomography[J].Journal of Materials in Civil Engineering,2014,26(9):04014052. 53 Aragão F, Kim Y, Karki P, et al. Semiempirical, analytical, and computational predictions of dynamic modulus of asphalt concrete mixtures[J].Transportation Research Record: Journal of the Transportation Research Board,2010,2181:19. 54 Wan C, Zhang X, Wang L, et al. Three-dimensional micromechanical finite element analysis on gauge length dependency of the dyna-mic modulus of asphalt mixtures[J].Road Materials and Pavement Design,2012,13(4):769. 55 Fakhari Tehrani F, Quignon J, Allou F, et al. Two-dimensional/three-dimensional biphasic modelling of the dynamic modulus of bituminous materials[J].European Journal of Environmental and Civil Engineering,2013,17(6):430. 56 Tian Li. The virtual test of asphalt mixture stiffness moduli based on DEM[D].Xi’an: Chang’an University,2008(in Chinese). 田莉.基于离散元方法的沥青混合料劲度模量虚拟试验研究[D].西安:长安大学,2008. 57 Shi F Z, Chang M F, Pei J Z, et al. Mesoscopic analysis on vector field of asphalt mixture using discrete element method[J].Journal of Chang’an University (Natural Science Edition),2014,34(2):9(in Chinese). 石福周,常明丰,裴建中,等.基于离散元方法的沥青混合料矢量场细观分析[J].长安大学学报(自然科学版),2014,34(2):9. 58 田莉,闫振林,胡霞光.离散单元法在沥青混合料动态模量研究中的应用[C]∥中国力学学会学术大会.郑州,2009:1. 59 Adhikari S, You Z. 3D discrete element models of the hollow cylindrical asphalt concrete specimens subject to the internal pressure[J].International Journal of Pavement Engineering,2010,11(5):429. 60 Liu Y, You Z. Accelerated discrete-element modeling of asphalt-based materials with the frequency-temperature superposition principle[J].Journal of Engineering Mechanics,2011,137(5):355. 61 Sanjeev Adhikari Z Y, Emin Kutay M. Prediction of dynamic modulus of asphalt concrete using two-dimensional and three-dimensional discrete element modeling approach[J].American Society of Civil Engineers,2008,179:1020. 62 Feng H, Pettinari M, Hofko B, et al. Study of the internal mechanical response of an asphalt mixture by 3-D discrete element modeling[J].Construction and Building Materials,2015,77:187. 63 李海滨,青维,盛燕萍.基于CT扫描和离散元的沥青混合料动态模量虚拟仿真[J].中外公路,2016(5):202. 64 陈俊,张东,黄晓明.离散元颗粒流软件(PFC)在道路工程中的应用[M].北京:人民交通出版社,2015. 65 Tian R X, Jiao H G. Application status and analysis of discrete element software PFC in mining engineering[J].Mining & Metallurgy,2011,20(1):7(in Chinese). 田瑞霞,焦红光.离散元软件PFC在矿业工程中的应用现状及分析[J].矿冶,2011,20(1):7. 66 Tian L, Liu Y, Wang B G. 3D DEM model and digital restructure technique for asphalt mixture simulation[J].Journal of Chang’an University(Natural Science Edition),2007(4):23(in Chinese). 田莉,刘玉,王秉纲.沥青混合料三维离散元模型及其重构技术[J].长安大学学报(自然科学版),2007(4):23. 67 Ma T, Zhang D, Zhang Y, et al. Micromechanical response of aggregate skeleton within asphalt mixture based on virtual simulation of wheel tracking test[J].Construction and Building Materials,2016,111:153. 68 You Z, Adhikari S, Dai Q. Air void effect on an idealised asphalt mixture using two-dimensional and three-dimensional discrete element modelling approach[J].International Journal of Pavement Engineering,2010,11(5):381. 69 You Z,Buttlar W G. Discrete element modeling to predict the modulus of asphalt concrete mixtures[J].Journal of Materials in Civil Engineering,2004,16(2):140. 70 Chen J, Huang B, Shu X, et al. DEM Simulation of laboratory compaction of asphalt mixtures using an open source code[J].Journal of Materials in Civil Engineering,2015,27(3):04014130. 71 Wan C, Zhang X N, He L F, et al. Numerical prediction method for dynamic modulus of asphalt mixture[J].China Journal of Highway and Transport,2012,25(4):16(in Chinese). 万成,张肖宁,贺玲凤,等.沥青混合料动态模量数值预测方法[J].中国公路学报,2012,25(4):16. 72 Manahiloh K N, Muhunthan B, Kayhanian M, et al. X-ray computed tomography and nondestructive evaluation of clogging in porous concrete field samples[J].Journal of Materials in Civil Engineering,2012,24(8):1103. 73 Wan Cheng. Research on 3D reconstruction and digital test of asphalt concrete based on X-ray CT and finite element method[D].Guangzhou: South China University of Technology,2010. 万成.基于X-ray CT和有限元方法的沥青混合料三维重构与数值试验研究[D].广州:华南理工大学,2010. 74 Chen Z Q, Hutchinson T C. Image-based framework for concrete surface crack monitoring and quantification[J].Advances in Civil Engineering, DOI: 10.1155/2010/215295. 75 You Z, Liu Y, Dai Q. Three-dimensional microstructural-based discrete element viscoelastic modeling of creep compliance tests for asphalt mixtures[J].Journal of Materials in Civil Engineering,2011,23(1):79. 76 Kim H, Buttlar W G. Discrete fracture modeling of asphalt concrete[J].International Journal of Solids and Structures,2009,46(13):2593.
渝公网安备50019002502923号 版权所有：《材料导报》编辑部
2018, Vol. 32 
